research objectives have direct relationship with the

• Defining Research Objectives: How To  Write Them

Almost all industries use research for growth and development. Research objectives are how researchers ensure that their study has direction and makes a significant contribution to growing an industry or niche.

Research objectives provide a clear and concise statement of what the researcher wants to find out. As a researcher, you need to clearly outline and define research objectives to guide the research process and ensure that the study is relevant and generates the impact you want.

In this article, we will explore research objectives and how to leverage them to achieve successful research studies.

What Are Research Objectives?

Research objectives are what you want to achieve through your research study. They guide your research process and help you focus on the most important aspects of your topic.

You can also define the scope of your study and set realistic and attainable study goals with research objectives. For example, with clear research objectives, your study focuses on the specific goals you want to achieve and prevents you from spending time and resources collecting unnecessary data.

However, sticking to research objectives isn’t always easy, especially in broad or unconventional research. This is why most researchers follow the SMART criteria when defining their research objectives.

Understanding SMART Criteria in Research

Think of research objectives as a roadmap to achieving your research goals, with the SMART criteria as your navigator on the map.

SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound. These criteria help you ensure that your research objectives are clear, specific, realistic, meaningful, and time-bound.

Here’s a breakdown of the SMART Criteria:

Specific : Your research objectives should be clear: what do you want to achieve, why do you want to achieve it, and how do you plan to achieve it? Avoid vague or broad statements that don’t provide enough direction for your research.

Measurable : Your research objectives should have metrics that help you track your progress and measure your results. Also, ensure the metrics are measurable with data to verify them.

Achievable : Your research objectives should be within your research scope, timeframe, and budget. Also, set goals that are challenging but not impossible.

Relevant: Your research objectives should be in line with the goal and significance of your study. Also, ensure that the objectives address a specific issue or knowledge gap that is interesting and relevant to your industry or niche.

Time-bound : Your research objectives should have a specific deadline or timeframe for completion. This will help you carefully set a schedule for your research activities and milestones and monitor your study progress.

Characteristics of Effective Research Objectives

Clarity : Your objectives should be clear and unambiguous so that anyone who reads them can understand what you intend to do. Avoid vague or general terms that could be taken out of context.

Specificity : Your objectives should be specific and address the research questions that you have formulated. Do not use broad or narrow objectives as they may restrict your field of research or make your research irrelevant.

Measurability : Define your metrics with indicators or metrics that help you determine if you’ve accomplished your goals or not. This will ensure you are tracking the research progress and making interventions when needed.

Also, do use objectives that are subjective or based on personal opinions, as they may be difficult to accurately verify and measure.

Achievability : Your objectives should be realistic and attainable, given the resources and time available for your research project. You should set objectives that match your skills and capabilities, they can be difficult but not so hard that they are realistically unachievable.

For example, setting very difficult make you lose confidence, and abandon your research. Also, setting very simple objectives could demotivate you and prevent you from closing the knowledge gap or making significant contributions to your field with your research.

Relevance : Your objectives should be relevant to your research topic and contribute to the existing knowledge in your field. Avoid objectives that are unrelated or insignificant, as they may waste your time or resources.

Time-bound : Your objectives should be time-bound and specify when you will complete them. Have a realistic and flexible timeframe for achieving your objectives, and track your progress with it. 

Steps to Writing Research Objectives

Identify the research questions.

The first step in writing effective research objectives is to identify the research questions that you are trying to answer. Research questions help you narrow down your topic and identify the gaps or problems that you want to address with your research.

For example, if you are interested in the impact of technology on children’s development, your research questions could be:

• What is the relationship between technology use and academic performance among children?
• Are children who use technology more likely to do better in school than those who do not?
• What is the social and psychological impact of technology use on children?

Brainstorm Objectives

Once you have your research questions, you can brainstorm possible objectives that relate to them. Objectives are more specific than research questions, and they tell you what you want to achieve or learn in your research.

You can use verbs such as analyze, compare, evaluate, explore, investigate, etc. to express your objectives. Also, try to generate as many objectives as possible, without worrying about their quality or feasibility at this stage.

Prioritize Objectives

Once you’ve brainstormed your objectives, you’ll need to prioritize them based on their relevance and feasibility. Relevance is how relevant the objective is to your research topic and how well it fits into your overall research objective.

Feasibility is how realistic and feasible the objective is compared to the time, money, and expertise you have. You can create a matrix or ranking system to organize your objectives and pick the ones that matter the most.

Refine Objectives

The next step is to refine and revise your objectives to ensure clarity and specificity. Start by ensuring that your objectives are consistent and coherent with each other and with your research questions. 

Make Objectives SMART

A useful way to refine your objectives is to make them SMART, which stands for specific, measurable, achievable, relevant, and time-bound. 

• Specific : Objectives should clearly state what you hope to achieve.
• Measurable : They should be able to be quantified or evaluated.
• Achievable : realistic and within the scope of the research study.
• Relevant : They should be directly related to the research questions.
• Time-bound : specific timeframe for research completion.

Review and Finalize Objectives

The final step is to review your objectives for coherence and alignment with your research questions and aim. Ensure your objectives are logically connected and consistent with each other and with the purpose of your study.

You also need to check that your objectives are not too broad or too narrow, too easy or too hard, too many or too few. You can use a checklist or a rubric to evaluate your objectives and make modifications.

Examples of Well-Written Research Objectives

Example 1- Psychology

Research question: What are the effects of social media use on teenagers’ mental health?

Objective : To determine the relationship between the amount of time teenagers in the US spend on social media and their levels of anxiety and depression before and after using social media.

What Makes the Research Objective SMART?

The research objective is specific because it clearly states what the researcher hopes to achieve. It is measurable because it can be quantified by measuring the levels of anxiety and depression in teenagers. 

Also, the objective is achievable because the researcher can collect enough data to answer the research question. It is relevant because it is directly related to the research question. It is time-bound because it has a specific deadline for completion.

Example 2- Marketing

Research question : How can a company increase its brand awareness by 10%?

Objective : To develop a marketing strategy that will increase the company’s sales by 10% within the next quarter.

How Is this Research Objective SMART?

The research states what the researcher hopes to achieve ( Specific ). You can also measure the company’s reach before and after the marketing plan is implemented ( Measurable ).

The research objective is also achievable because you can develop a marketing plan that will increase awareness by 10% within the timeframe. The objective is directly related to the research question ( Relevant ). It is also time-bound because it has a specific deadline for completion.

Research objectives are a well-designed roadmap to completing and achieving your overall research goal. 

However, research goals are only effective if they are well-defined and backed up with the best practices such as the SMART criteria. Properly defining research objectives will help you plan and conduct your research project effectively and efficiently.

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• research goals
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• research roadmap
• smart goals
• SMART research objectives
• Moradeke Owa

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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• Writing Tips

How to Write Research Objectives

3-minute read

• 22nd November 2021

Writing a research paper, thesis, or dissertation ? If so, you’ll want to state your research objectives in the introduction of your paper to make it clear to your readers what you’re trying to accomplish. But how do you write effective research objectives? In this post, we’ll look at two key topics to help you do this:

• How to use your research aims as a basis for developing objectives.
• How to use SMART criteria to refine your research objectives.

For more advice on how to write strong research objectives, see below.

Research Aims and Objectives

There is an important difference between research aims and research objectives:

• A research aim defines the main purpose of your research. As such, you can think of your research aim as answering the question “What are you doing?”
• Research objectives (as most studies will have more than one) are the steps you will take to fulfil your aims. As such, your objectives should answer the question “How are you conducting your research?”

For instance, an example research aim could be:

This study will investigate the link between dehydration and the incidence of urinary tract infections (UTIs) in intensive care patients in Australia.

To develop a set of research objectives, you would then break down the various steps involved in meeting said aim. For example:

This study will investigate the link between dehydration and the incidence of urinary tract infections (UTIs) in intensive care patients in Australia. To achieve this, the study objectives w ill include:

• Replicat ing a small Singaporean study into the role of dehydration in UTIs in hospital patients (Sepe, 2018) in a larger Australian cohort.
• Trialing the use of intravenous fluids for intensive care patients to prevent dehydration.
• Assessing the relationship between the age of patients and quantities of intravenous fluids needed to counter dehydration.

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Note that the objectives don’t go into any great detail here. The key is to briefly summarize each component of your study. You can save details for how you will conduct the research for the methodology section of your paper.

Make Your Research Objectives SMART

A great way to refine your research objectives is to use SMART criteria . Borrowed from the world of project management, there are many versions of this system. However, we’re going to focus on developing specific, measurable, achievable, relevant, and timebound objectives.

In other words, a good research objective should be all of the following:

• S pecific – Is the objective clear and well-defined?
• M easurable – How will you know when the objective has been achieved? Is there a way to measure the thing you’re seeking to do?
• A chievable – Do you have the support and resources necessary to undertake this action? Are you being overly ambitious with this objective?
• R elevant – Is this objective vital for fulfilling your research aim?
• T imebound – Can this action be realistically undertaken in the time you have?

If you follow this system, your research objectives will be much stronger.

Expert Research Proofreading

Whatever your research aims and objectives, make sure to have your academic writing proofread by the experts!

Our academic editors can help you with research papers and proposals , as well as any other scholarly document you need checking. And this will help to ensure that your academic writing is always clear, concise, and precise.

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Module 6: Marketing Information and Research

The marketing research process, learning objectives.

• Identify the steps of conducting a marketing research project

A Standard Approach to Research Inquiries

Marketing research is a useful and necessary tool for helping marketers and an organization’s executive leadership make wise decisions. Carrying out marketing research can involve highly specialized skills that go deeper than the information outlined in this module. However, it is important for any marketer to be familiar with the basic procedures and techniques of marketing research.

It is very likely that at some point a marketing professional will need to supervise an internal marketing research activity or to work with an outside marketing research firm to conduct a research project. Managers who understand the research function can do a better job of framing the problem and critically appraising the proposals made by research specialists. They are also in a better position to evaluate their findings and recommendations.

Periodically marketers themselves need to find solutions to marketing problems without the assistance of marketing research specialists inside or outside the company. If you are familiar with the basic procedures of marketing research, you can supervise and even conduct a reasonably satisfactory search for the information needed.

Step 1: Identify the Problem

The first step for any marketing research activity is to clearly identify and define the problem you are trying to solve. You start by stating the marketing or business problem you need to address and for which you need additional information to figure out a solution. Next, articulate the objectives for the research: What do you want to understand by the time the research project is completed? What specific information, guidance, or recommendations need to come out of the research in order to make it a worthwhile investment of the organization’s time and money?

It’s important to share the problem definition and research objectives with other team members to get their input and further refine your understanding of the problem and what is needed to solve it. At times, the problem you really need to solve is not the same problem that appears on the surface. Collaborating with other stakeholders helps refine your understanding of the problem, focus your thinking, and prioritize what you hope to learn from the research. Prioritizing your objectives is particularly helpful if you don’t have the time or resources to investigate everything you want.

To flesh out your understanding of the problem, it’s useful to begin brainstorming actual research questions you want to explore. What are the questions you need to answer in order to get to the research outcomes? What is the missing information that marketing research will help you find? The goal at this stage is to generate a set of preliminary, big-picture questions that will frame your research inquiry. You will revisit these research questions later in the process, but when you’re getting started, this exercise helps clarify the scope of the project, whom you need to talk to, what information may already be available, and where to look for the information you don’t yet have.

Applied Example: Marketing Research for Bookends

To illustrate the marketing research process, let’s return to Uncle Dan and his ailing bookstore, Bookends. You need a lot of information if you’re going to help Dan turn things around, so marketing research is a good idea. You begin by identifying the problem and then work to set down your research objectives and initial research questions:

Step 2: Develop a Research Plan

Once you have a problem definition, research objectives, and a preliminary set of research questions, the next step is to develop a research plan. Essential to this plan is identifying precisely what information you need to answer your questions and achieve your objectives. Do you need to understand customer opinions about something? Are you looking for a clearer picture of customer needs and related behaviors? Do you need sales, spending, or revenue data? Do you need information about competitors’ products, or insight about what will make prospective customers notice you? When do need the information, and what’s the time frame for getting it? What budget and resources are available?

Once you have clarified what kind of information you need and the timing and budget for your project, you can develop the research design. This details how you plan to collect and analyze the information you’re after. Some types of information are readily available through  secondary research and secondary data sources. Secondary research analyzes information that has already been collected for another purpose by a third party, such as a government agency, an industry association, or another company. Other types of information need to from talking directly to customers about your research questions. This is known as primary research , which collects primary data captured expressly for your research inquiry.   Marketing research projects may include secondary research, primary research, or both.

Depending on your objectives and budget, sometimes a small-scale project will be enough to get the insight and direction you need. At other times, in order to reach the level of certainty or detail required, you may need larger-scale research involving participation from hundreds or even thousands of individual consumers. The research plan lays out the information your project will capture—both primary and secondary data—and describes what you will do with it to get the answers you need. (Note: You’ll learn more about data collection methods and when to use them later in this module.)

Your data collection plan goes hand in hand with your analysis plan. Different types of analysis yield different types of results. The analysis plan should match the type of data you are collecting, as well as the outcomes your project is seeking and the resources at your disposal. Simpler research designs tend to require simpler analysis techniques. More complex research designs can yield powerful results, such as understanding causality and trade-offs in customer perceptions. However, these more sophisticated designs can require more time and money to execute effectively, both in terms of data collection and analytical expertise.

The research plan also specifies who will conduct the research activities, including data collection, analysis, interpretation, and reporting on results. At times a singlehanded marketing manager or research specialist runs the entire research project. At other times, a company may contract with a marketing research analyst or consulting firm to conduct the research. In this situation, the marketing manager provides supervisory oversight to ensure the research delivers on expectations.

Finally, the research plan indicates who will interpret the research findings and how the findings will be reported. This part of the research plan should consider the internal audience(s) for the research and what reporting format will be most helpful. Often, senior executives are primary stakeholders, and they’re anxious for marketing research to inform and validate their choices. When this is the case, getting their buy-in on the research plan is recommended to make sure that they are comfortable with the approach and receptive to the potential findings.

Applied Example: A Bookends Research Plan

You talk over the results of your problem identification work with Dan. He thinks you’re on the right track and wants to know what’s next. You explain that the next step is to put together a detailed plan for getting answers to the research questions.

Dan is enthusiastic, but he’s also short on money. You realize that such a financial constraint will limit what’s possible, but with Dan’s help you can do something worthwhile. Below is the research plan you sketch out:

Step 3: Conduct the Research

Conducting research can be a fun and exciting part of the marketing research process. After struggling with the gaps in your knowledge of market dynamics—which led you to embark on a marketing research project in the first place—now things are about to change. Conducting research begins to generate information that helps answer your urgent marketing questions.

Typically data collection begins by reviewing any existing research and data that provide some information or insight about the problem. As a rule, this is secondary research. Prior research projects, internal data analyses, industry reports, customer-satisfaction survey results, and other information sources may be worthwhile to review. Even though these resources may not answer your research questions fully, they may further illuminate the problem you are trying to solve. Secondary research and data sources are nearly always cheaper than capturing new information on your own. Your marketing research project should benefit from prior work wherever possible.

After getting everything you can from secondary research, it’s time to shift attention to primary research, if this is part of your research plan. Primary research involves asking questions and then listening to and/or observing the behavior of the target audience you are studying. In order to generate reliable, accurate results, it is important to use proper scientific methods for primary research data collection and analysis. This includes identifying the right individuals and number of people to talk to, using carefully worded surveys or interview scripts, and capturing data accurately.

Without proper techniques, you may inadvertently get bad data or discover bias in the responses that distorts the results and points you in the wrong direction. The module on Marketing Research Techniques discusses these issues in further detail, since the procedures for getting reliable data vary by research method.

Applied Example: Getting the Data on Bookends

Dan is on board with the research plan, and he’s excited to dig into the project. You start with secondary data, getting a dump of Dan’s sales data from the past two years, along with related information: customer name, zip code, frequency of purchase, gender, date of purchase, and discounts/promotions (if any).

You visit the U.S. Census Bureau Web site to download demographic data about your metro area. The data show all zip codes in the area, along with population size, gender breakdown, age ranges, income, and education levels.

The next part of the project is customer-survey data. You work with Dan to put together a short survey about customer attitudes toward Bookends, how often and why they come, where else they spend money on books and entertainment, and why they go other places besides Bookends. Dan comes up with the great idea of offering a 5 percent discount coupon to anyone who completes the survey. Although it eats into his profits, this scheme gets more people to complete the survey and buy books, so it’s worth it.

For a couple of days, you and Dan take turns doing “man on the street” interviews (you interview the guy in the red hat, for instance). You find people who say they’ve never been to Bookends and ask them a few questions about why they haven’t visited the store, where else they buy books and other entertainment, and what might get them interested in visiting Bookends sometime. This is all a lot of work, but for a zero-budget project, it’s coming together pretty well.

Step 4: Analyze and Report Findings

Analyzing the data obtained in a market survey involves transforming the primary and/or secondary data into useful information and insights that answer the research questions. This information is condensed into a format to be used by managers—usually a presentation or detailed report.

Analysis starts with formatting, cleaning, and editing the data to make sure that it’s suitable for whatever analytical techniques are being used. Next, data are tabulated to show what’s happening: What do customers actually think? What’s happening with purchasing or other behaviors? How do revenue figures actually add up? Whatever the research questions, the analysis takes source data and applies analytical techniques to provide a clearer picture of what’s going on. This process may involve simple or sophisticated techniques, depending on the research outcomes required. Common analytical techniques include regression analysis to determine correlations between factors; conjoint analysis to determine trade-offs and priorities; predictive modeling to anticipate patterns and causality; and analysis of unstructured data such as Internet search terms or social media posts to provide context and meaning around what people say and do.

Good analysis is important because the interpretation of research data—the “so what?” factor—depends on it. The analysis combs through data to paint a picture of what’s going on. The interpretation goes further to explain what the research data mean and make recommendations about what managers need to know and do based on the research results. For example, what is the short list of key findings and takeaways that managers should remember from the research? What are the market segments you’ve identified, and which ones should you target?  What are the primary reasons your customers choose your competitor’s product over yours, and what does this mean for future improvements to your product?

Individuals with a good working knowledge of the business should be involved in interpreting the data because they are in the best position to identify significant insights and make recommendations from the research findings. Marketing research reports incorporate both analysis and interpretation of data to address the project objectives.

The final report for a marketing research project may be in written form or slide-presentation format, depending on organizational culture and management preferences. Often a slide presentation is the preferred format for initially sharing research results with internal stakeholders. Particularly for large, complex projects, a written report may be a better format for discussing detailed findings and nuances in the data, which managers can study and reference in the future.

Applied Example: Analysis and Insights for Bookends

Getting the data was a bit of a hassle, but now you’ve got it, and you’re excited to see what it reveals. Your statistician cousin, Marina, turns out to be a whiz with both the sales data and the census data. She identified several demographic profiles in the metro area that looked a lot like lifestyle segments. Then she mapped Bookends’ sales data into those segments to show who is and isn’t visiting Bookends. After matching customer-survey data to the sales data, she broke down the segments further based on their spending levels and reasons they visit Bookends.

Gradually a clearer picture of Bookends’ customers is beginning to emerge: who they are, why they come, why they don’t come, and what role Bookends plays in their lives. Right away, a couple of higher-priority segments—based on their spending levels, proximity, and loyalty to Bookends—stand out. You and your uncle are definitely seeing some possibilities for making the bookstore a more prominent part of their lives. You capture these insights as “recommendations to be considered” while you evaluate the right marketing mix for each of the new segments you’d like to focus on.

Step 5: Take Action

Once the report is complete, the presentation is delivered, and the recommendations are made, the marketing research project is over, right? Wrong.

What comes next is arguably the most important step of all: taking action based on your research results.

If your project has done a good job interpreting the findings and translating them into recommendations for the marketing team and other areas of the business, this step may seem relatively straightforward. When the research results validate a path the organization is already on, the “take action” step can galvanize the team to move further and faster in that same direction.

Things are not so simple when the research results indicate a new direction or a significant shift is advisable. In these cases, it’s worthwhile to spend time helping managers understand the research, explain why it is wise to shift course, and explain how the business will benefit from the new path. As with any important business decision, managers must think deeply about the new approach and carefully map strategies, tactics, and available resources to plan effectively. By making the results available and accessible to managers and their execution teams, the marketing research project can serve as an ongoing guide and touchstone to help the organization plan, execute, and adjust course as it works toward desired goals and outcomes.

It is worth mentioning that many marketing research projects are never translated into management action. Sometimes this is because the report is too technical and difficult to understand. In other cases, the research conclusions fail to provide useful insights or solutions to the problem, or the report writer fails to offer specific suggestions for translating the research findings into management strategy. These pitfalls can be avoided by paying due attention to the research objectives throughout the project and allocating sufficient time and resources to do a good job interpreting research results for those who will need to act on them.

Applied Example: Bookends’ New Customer Campaign

Your research findings and recommendations identified three segments for Bookends to focus on. Based on the demographics, lifestyle, and spending patterns found during your marketing research, you’re able to name them: 1) Bored Empty-Nesters, 2) Busy Families, and 3) Hipster Wannabes. Dan has a decent-sized clientele across all three groups, and they are pretty good spenders when they come in. But until now he hasn’t done much to purposely attract any of them.

With newly identified segments in focus, you and Dan begin brainstorming about a marketing mix to target each group. What types of books and other products would appeal to each one? What activities or events would bring them into the store? Are there promotions or particular messages that would induce them to buy at Bookends instead of Amazon or another bookseller? How will Dan reach and communicate with each group? And what can you do to bring more new customers into the store within these target groups?

Even though Bookends is a real-life project with serious consequences for your uncle Dan, it’s also a fun laboratory where you can test out some of the principles you’re learning in your marketing class. You’re figuring out quickly what it’s like to be a marketer.

Well done, rookie!

Check Your Understanding

Answer the question(s) below to see how well you understand the topics covered in this outcome. This short quiz does  not  count toward your grade in the class, and you can retake it an unlimited number of times.

Use this quiz to check your understanding and decide whether to (1) study the previous section further or (2) move on to the next section.

• Revision and Adaptation. Authored by : Lumen Learning. License : CC BY: Attribution
• Chapter 3: Marketing Research: An Aid to Decision Making, from Introducing Marketing. Authored by : John Burnett. Provided by : Global Text. Located at : http://solr.bccampus.ca:8001/bcc/file/ddbe3343-9796-4801-a0cb-7af7b02e3191/1/Core%20Concepts%20of%20Marketing.pdf . License : CC BY: Attribution
• Urban life (Version 2.0). Authored by : Ian D. Keating. Located at : https://www.flickr.com/photos/ian-arlett/19313315520/ . License : CC BY: Attribution

Formulating Research Aims and Objectives

Formulating research aim and objectives in an appropriate manner is one of the most important aspects of your thesis. This is because research aim and objectives determine the scope, depth and the overall direction of the research. Research question is the central question of the study that has to be answered on the basis of research findings.

Research aim emphasizes what needs to be achieved within the scope of the research, by the end of the research process. Achievement of research aim provides answer to the research question.

Research objectives divide research aim into several parts and address each part separately. Research aim specifies WHAT needs to be studied and research objectives comprise a number of steps that address HOW research aim will be achieved.

As a rule of dumb, there would be one research aim and several research objectives. Achievement of each research objective will lead to the achievement of the research aim.

Consider the following as an example:

Research title: Effects of organizational culture on business profitability: a case study of Virgin Atlantic

Research aim: To assess the effects of Virgin Atlantic organizational culture on business profitability

Following research objectives would facilitate the achievement of this aim:

• Analyzing the nature of organizational culture at Virgin Atlantic by September 1, 2022
• Identifying factors impacting Virgin Atlantic organizational culture by September 16, 2022
• Analyzing impacts of Virgin Atlantic organizational culture on employee performances by September 30, 2022
• Providing recommendations to Virgin Atlantic strategic level management in terms of increasing the level of effectiveness of organizational culture by October 5, 2022

Figure below illustrates additional examples in formulating research aims and objectives:

Formulation of research question, aim and objectives

Common mistakes in the formulation of research aim relate to the following:

1. Choosing the topic too broadly . This is the most common mistake. For example, a research title of “an analysis of leadership practices” can be classified as too broad because the title fails to answer the following questions:

a) Which aspects of leadership practices? Leadership has many aspects such as employee motivation, ethical behaviour, strategic planning, change management etc. An attempt to cover all of these aspects of organizational leadership within a single research will result in an unfocused and poor work.

b) An analysis of leadership practices in which country? Leadership practices tend to be different in various countries due to cross-cultural differences, legislations and a range of other region-specific factors. Therefore, a study of leadership practices needs to be country-specific.

c) Analysis of leadership practices in which company or industry? Similar to the point above, analysis of leadership practices needs to take into account industry-specific and/or company-specific differences, and there is no way to conduct a leadership research that relates to all industries and organizations in an equal manner.

Accordingly, as an example “a study into the impacts of ethical behaviour of a leader on the level of employee motivation in US healthcare sector” would be a more appropriate title than simply “An analysis of leadership practices”.

2. Setting an unrealistic aim . Formulation of a research aim that involves in-depth interviews with Apple strategic level management by an undergraduate level student can be specified as a bit over-ambitious. This is because securing an interview with Apple CEO Tim Cook or members of Apple Board of Directors might not be easy. This is an extreme example of course, but you got the idea. Instead, you may aim to interview the manager of your local Apple store and adopt a more feasible strategy to get your dissertation completed.

3. Choosing research methods incompatible with the timeframe available . Conducting interviews with 20 sample group members and collecting primary data through 2 focus groups when only three months left until submission of your dissertation can be very difficult, if not impossible. Accordingly, timeframe available need to be taken into account when formulating research aims and objectives and selecting research methods.

Moreover, research objectives need to be formulated according to SMART principle,

 where the abbreviation stands for specific, measurable, achievable, realistic, and time-bound.

Examples of SMART research objectives

At the conclusion part of your research project you will need to reflect on the level of achievement of research aims and objectives. In case your research aims and objectives are not fully achieved by the end of the study, you will need to discuss the reasons. These may include initial inappropriate formulation of research aims and objectives, effects of other variables that were not considered at the beginning of the research or changes in some circumstances during the research process.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

• Open access
• Published: 08 May 2024

The relationship between childhood adversity and sleep quality among rural older adults in China: the mediating role of anxiety and negative coping

• Yuqin Zhang 1 ,
• Chengwei Lin 2 ,
• Hongwei Li 1 ,
• Xueyan Zhou 4 ,
• Ying Xiong 5 ,
• Jin Yan 1 ,
• Mengxue Xie 1 ,
• Xueli Zhang 6 ,
• Chengchao Zhou 7 &
• Lian Yang 1  

BMC Psychiatry volume  24 , Article number:  346 ( 2024 ) Cite this article

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Metrics details

Studies have revealed the effects of childhood adversity, anxiety, and negative coping on sleep quality in older adults, but few studies have focused on the association between childhood adversity and sleep quality in rural older adults and the potential mechanisms of this influence. In this study, we aim to evaluate sleep quality in rural older adults, analyze the impact of adverse early experiences on their sleep quality, and explore whether anxiety and negative coping mediate this relationship.

Data were derived from a large cross-sectional study conducted in Deyang City, China, which recruited 6,318 people aged 65 years and older. After excluding non-agricultural household registration and lack of key information, a total of 3,873 rural older adults were included in the analysis. Structural equation modelling (SEM) was used to analyze the relationship between childhood adversity and sleep quality, and the mediating role of anxiety and negative coping.

Approximately 48.15% of rural older adults had poor sleep quality, and older adults who were women, less educated, widowed, or living alone or had chronic illnesses had poorer sleep quality. Through structural equation model fitting, the total effect value of childhood adversity on sleep quality was 0.208 (95% CI: 0.146, 0.270), with a direct effect value of 0.066 (95% CI: 0.006, 0.130), accounting for 31.73% of the total effect; the total indirect effect value was 0.142 (95% CI: 0.119, 0.170), accounting for 68.27% of the total effect. The mediating effects of childhood adversity on sleep quality through anxiety and negative coping were significant, with effect values of 0.096 (95% CI: 0.078, 0.119) and 0.024 (95% CI: 0.014, 0.037), respectively. The chain mediating effect of anxiety and negative coping between childhood adversity and sleep quality was also significant, with an effect value of 0.022 (95% CI: 0.017, 0.028).

Conclusions

Anxiety and negative coping were important mediating factors for rural older adult’s childhood adversity and sleep quality. This suggests that managing anxiety and negative coping in older adults may mitigate the negative effects of childhood adversity on sleep quality.

Peer Review reports

The global population is entering an aging stage, and China has the fastest rate of population aging in the world. According to China’s seventh national census, in 2020, 191 million individuals were aged 65 years and older, accounting for 13.50% of the total population [ 1 ], and the proportion of people aged 65 and above in rural is 6.6% higher than in urban [ 2 ]. In addition, China’s long-standing urban-rural dual structure has resulted in inequality in economic, medical, and educational development, leading to significant differences in the health status of China’s urban and rural older populations [ 3 , 4 ]. Relevant studies have found that, urban residents have a higher survival rate [ 5 ], better self-assessed health status and better self-assessed self-care ability than rural dwellers [ 6 ]. Therefore, to reduce health inequalities among older adults, the health status of rural older adults is an important focus.

Good quality sleep has been found to be essential for health [ 7 , 8 , 9 ]. However, sleep problems are prevalent among the older population [ 10 , 11 ]. Gulia and Tatineny have reported that the current prevalence of sleep disorders in the global older population is 30–40% [ 12 , 13 ]. In a systematic review, Lu reported that the overall prevalence of poor sleep among the older population in China had reached 35.9% [ 14 ]. In the rural older adults, the prevalence of sleep disorders is more than 40% [ 15 ], even as high as 58.40% [ 16 ].There are various factors that affect sleep quality [ 17 , 18 ]. Adverse childhood experiences (ACEs) are stressful and/or traumatic experiences that occur during childhood [ 19 ]. There is growing evidence that ACEs may lead to sleep problems in adulthood [ 20 , 21 ] and that the influence can last up to 50 years [ 22 ]. For example, emotional abuse and neglect experienced early in life impede the development of individuals’ social relationships later in life and negatively affect the subjective sleep quality of older adults [ 23 ]. A study by Dorji. found that older adults with multiple (≥ 7) ACEs had a higher incidence of insomnia [ 24 ]. Although previous investigations have indicated the relationship between childhood adversity and sleep quality in older adults, they have ignored possible potential mechanisms for this relationship.

Previous studies have found that anxiety negatively affects sleep quality in older adults [ 25 ], whereas a good mental state can improve their sleep quality. Notably, childhood adversity may be associated with increased anxiety symptoms in late adulthood [ 26 ]. Raposo have reported that older adults who experienced childhood adversity were more likely to suffer from anxiety (OR = 1.48; 95%CI = 1.20–1.83) [ 27 ]. Considering the relationships among anxiety, childhood adversity, and sleep quality, one aim of this study was to verify whether anxiety mediates the relationship between childhood adversity and sleep quality.

A coping style refers to a psychological and behavioral strategy adopted by an individual in response to changes in the internal and external environment [ 28 ]. Negative coping is usually positively associated with sleep disorders [ 29 , 30 ]. Coping style usually evolves over time and may be influenced by exposure to childhood adversity; for instance, people exposed to early adverse experiences show predominantly emotion-focused and avoidance coping styles, such as denial and disengagement [ 31 , 32 ]. In addition to childhood adversity, negative emotions or psychological states also can influence individuals’ coping strategies [ 33 ]. For example, Orgeta reported that older adults with high levels of anxiety were more likely to adopt dysfunctional coping [ 34 ]. Therefore, we hypothesized that anxiety affects coping styles in older adults and that negative coping may be a potential mediator between childhood adversity and sleep quality.

Stress is defined as the process of adaptive and coping responses when an individual faces or perceives threatening or challenging environmental changes [ 35 ]. People respond to stress with either problem-focused coping or emotion-focused coping [ 36 ]. Stress can be caused by many factors, such as early adversities, and the result of stress is adaptive or maladaptive psychosomatic responses. Based on the above, we constructed a structural equation model of a large cross-sectional dataset to explore the effects of childhood adversity on sleep quality, with childhood adversity as the stressor and anxiety and negative coping as mediators.

Research methods

Research population.

The data were sourced from a large-scale cross-sectional study conducted in 2022 that recruited older adults aged 65 years and older living in 6 districts and counties in Deyang City, Sichuan Province. Using a multistage stratified random cluster sampling method, townships (streets) were randomly selected from six county (districts), administrative villages (communities) were randomly selected from each sample township (streets), finally, people over 65 years old were selected randomly in each chosen village or community. The inclusion criteria were as follows: (1) individuals aged ≥ 65 years; (2) permanent residents in the survey area (those who have lived in the area for 6 months or more); (3) those who signed an informed consent form and agreed to take the questionnaire survey. The exclusion criteria were as follows: (1) unwilling to participate in research; (2) individuals identified by local village doctors who are unable to answer questions independently and have a history of dementia;3) other reasons for not participating in the study. The household registration system is a very important factor affecting the unequal social welfare rights and privileges of urban and rural residents in China [ 37 ], which is associated with poor health [ 38 ]. In this study, rural means that residents with agricultural household registration. A total of 6318 respondents were recruited, excluding non-agricultural household registration (2345) and missing main information (100), and finally included 3873 for analysis. The study was approved by the Medical Ethics Committee of the Affiliated Hospital of Chengdu University of Chinese Medicine, and all participants signed an informed consent form before taking the survey.

Measurement tools

General information.

This includes the age, gender, education level, marital status, chronic disease status, and exercise status of the participating older adults.

Childhood adversity

Childhood adversity was measured using the Adverse Childhood Experiences Scale developed by the Centers for Disease Control and Prevention (USA). The scale contains three major dimensions (abuse, neglect, and household dysfunction) and ten subdimensions including emotional abuse, physical abuse, sexual abuse, and emotional neglect. Higher ACE scores indicate more severe ACE exposure [ 19 , 39 ]. The internal consistency coefficients of the abuse, neglect, and household dysfunction subscales in this study were 0.790, 0.732, and 0.778, respectively.

• Sleep quality

Sleep quality was evaluated using the revised Chinese-version Pittsburgh Sleep Quality Index (PSQI). The scale consists of seven dimensions including subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction. A PSQI score of ≥ 7 is generally considered to indicate poor sleep quality [ 15 , 40 , 41 ]. The internal consistency coefficient of the scale in this study was 0.754.

Anxiety in older adults was measured using the Self-Rating Anxiety Scale (SAS). The scale consists of 20 items and is rated on a 4-point scale. An SAS score of 50 or more is considered to be indicative of anxiety symptoms [ 42 ]. The internal consistency coefficient of this scale in this study was 0.831.

Trait coping style

Negative coping was measured using the Trait Coping Style Questionnaire (TCSQ). The scale consists of 20 questions in 2 dimensions—negative coping and positive coping—and is rated on a 5-point scale. The negative coping and positive coping scores are the sum of the scores for each item in the corresponding dimensions. A positive total score indicates a predominantly positive attitude toward coping with events, whereas a negative score indicates a predominantly negative coping style [ 43 , 44 ]. Only the negative coping dimension of the scale, which has an internal consistency coefficient of 0.929, was selected in this study.

Statistical analysis

The variables in the study were descriptively analyzed using the mean, standard deviation, frequency (n), and constituent ratio (%), and difference tests were conducted using t-tests and the Kruskal-Wallis H test. Spearman’s correlation was used to analyze whether there were correlations between sleep quality and the variables. Finally, a multiple-mediator structural equation model was constructed to analyze the effects of anxiety and negative coping on the relationship between sleep quality and childhood adversity, and the bootstrap method was applied to verify the mediating effect. After the initial establishment of the model, we evaluated the fit degree of the structural equation model and adjusted the model via calculating indicators such as standardized root-mean-square residual (SRMR ≤ 0.08), root-mean-square error of approximation (RMSEA ≤ 0.08), goodness of fit index (GFI ≥ 0.90), comparative fit index (CFI ≥ 0.90), normed fit index (NFI ≥ 0.90) according to the studies by Wen and Kang [ 45 , 46 ]. Data were analyzed using SPSS 25.0 and AMOS 24.0 software, and a P value < 0.05 was considered to be statistically significant. The bootstrap CI was set to 95%, and the bootstrap sample size was 5000. If the 95% CI interval does not contain 0, it indicates a significant mediating effect.

Research results

Comparison of the general information and sleep quality scores of the study participants.

A total of 3,873 older adults were included in this study. The mean participant age was 72.84 ± 6.13 years, ranging from a minimum of 65 years to a maximum of 99 years. The mean PISQ score was 6.94 ± 3.88, and older adults with poor sleep quality (PSQI score ≥ 7) accounted for 48.15%. The mean ACE score was 2.09 ± 1.16, the mean SAS score was 44.13 ± 9.84, and the mean TCSQ negative coping score was 21.88 ± 8.23.

The results of univariate analysis showed that among the different gender populations, women had poorer sleep quality and a statistically significantly higher PSQI score than men at 7.44 ± 3.98 (t = 8.845, p  < 0.001). The PSQI score increased with age: that of adults aged 80 years and older was 7.32 ± 4.01, and the difference was statistically significant (H = 11.125, p  = 0.004). Regarding the groups with different educational levels, the highest PSQI score was found among illiteracy individuals (7.39 ± 4.01), with a statistically significant difference (H = 39.885, p  < 0.001). Sleep quality varied among older adults with different marital statuses, and the worst sleep quality was found in widowed older adults, with a PSQI score of 7.52 ± 4.00, which presented a statistically significant difference (H = 39.582, p  < 0.001). Older adults living alone had the worst sleep quality with a statistically significantly different PSQI score of 7.46 ± 3.90 (H = 20.904, p  < 0.001). Older adults with chronic diseases had poor sleep quality with a statistically significantly different PSQI score of 7.4 ± 3.95 (t=-8.83, p  < 0.001) (Table  1 ).

Association of sleep quality with childhood adversity, anxiety, and negative coping in rural older adults

The relevant analysis results indicated that the PSQI score was positively correlated with the ACE score ( r  = 0.092, P  < 0.01). The PSQI score was positively correlated with the SAS score and negative coping score ( r  = 0.279 and r  = 0.239, respectively; both P  < 0.01). The ACE score was positively correlated with the SAS score and negative coping score ( r  = 0.217 and r  = 0.133, respectively; both P  < 0.01). There was also a positive correlation between the SAS score and negative coping score ( r  = 0.351, P  < 0.01) (Table  2 ).

Analysis of mediating effects

Goodness-of-fit indices and path coefficients for the theoretical model of older adults’ sleep quality.

Based on the results of the above analyses, a structural equation model was constructed with childhood adversity as the independent variable, anxiety and negative coping as the mediating variables, and sleep quality as the dependent variable. The final model was screened according to the following model fitting indices: SRMR = 0.05, RMSEA = 0.06, GFI = 0.97, CFI = 0.90 and NFI = 0.89. The results of the fitting indices indicated that the model was well fitted. The differences in each of the standardized path coefficients in the model were statistically significant (all P  < 0.05) (Fig.  1 ).

Serial mediation models for childhood adversity, anxiety, negative coping and sleep quality

Bootstrap test of the theoretical model of older adults’ sleep quality

Table  3 demonstrates the results of structural modeling: (1) The total effect value of childhood adversity on sleep quality was 0.208 (95% CI: 0.146, 0.270), with a direct effect value of 0.066 (95% CI: 0.006, 0.130), accounting for 31.73% of the total effect, and a total indirect effect value of 0.142 (95% CI: 0.119, 0.170), accounting for 68.27% of the total effect. (2) The mediating effect of anxiety on the association between childhood adversity and sleep quality was significant, with a path effect value of 0.096 (95% CI: 0.078, 0.119), accounting for 46.15% of the total effect. (3) The mediating effect of negative coping on the association between childhood adversity on sleep quality was significant, with a path effect value of 0.024 (95% CI: 0.014, 0.037), accounting for 11.54% of the total effect. (4) The multiple mediating effects of anxiety and negative coping on the association between childhood adversity on sleep quality were also significant, with a pathway effect value of 0.022 (95% CI: 0.017, 0.028), accounting for 10.58% of the total effect (Table  3 ).

Current status and influencing factors of sleep quality in older adults

The proportion of older adults with poor sleep quality (PSQI score ≥ 7) was 48.15%, which is similar to the results of previous studies [ 15 , 16 ]. Due to gradual aging, the sleep-wake cycle of the older adults is disordered, and the efficiency of the circadian rhythm mechanism is reduced, which leads to changes in their sleep duration, sleep architecture, and sleep depth [ 12 ]. Furthermore, the occurrence of a variety of sleep problems such as sleep disruption, early sleep onset, and early awakening [ 47 , 48 , 49 ], result in a general decline in the sleep quality of older adults. We also found that gender, educational level, marital status, residency status, and chronic diseases were influencing factors of sleep quality. First, women have poorer sleep quality than men, which is in accordance with the established viewpoint [ 50 , 51 ]. Poor sleep quality and an increased risk of sleep disorders in older women may be due to the following reasons: (1) women are at a disadvantage in terms of socioeconomic factors, such as education and personal income [ 52 ]; (2) women are more susceptible to somatic [ 53 ] and psychiatric [ 54 , 55 ] disorders than men; and (3) women experience changes in secreted reproductive hormones [ 56 ]. Second, differences in sleep quality among older adults with different educational levels may be due to the fact that well-educated older adults have a higher sense of wellness and are more likely to access healthcare knowledge, which in turn leads to a better sleep state [ 57 ]. Third, the poorer sleep quality in widowed older adults and those living alone than in others may be related to loneliness and lack of social support leading to mood disorders, which in turn may cause reduced sleep efficiency and quality [ 58 ]. Finally, having a chronic disease is also a risk factor for poor sleep quality in older adults, which may be related to the physical discomfort caused by chronic diseases, the side effects of medications, and the associated financial pressure and psychological burden [ 59 ].

Direct effect of childhood adversity on sleep quality in older adults

The present study found that childhood adversity had a direct effect on sleep quality. Early life experiences, such as abuse, poverty, or the death of a parent, can affect sleep not only in childhood and adolescence but also in adulthood [ 60 , 61 ]. Childhood is an important phase for significant development of the hypothalamic-pituitary-adrenal (HPA) axis and the brain [ 58 ], and adverse events experienced during childhood can lead to long-term changes in the HPA axis response to stress (e.g., hyperactivity) and interfere with normal neurodevelopment in childhood and adolescence [ 62 ], increasing the risk of developing psychiatric disorders such as depression and post-traumatic stress disorder, which indirectly affect sleep in adulthood [ 63 ]. In addition, people exposed to ACEs are more likely to adopt unhealthy lifestyles and behaviors [ 64 , 65 ], and these changes may directly affect the sleep-wake cycle and lead to sleep problems.

Mediating effect of anxiety between childhood adversity and sleep quality in older adults

Sleep problems are not only a precursor but also a consequence of mental illness [ 66 , 67 ]. Our study found that anxiety could partially explain the relationship between childhood adversity and sleep disorders. Extensive studies have confirmed that exposure to adverse experiences in early life can increase an individual’s risk of developing psychiatric disorders such as anxiety and depression [ 68 , 69 ]. Anxiety is thus associated with a variety of sleep problems, with higher levels of anxiety corresponding to more severe sleep disorders [ 25 , 70 , 71 ]. Furthermore, anxiety has been found to mediate the effects of childhood adversity on sleep quality. For example, Amarneh found that elevated levels of anxiety sensitivity may explain the relationship between child maltreatment and adult sleep disorders among psychiatric hospitalizations [ 72 ]. Haimov found that COVID-19-related anxiety mediated the association between the number of childhood adversities and adult sleep quality [ 73 ]. The findings of our study further support the mediating role of anxiety on the effects of childhood adversity on sleep quality in older adults, suggesting that actively intervening in older adults’ anxiety states may mitigate the effects of childhood adversity on their sleep quality.

Mediating effect of negative coping between childhood adversity and sleep quality in older adults

Our results also identified a significant mediating effect of negative coping in the action of childhood adversity on sleep quality. Individuals’ exposure to environmental stressors early in life can compromise their adaptive coping strategies [ 74 ] and thus further affect sleep [ 75 ]. This result can be explained by the theory of stress. This theory states that when facing stressful events, people may take measures to disengage from threatening stimuli and generate associated thoughts and emotions (i.e., reducing activity and sleeping longer to minimize exposure to the stressor and the associated maladaptive emotions and thoughts) as well as adopt emotion-focused coping (i.e., regulating emotional responses to problems). However, such approaches may increase alertness and thus produce physiological arousal, disrupting or reducing sleep, which in turn affects sleep quality [ 76 ].

Finally, we founded that childhood adversity affected sleep quality in older adults through anxiety and negative coping. As mentioned above, stressful life events in childhood are associated with an increased risk of anxiety disorders in adulthood. Under the influence of such negative emotions, individuals are more inclined to adopt negative coping, which in turn affects the sleep quality in older adults. The above results facilitate a deeper understanding of the relationships among childhood adversity, anxiety, negative coping, and sleep quality and provide clues for exploring the potential mechanisms of how childhood adversity affects sleep quality in older adults.

Research limitations

In this study, the theoretical structural equation model fit the data well and provided an epidemiologic basis for the associations among childhood adversity, anxiety, negative coping, and sleep quality. However, there are several limitations. First, the results for the main variables in this study were obtained via self-report from the respondents and thus may be subject to unavoidable recall bias. Second, this study utilized a cross-sectional research design, which does not allow for a more precise determination of the causal relationship between variables. Third, this study explored the relationship between ACEs and PSQI scores but did not determine a dose-response relationship or whether different types of childhood adversities have different effects on sleep quality. Finally, the effects of drugs (such as antidepressants and anti-inflammatory drugs) on sleep quality were ignored in this study.

To sum up, anxiety and negative coping not only had direct effects on sleep quality but also played mediating roles in the association between childhood adversity and sleep quality, with a chained multiple mediating effect. These findings suggest that timely intervention for anxiety symptoms and negative coping states in older adults may mitigate the negative impact of childhood adversity on sleep quality.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Adverse Childhood Experiences

the revised Chinese-version Pittsburgh Sleep Quality Index

Self-Rating Anxiety Scale

Trait Coping Style Questionnaire

Structural equation modelling

confidence interval

root mean square error of approximation

the hypo-thalamic pituitary adrenal axis

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Acknowledgements

We thank the responsible person of local health work, all participants and the staff of data reduction for their cooperation.

This work was funded by the research projects of “Investigation on health status and risk factors of the elderly over 65 years old in Deyang City” (No.301021062) of Chengdu University of Traditional Chinese Medicine.

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YQ Z, CW L and HW L were responsible for conception and design of the study. L L, XY Z and Y X were involved in recruiting the participants. YQ Z and CW L did the statistical analysis and were involved in manuscript preparation and drafting the article.J Y , MX X, and XL Z were involved in editing and revising the manuscript. CC Z and L Y were responsible for the critical revision of the manuscript. All authors have contributed to and have approved the final manuscript.

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Zhang, Y., Lin, C., Li, H. et al. The relationship between childhood adversity and sleep quality among rural older adults in China: the mediating role of anxiety and negative coping. BMC Psychiatry 24 , 346 (2024). https://doi.org/10.1186/s12888-024-05792-2

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Authors of this article:

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• Jiaqi Fu 1, 2 * , MD   ; 
• Jie Lai 1, 2 * , MD   ; 
• Shisi Deng 1, 2 , MD   ; 
• Zihan Guo 1, 2 , MD   ; 
• Chuhan Zhong 1, 2 , MD   ; 
• Jianyao Tang 1, 2 , MD   ; 
• Wenqiong Cao 1 , BSc   ; 
• Yanni Wu 1 , PhD  

1 Nanfang Hospital, Southern Medical University, Guangzhou, China

2 School of Nursing, Southern Medical University, Guangzhou, China

*these authors contributed equally

Corresponding Author:

Yanni Wu, PhD

Nanfang Hospital

Southern Medical University

No 1838 Guangzhou Avenue North

Baiyun District, Guangdong Province

Guangzhou, 510515

Phone: 86 02061641192

Email: [email protected]

Background: The internet community has become a significant source for researchers to conduct qualitative studies analyzing users’ views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data.

Objective: This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care.

Methods: We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

Results: After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users’ written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users’ privacy. Furthermore, the rates of reporting ethical approval were different between different countries ( P =.02) and between papers that contained users’ written content (both direct and paraphrased quotes) and papers that did not contain users’ written content ( P <.001).

Conclusions: Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

Introduction

Social media are web-based computer-mediated tools to collaborate, share, or exchange information, ideas, pictures, or videos in virtual communities and networks such as message boards, communities, chat rooms, forums, Twitter, and Facebook [ 1 ]. Moreover, patients and researchers can use internet communities to provide health care and disseminate health information [ 2 , 3 ]. Health care refers to the efforts made to improve or maintain physical, mental, or emotional well-being, including prevention, diagnosis, treatment, recovery, and other physical and mental impairments [ 4 ]. Currently, with 57% of the global population’s access to social media, more than 40% of the patients and caregivers worldwide utilize the internet community for health care information needs [ 5 ]. With diverse populations accessing internet communities and sharing information about health care topics, researchers have the opportunity to collect and analyze text about health care from a diverse range of participants in the internet community, which was unavailable previously [ 6 ]. Usually, quantitative data are derived from information extraction, which can be analyzed statistically, and the summary results presented cannot be directly linked to individual participants. In contrast, qualitative research within internet community analysis posts and comments qualitatively or thematically involves a more detailed and in-depth analysis and understanding of the full written content [ 7 ]. However, a controversial ethical problem has been raised about conducting qualitative research containing internet users’ verbatim quotes that could lead to traceability of the original post, thereby causing a threat to an individual’s privacy [ 8 ]. Additionally, a previous study investigated public and patients’ views regarding ethics in research using social media data and reported that internet users were aggrieved if they found any of their quotes cited in a medical research paper without obtaining their informed consent [ 9 ]. Further, besides the privacy breach caused by posts being traced, there is greater harm for special groups or vulnerable groups if we do not highlight the importance of the technical standards for text mining and privacy protection in health care. For instance, some unusual postings, abnormal pictures, and interactions that were expressed by individuals with mental disorders in social media can be detected by researchers by using text mining tools without obtaining their consent [ 10 ]. The publication of research on mental disorders, including quotes in posts, can result in a high risk of information harm, which can lead to personal information being revealed and further stigmatization of the condition or disease [ 11 ]. Since 2001, ethical concerns have been debated for decades about ethical approval, informed consent, and how to ensure anonymity and preserve data privacy and confidentiality in qualitative research in the internet community [ 12 - 14 ].

With the rapid development of social media and internet research, some ethical guidelines or standards have been published to ensure that research based on internet communities is conducted ethically. The Association of Internet Researchers (internet research ethical guidelines 2.0 and 3.0) showed that researchers working without the direct approval of ethics review boards would have additional challenges to face, and obtaining informed consent is obviously impracticable in several big data projects. However, with the ethical issues about privacy breaches and harms of risk of discrimination, the Association of Internet Researchers recommended reserving the acquisition of informed consent to the dissemination stage by asking for informed consent from specific participants before publication of their quotes [ 15 , 16 ]. Furthermore, researchers should take responsibility for information confidentiality and anonymity according to the internet research ethics criteria prepared by the National Committee for Research Ethics in the Social Sciences and the Humanities guidelines, which recommend a basic research ethic norm for the analyses, reports, and evaluations that apply to all research [ 17 ]. Moreover, the National Committee for Research Ethics in the Social Sciences and the Humanities guidelines contain more details about the demand for legal consent and privacy standards imposed by the European Union’s General Data Protection Regulation. The General Data Protection Regulation is a European Union–wide regulation targeting the project of personal data processing. The General Data Protection Regulation defines personal data as any information relating to an identifiable person (data subject), including name, online identification number, location data, and other factors related to personal, physical, physiological, mental, or social identity [ 18 ]. The General Data Protection Regulation recommends using anonymous data and deleting identifiable information to ensure the confidentiality of the data. Consent should be obtained from the individual for use in scientific research [ 18 , 19 ]. The British Psychological Society guideline does not explicitly refer to the internet community but suggests that researchers may consider paraphrasing the verbatim quotes to reduce the risk of being traced or identified in qualitative research [ 20 ]. When paraphrasing, steps must be put into place to ensure that the original meaning of the message is maintained. Currently, there is no widespread consensus on ethical considerations by social media researchers.

Some researchers have tried to explore the reporting of existing ethical considerations in research papers using social media data. For instance, Sinnenberg et al [ 6 ] reported that only 32% and 12% of the papers mentioned acquiring ethical approval and informed consent, respectively, by utilizing multiple analysis methods, including surveillance, intervention, recruitment, engagement, content analysis, and network analysis with Twitter data before 2015. Thereafter, Takats et al [ 21 ] conducted an updated examination based on Sinnenberg et al’s [ 6 ] study. They found that of 367 studies using different methodological approaches, including sentiment mining, surveillance, and thematic exploration of public health research using Twitter data between 2010 to 2019, 17% of the studies included verbatim tweets and identifiable information about the internet users [ 21 ]. Similarly, Lathan et al [ 22 ] reviewed papers, including both qualitative and quantitative methods, by using Facebook data to explore public health issues and reported that only 48% and 10% of the papers obtained ethical approval and informed consent, respectively. Furthermore, in a study on research using YouTube data or comments, Tanner et al [ 23 ] found that only 26.1% of these studies sought ethical approval, only 1 paper (0.08%) sought informed consent, and 27.7% contained identifiable information. These findings indicate widespread neglect of ethical issues such as ethical approval, informed consent, and privacy issues in research papers using social media data.

Our study focuses on the ethical challenges of qualitative studies utilizing social media data. First, social media can be considered as sources for qualitative data collection because of the low cost, vast amount of available sources about health information, and users’ health behaviors, experiences, and attitudes. Second, qualitative research is context-dependent and mainly contains quotations and written content to support the viewpoint. It is acknowledged that quote materials from social media would potentially be traced back to the original posts and threaten the users’ privacy [ 24 ]. This is supported by findings reported by Ayers et al [ 25 ] who found that online searches of verbatim Twitter quotes in journal papers described as “content analyses” or “coded Twitter postings” can be traced back to individual internet users 84% of the time. Furthermore, Lathan et al [ 22 ] identified that 46% of the studies with verbatim or paraphrased quotes could be traced to the original posts in 10 minutes. Therefore, it is essential to investigate the extent to which ethical oversight is reported in qualitative studies using social media data. Moreover, qualitative research often involves personally sensitive data about health conditions and diseases; hence, anonymity and proper deidentification would be more important for researchers [ 26 , 27 ].

Previous studies have reviewed the ethical challenges and methodological use of social media platforms such as Twitter [ 6 , 21 ], Facebook [ 22 ], and YouTube [ 23 ] for health care research in both qualitative and quantitative studies. Although there is plenty of qualitative data pouring into social media such as blogs, Twitter, Facebook, and Weibo, evidence is lacking on the investigation of ethical considerations targeting qualitative data in different software and web-based discussion forums to provide a more comprehensive understanding of the ethical issues. To address the ethical considerations in qualitative research of different internet communities and draw the attention of researchers and publishers to ethical issues, we conducted this study to evaluate the ethical practices and ethical considerations of qualitative studies on health care by using data of internet communities. This review aims to (1) assess the rates of reporting institutional review board (IRB) approval and informed consent in studies focused on mining text in the internet community and social media, (2) compare these rates according to the year of publication, country conducting the research, website included in the study’s analysis, and journal’s guidelines about ethical approval for the type of study, and (3) describe whether the studies used anonymized/deidentified data.

Research Design

We conducted a scoping review to investigate how qualitative research mining social media data handles ethical approval, informed consent, and confidential issues. We performed this study according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The completed PRISMA-ScR checklist is provided in Multimedia Appendix 1 .

Search Strategy

All published qualitative studies from 2010 to March 31, 2023, focusing on mining text from online community and social media sources about health care in the following databases were included in this study: PubMed, Web of Science, CINAHL, Cochrane, and Embase. A standardized search string containing Medical Subject Headings (MeSH) and non-MeSH entry terms was used in the search strategy. In addition, the reference lists of the retrieved papers and citation tracking were manually searched as a supplement to database searches to improve comprehensiveness. Gray literature was also identified through internet searches in Google Scholar and OpenGrey websites. The search strategies are represented in Multimedia Appendix 2 .

Inclusion and Exclusion Criteria

We divided the criteria into 2 parts. First, we limited the inclusion and exclusion criteria used at the title and abstract screening stage eligible for (1) studies mining existing text and posts from the internet community and social media data focusing on health care topics, (2) studies using qualitative methods or available qualitative parts in mixed methods studies to analyze data, and (3) studies only written in English. Ineligible studies were those related to investigating the use and dissemination of social media in health care, using social media or internet community as an intervention tool, and using social media to conduct web-based interviews, surveys, or focus groups. We also excluded studies published as reviews, case studies, conference abstracts, commentaries, policies, guidelines, and recommendations. Second, at the full screening stage, the specific eligible inclusion criteria were studies focused on mining text about health care topics with full-text papers. Studies that did not have the full text after contacting the authors and that were not originally in the English language were excluded.

Study Selection

All results of the searches were entered into the EndNote library, and duplicates were removed. Two researchers reviewed the titles and abstracts based on the inclusion and exclusion criteria independently. Those studies that were irrelevant to the study topic were discarded, and then the full text was screened to select eligible papers. Any disagreements were discussed and resolved by consensus or a third person.

Data Extraction

Data were extracted between April 2023 and May 2023. Two researchers independently read the full text carefully, and the results were extracted using a standardized data extraction spreadsheet, including research type, first author, study objective, sample size, publication time, country where the research was conducted or country of the first author, website or internet community the studies focus on, type of data collected from social media, language of collected posts or data, privacy level of data (public or privacy posts), study design, research results, published journal, and information about the ethical considerations. Disagreements were resolved by consensus of a third person. The information about ethical considerations was analyzed to investigate the rates of reporting ethical approval, informed consent, and privacy issues: whether IRB review was reported (IRB approval, IRB exemption, unnecessary, not mentioned) and the reason for not requiring IRB approval; whether informed consent was obtained from participants or the websites’ administrators, consent types (digitally informed consent or written informed consent, informed consent is not required, consent was waived by IRB), and the methods used to obtain consent in each study; and whether quoting a post in papers could lead to the identification of internet users in each study. The description of users’ posts (verbatim quote, paraphrase) was recorded. We also analyzed if posts were paraphrased to maintain the original meaning, if actions were taken to deidentify the internet users, and if the posts contained other identifying information (ie, usernames, photos, links, hashtags) attached to the post. As every journal would provide publication ethical considerations and requirements, we also searched the submission guidelines and editorial policies of each journal submission website to check whether the journal contained any ethical guidance targeting studies using data from internet community and social media platforms. Additional information was included about the details of ethical approval, informed consent, and privacy, for example, whether individuals can withdraw their quotes if they want to be excluded from the study at any time without any reprisal and whether the quotations were tested for deidentification via search engines. There was excellent agreement on the primary outcome between the 2 researchers (k>.95 for all).

Data Analysis

Data were analyzed using SPSS software (IBM Corp). The chi-square test or Fisher exact tests (when cell size was less than 5) were used to test for differences between the rates of informed consent and ethical approval according to publication year, website, and different countries. All P values were 2-sided, and P values <.05 indicated significance.

Study Selection for the Review

We reviewed 4674 papers after removing the duplicates. After screening the titles, abstracts, and full-texts, we reviewed 108 eligible papers ( Figure 1 ). The full list of the included papers and all the extracted information are incorporated in Multimedia Appendix 3 [ 28 - 135 ]. Of the 108 studies reviewed, 73 (67.6%) were qualitative studies and 35 (32.4%) were mixed methods studies. All papers had text mined from internet communities or social media for qualitative analysis. The sample size ranged from 32 to 392,962. Approximately 82.4% (89/108) of the studies were published after 2018, and there was a sharp increase in the number of studies from 2019 to 2022. Moreover, nearly half of the studies (55/108, 50.9%) were published in the United States. Regarding the websites for mining text, the most widely used social media platform was Twitter (42/108, 38.9%), followed by Facebook (17/108, 15.7%).

Ethics Approval in These Studies

Our results indicated that of the 108 studies, 78 (72.2%) reported ethics approval. Of the 78 studies, 31 (40%) explicitly stated that ethics approval was obtained before the study was undertaken, 33 (42%) reported that the ethics approval was granted through exemptions by the local IRB, and 14 (18%) explicitly demonstrated that approval by the ethics committee was not required because publicly available data were collected from internet communities and social media platforms. However, 30 (27.8%) of the 108 studies did not mention about obtaining IRB approval ( Table 1 ).

Based on our exploration of the ethical guidelines of each journal to determine whether there were ethical requirements for studies mining social media data, only 36.1% (39/108) of the studies were published in journals that required ethical considerations for studies gathering data from social media platforms by using internet and digital technologies. Of the 39 studies published in 19 journals, 27 (69%) were published in the Journal of Medical Internet Research and its sister journals. The submission guidelines of the Journal of Medical Internet Research state that authors of manuscripts describing studies of internet, digital tools, and technologies are required to verify that they have adhered to local, national, regional, and international laws and regulations, and are required to verify that they complied with informed consent guidelines. Moreover, 2 journals also provided a specific requirement, that is, when researchers interact with individuals or obtain privacy information gathered from social media platforms, they should obtain ethics approval prior to conducting the study and informed consent from anyone who could potentially be identified. Surprisingly, there were no significant differences in the ethics approval reportage between journals with ethics approval guidelines and those that did not have ethics guidelines for researchers gathering data from social media platforms ( P =.08). Notably, the rates of reporting ethics approval were different between different countries ( P =.02). However, there were no statistically significant differences between the rates of reporting ethical approval and different websites or publication years (all P >.05) ( Table 2 ).

Informed Consent

Of the 108 studies, 59 (54.7%) showed that they did not include any information about informed consent and 49 (45.3%) mentioned informed consent. Of the 49 studies that mentioned informed consent, 14 (13%) demonstrated that informed consent was waived by local institutional boards, and 21 (19%) reported that informed consent was not required because this information is publicly available in websites or did not involve human participants. We interpreted this as not seeking informed consent. Only 14 (12.9%) of the 108 studies explicitly indicated that informed consent was obtained ( Table 1 ). Among the 14 studies, 2 (14%) only provided a generic statement that informed consent was obtained but did not report the process of how the informed consent was obtained, while 12 (86%) received digital informed consent. Of the 12 studies that reported receiving digital informed consent, 6 reported that they sought permission from the communities’ or groups’ administrators and by posting a statement of the research objective on the group’s wall, while 5 studies contacted the participants privately via email, commenting below the posts and software to gain consent, and 1 study reported that it had sent a digital version of the informed consent book. Furthermore, among the studies that had obtained informed consent, 7 studies included the statement that the individuals’ posts would be removed if they wanted to be excluded from the study, and they could withdraw from the study whenever they wanted. In addition, the rates of reporting informed consent showed no statistical significance between publication years, different countries, and different websites (all P >.05) ( Table 2 ).

Confidentiality of the Information

All data sources were obtained from anonymous websites or communities, and the majority (104/108, 96.3%) of the data sources did not contain usernames. Notably, only 3.7% (4/108) of the studies contained the participants’ usernames or pseudonyms. One study reported that pseudonyms like Sasha had been used instead of the real name. The other 3 studies contained the expression for usernames but did not state whether pseudonyms were used. Except for 9 studies that used nonnative language quotes and 3 studies that were transcribed into text via video, among the 108 included studies, 76 (70.3%) quoted at least one native language post in their reports. Additionally, 20 studies presenting aggregated analysis or composite accounts did not include any quotation or written content. Of the 76 studies containing internet users’ written content, 52 (68%) contained just verbatim-quoted participants’ posts and 24 (32%) contained paraphrased posts ( Table 1 ). Among the 52 studies containing direct and verbatim quotations, which are likely to be traced to the original posts from users, only 17 (33%) studies took measures to deidentify the users. The 17 studies mentioned that all names or usernames were removed and personal identifying information was removed to maintain privacy, while 42% (22/52) of the studies did not mention any measures that were taken to deidentify the users and maintain confidentiality. Approximately 32% (24/76) of the studies described that they paraphrased posts and removed any explicitly identified personal information to maintain confidentiality to reduce the likelihood of users being identified via search engines. Of the 24 studies, 20 (83%) reported that the quotations were slightly modified or summarized for readability, the symbol information was removed using “…”, and key identifiable information was removed to protect privacy while maintaining the meaning of posts. Four of the 24 (17%) studies did not report the methods and details of paraphrasing. Notably, only 3% (2/76) of the studies containing users’ written content showed that researchers intentionally entered each quote into search engines to ensure that every quote did not lead to the original posts. Moreover, of the 76 studies containing written content, 62 (82%) did not contain other types of identity information attached to the posts, while 14 (18%) included other identifying data (hashtags, emojis, geolocation, photos, links, screenshots) attached to the original posts for analysis ( Table 1 ). Of the 14 studies including other identifying information, 4 (29%) contained photos and screenshots associated with the website pages. Of the 52 studies that disclosed verbatim quotes and other identifiable information, 26 (46%) studies reported informed consent consideration, and only 8 (15%) obtained explicitly informed consent. Additionally, of the 77% (40/52) of the studies that mentioned IRB or ethical review, 38% (15/40) received IRB approval, and 63% (25/40) of the studies were granted exemption. The proportion of reporting ethical approval in studies containing users’ written content was modestly higher than that in studies not containing users’ written content (60/76, 79% vs 14/32, 44%; P <.001) ( Table 2 ).

Principal Findings and Comparison to Prior Work

In this scoping review, we included 108 studies ( Multimedia Appendix 3 ; [ 28 - 135 ]) that focused on mining text from internet community and social media data for health care research, and we reviewed the ethical consideration reportage and outcome reports in these studies. We found that the rates of reporting IRB approval and informed consent in qualitative research on health care utilizing social media data were 59.3% (64/108) and 12.9% (14/108), respectively. Our findings demonstrate that the key ethical considerations for qualitative research in online communities are insufficiently discussed and described. However, the reporting rates of ethical considerations in the papers in our scoping review were much higher than those reported in systematic reviews including multiple analysis methodologies on only 1 social media platform. For example, ethics approval and informed consent were reported in 48% and 10% of research studies using only Facebook data [ 22 ], 32% and 0% from 2006 to 2019 [ 21 ], 40% and 0.9% (only 1 paper) from 2015 to 2016 in public health research using only Twitter data [ 25 ], and 26.1% and 0.8% (only 1 paper) in researches incorporating only YouTube data [ 23 ], respectively. In fact, previous studies were limited to only a few selected websites such as Twitter, Facebook, and YouTube. There is a lack of research that incorporates a variety of different social media data for comparisons. Differences in the reporting of ethical considerations may be attributed to the different methodologies adopted by studies. For example, Lathan et al [ 22 ] analyzed the ethical considerations in studies including predictive or model development, while our research focuses on the ethical considerations in qualitative studies.

Importantly, our findings indicate that there is a need to develop a standardized and apparent approach for the reporting of ethical considerations in qualitative research of data from social media and online communities. Our research demonstrates that the rates of reporting ethics approval are different in different countries ( P =.02). Specifically, a wide variety of national research ethics governing bodies and over 1000 laws, regulations, and standards provide oversight for human subjects research in 130 countries. Obviously, a guideline is needed for best ethical practices for qualitative research involving posts from social media platforms. Surprisingly, there were no significant differences between the rates of reporting ethical approval and those of journals specifying ethical requirements for studies involving text mining ( P =.08). This inconsistent result of publication guidelines and reports of ethical approval consent is similar to previous findings on the ethical standards in COVID-19 human studies [ 136 ]. Although there are journal publication guidelines for studies mining social media data, the reports of ethical approval and consent in the papers published in such journals do not exactly follow the guidelines. Consequently, this finding indicates that more ethical awareness is needed among researchers, editors, and reviewers for qualitative studies on data mining.

Besides the different legal and regulations in different countries, the inconsistency in the ethics approval in published papers may be because social media research is a highly interdisciplinary science, and computer science researchers may be less experienced or may pay less attention to the key ethical issues of protecting human subjects [ 137 ]. Medical and health science researchers may have considered some ethical concerns about gathering social media data but they may not be familiar with the relevant guidelines. For example, the Association of Internet Researchers has a detailed ethical guideline targeting social scientists conducting digital research, while it may be less popular and less well-known among medical and health care researchers. At the institute level, Ferretti et al [ 138 ] noticed that institutionalized review committees, especially the individual IRB institutes for universities and health care systems lack knowledge about the methodology, text mining technical standards, data security, and ethical harms for studies using big data and social media as sources. Because of this lack of knowledge, institutional ethics committees may have inconsistent ethical criteria and perspectives about web-based projects using social media data [ 139 ]. Therefore, some ethics review committees exclude research on internet communities from ethical oversights because their ethics standards are confined only to medical fields. Above all, it is additionally challenging for ethical approval institutions because of the continuous development and dynamic change of studies using social media data. Furthermore, it is necessary for ethics committee members to be trained about the ethical issues in studies mining text from social media. Inviting interdisciplinary researchers to join in the approval process would be an appropriate method to increase the awareness of ethical considerations [ 140 , 141 ].

Interestingly, the reporting rate of obtaining informed consent for mining social media data in qualitative studies was unexpected. The most influential ethical reports such as the Nuremberg Code [ 142 ], Declaration of Helsinki [ 143 ], and the Belmont Report [ 144 ] have demonstrated the principle of informed consent in research involving humans. Our review shows that only 12.9% (14/108) of the studies explicitly obtained informed consent and 32.4% (35/108) of the studies reported that informed consent was exempted by IRB or was not required, as the information was available publicly in websites or did not involve human participants. Our results are similar to those of Wongkoblap et al [ 145 ] who reported that only 16.7% of the studies received informed consent from participants prior to data analysis on data mining of social network data on mental health disorders.

There are multiple reasons for the challenges in obtaining informed consent in an internet setting. First, it is impractical for researchers to gain individual informed consent from a large number of users in an internet community [ 146 ]. Second, members of ethics review boards lack consensus about the need for informed consent from an internet community for qualitative research under the current legal definition [ 147 ]. Moreover, there has been a debate on the criteria of human subject research in using social media data. The federal regulation recommends that if data in the studies are obtained from public social media websites, where data are identifiable and do not require interaction with individuals, such studies do not constitute human subject research, while studies involving the identification of private information or interaction with the individual can be considered as human subject research [ 148 ]. In contrast, some researchers believe that social media and big data research are not ethically exceptional and should be treated in the same manner and with the same rules as those for traditional forms of research [ 149 ]. There is ambiguity as to what is appropriate or should be standard practice for obtaining informed consent.

Currently, it is challenging to maintain privacy and protect the traceability of individuals posting content in the internet community. Our findings indicated that 70.3% (76/108) of the studies contained internet users’ written content, of which 68% (52/76) included verbatim quotations of users’ posts that could lead to identification, and 18% (14/76) of the studies included other identifiable information such as links, screenshots, and emojis linked to original posts, which are similar to the findings of Ayers et al [ 25 ] and Lathan et al [ 22 ]. Usha Lawrance et al [ 150 ] and Wilkinson and Thelwall [ 151 ] argued that using direct quotes to support findings would lead to the identification of users and breach users’ confidentiality in internet community data. Moreover, quoting social media posts or disclosing usernames violate the International Committee of Medical Journal Editors’ ethics standards, which state that identifying information such as written descriptions and photos should not be published unless the information is essential for scientific purposes and the participants give written informed consent for publication [ 152 ]. Furthermore, our study demonstrates that the proportion of studies containing users’ written content (both direct and paraphrased quotations) is higher than that of studies that do not include any quotation or written content (60/76, 79% vs 14/32, 44%; P <.001)——a tentative explanation is that some researchers realized that ethical reportage should be stricter for qualitative papers with quotations from social media posts due to privacy and security issues. This is supported by Boyd and Crawford [ 153 ] who stated that rigorous thinking about the process of mining and anonymizing big data is required for ethics boards to ensure that people are protected. Our findings show that 32% (24/76) of the studies intentionally paraphrased the quotes to ensure that users could not locate them, and 20 studies used aggregated data interconnected with anonymity. Moreover, it is recommended by Wilkinson and Thelwall [ 151 ], Bond et al [ 154 ], and Markham et al [ 155 ] that researchers should not directly quote and work with aggregate data sets and separate texts from their original context, which is more acceptable to participants. In addition, the British Psychological Society guidelines recommend that researchers consider paraphrasing any verbatim quotes to reduce the risk of these being traced to the source [ 20 ]. Notably, 13 of the 25 papers in this study showed that they did not report the precautions taken for paraphrasing. This may be due to the lack of detailed methodology and consensus about paraphrasing quotes to reduce bias and maintaining the original meaning.

Limitations and Strengths

Our scoping review has several limitations. First, our research was limited to qualitative studies and the qualitative parts in mixed methods studies on text mining from social media, and it is unclear whether ethical considerations are critical in quantitative studies among internet communities. Second, we were restricted to studies published in English language and those with the full text available, and therefore, we could be underestimating the number of relevant papers published in other languages. Third, the rates of reporting ethical approval, informed consent, and privacy of this research relied on self-reported data. Thus, it is possible that although certain studies did not report the process of ethical considerations, such considerations may have been followed during the research. Conversely, some studies may have mentioned about the ethical considerations but may not have conducted them in practice. Hence, there is a bias because of the lack of accurate documentation that must be considered.

Social media text mining can be a useful tool for researchers to understand patient experiences of health conditions and health care. However, as illustrated by the absence of ethical discourse in publications, our analysis indicates significant gaps in the ethical considerations and governance of qualitative research of internet posts. Therefore, a complete and consistent consensus guideline of ethical considerations in qualitative research of internet posts is needed to protect users’ data. With the continued advancing development of text-mining techniques, qualitative studies mining text from social media should be more cautious while using user quotations to maintain user privacy and protect the traceability of the internet users posting content. We suggest that authors should report their results by using aggregated findings or deidentified ways like paraphrasing instead of verbatim quotations, which can prevent internet users from being identified through search engines. In addition, authors should provide more detailed information about the precautions taken for obtaining informed consent and paraphrasing to reduce the potential bias. Furthermore, journals and editors should pay more attention to the reporting standards of ethical consideration and privacy issues in qualitative research involving social media data.

Acknowledgments

This project was funded by the National Natural Science Foundation of China (72304131) and the Outstanding Youths Development Scheme of Nanfang Hospital, Southern Medical University (2023J005). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of this manuscript. We sincerely thank the funders of this study.

Data Availability

All data extracted and analyzed during this study are presented in this paper and in the multimedia appendices.

Authors' Contributions

YW was responsible for the protocol of the research and redrafted the paper critically. YZ and JF performed literature searches. YZ, JL, and WC performed study identification and screening. ZG, SD, CZ, and JT extracted and analyzed the data from the included journals. YZ and JL wrote the first draft of the paper. All authors read and approved the final manuscript.

Conflicts of Interest

None declared.

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist.

Search strategy for each database.

Summary of included literature.

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Abbreviations

Edited by T Leung, T de Azevedo Cardoso; submitted 02.08.23; peer-reviewed by E Zibrowski, J Scheibner; comments to author 06.10.23; revised version received 29.11.23; accepted 16.04.24; published 17.05.24.

©Yujie Zhang, Jiaqi Fu, Jie Lai, Shisi Deng, Zihan Guo, Chuhan Zhong, Jianyao Tang, Wenqiong Cao, Yanni Wu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

ORIGINAL RESEARCH article

Association between weight-adjusted-waist index (wwi) and cognitive decline in us elderly participants provisionally accepted.

• 1 Changchun University of Chinese Medicine, 李佳欣, China
• 2 Changchun University of Chinese Medicine, China

The final, formatted version of the article will be published soon.

Objective: To investigate the association between the weight-adjusted-waist index (WWI) and cognitive decline in elderly Americans from 2011 to 2014. Methods: A cross-sectional study was conducted on 2,762 elderly participants from the National Health and Nutrition Examination (NHANES) between 2011 and 2014. WWI was calculated by dividing waist circumference (cm) by the square root of body weight (kg). Participants assessed their cognitive functions using tests such as the DSST, AFT, and CERAD W-L. In this research, multiple logistic regression, HIA, limited cubic spline (RCS), and threshold effect analysis methods were utilized to explore the relationship between cognitive decline and WWI. Results: The study involved 2,762 participants aged 60 years and older, comprising 1,353 males (49%) and 1,409 females (51%), with a median age of 69.3 years (standard deviation = 6.7). The analysis revealed that the risk of cognitive decline was positively associated with the WWI. Fully adjusted models indicated significant correlations with the CERAD W-L (odds ratio [OR] = 1.24, 95% confidence interval [CI] = 1.06–1.46, P 0.05). A J-shaped relationship between WWI and low DSST scores was observed using multivariate restricted cubic spline (RCS) regression (P for non-linearity Conclusions: Our data have shown a significant positive association between the WWI and a higher risk of cognitive decline in older Americans, with a J-shaped non-linear relationship between WWI and DSST. In addition, our findings indicate that WWI was associated with greater cognitive decline than other markers of obesity.

Keywords: cognitive, Obesity, weight-adjusted-waist index (WWI), NHANES, Cross-sectional study

Received: 23 Feb 2024; Accepted: 16 May 2024.

Copyright: © 2024 Li, Sun, Zhang, Zhang and ZHOU. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jiaxin Li, 李佳欣, Changchun University of Chinese Medicine, 中国长春, China Mx. Liya ZHOU, Changchun University of Chinese Medicine, Changchun, 130117, Jilin Province, China

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Creating Good Relationships: Responsiveness, Relationship Quality, and Interpersonal Goals

Amy canevello.

Research Center for Group Dynamics, Institute for Social Research, University of Michigan

Jennifer Crocker

Research Center for Group Dynamics, Institute for Social Research, and Department of Psychology, University of Michigan

Perceived partner responsiveness is a core feature of close, satisfying relationships. But how does responsiveness originate? Can people create relationships characterized by high responsiveness, and consequently, higher quality relationships? We suggest that goals contribute to cycles of responsiveness between people, improving both people’s relationship quality. The present studies examine 1) how interpersonal goals initiate responsiveness processes in close relationships, 2) the self-perpetuating nature of these processes, and 3) how responsiveness evolves dynamically over time through both intrapersonal projection and reciprocal interpersonal relationship processes. In a semester-long study of 115 roommate dyads, actors’ compassionate and self-image goals predicted a cycle of responsiveness between roommates, occurring within weeks and across the semester. In a 3-week study of 65 roommate dyads, actors’ goals again predicted cycles of responsiveness between roommates, which then contributed to both actors’ and partners’ relationship quality. Results suggest that both projection and reciprocation of responsiveness associated with compassionate goals create upward spirals of responsiveness that ultimately enhance relationship quality for both people.

High quality close relationships contribute to mental and physical well-being; poor quality close relationships create stress and undermine health and well-being (e.g., Baumeister & Leary, 1995 ; Uchino, Cacioppo, & Kiecolt-Glaser, 1996 ). Relationship quality depends on beliefs about a relationship partner’s responsiveness--that is, on the perception that a partner understands, values, and supports important aspects of the self. People who perceive their relationship partners as responsive feel close, satisfied, and committed to those relationships ( Reis, Clark, & Holmes, 2004 ).

The present studies focus on the dynamic of responsiveness in dyadic relationships -- relationship processes that promote or undermine reciprocation of responsiveness between relationship partners, affecting both partners’ relationship quality over time. We suggest that people’s interpersonal goals for their relationships, that is their compassionate goals to support others and their self-image goals to create and maintain desired self-images ( Crocker & Canevello, 2008 ), predict positive and negative responsiveness dynamics respectively, changing both people’s relationship quality. In this way, people can create responsive, high-quality relationships for themselves and others.

Responsiveness in Relationships

Responsive relationship partners convey understanding, validation, and caring ( Gable & Reis, 2006 ). They are warm, sensitive to their partners’ feelings, and want to make their partners feel comfortable, valued, listened to, and understood.

Existing theory and research on responsiveness suggests that people’s responsiveness to partners contributes to both their own and partners’ perceptions of responsiveness in the relationship. Lemay and colleagues ( Lemay & Clark, 2008 ; Lemay, Clark, & Feeney, 2007 ) found that people contribute to their own experiences of responsiveness in close relationships; when people report being responsive to relationship partners, they project their responsiveness onto partners and perceive partners as more responsive. Other researchers characterize responsiveness as a transactional process between relationship partners. Reis and Shaver (1988) hypothesize that close relationships develop through an interpersonal process in which actors’ reactions to partners influence partners’ perceptions of actors’ responsiveness. Importantly, Reis and Shaver speculate that goals, motives, needs, and fears of both relationship partners contribute to and result from responsiveness in the relationship. That is, goals and motives predict people’s relationship behaviors and how they interpret partners’ behaviors, which in turn, feed back to predict goals and motives.

The present studies examine both intrapersonal and interpersonal processes of responsiveness and contribute to the responsiveness literature in three important ways. First, as suggested by Reis and Shaver (1988) , interpersonal goals should predict responsiveness processes in close relationships. However, no research that we know of explicitly examines the motivational underpinnings of responsiveness, whether based on projection or reciprocation. We propose that actors’ compassionate goals to support others and self-image goals to construct and maintain desired self-images shape their responsiveness to relationship partners. Through projection, actors’ responsiveness affects their perceptions of their partners’ responsiveness, and hence their own relationship experiences. Through partners’ perceptions and reciprocation of actors’ responsiveness, actors’ responsiveness affects both actors’ and partners’ relationship experiences. We suggest that because projection is an intrapersonal processes and the exchange of responsiveness between relationship partners is an interpersonal process, both can occur simultaneously. That is, people can project their responsiveness onto others, affecting their own relationship experiences, and at the same time, convey responsiveness to relationship partners, affecting partners’ relationship experiences.

Second, projected and reciprocated responsiveness can become self-perpetuating: relationship goals promote or undermine projection and reciprocation of responsiveness, which reinforce both people’s subsequent relationship goals. Thus, through their interpersonal goals, people can create responsive, high-quality relationships for themselves and others and contribute to both people’s goals for the relationship.

Third, to our knowledge, the present studies are the first to examine both immediate and long-term intra- and interpersonal responsiveness dynamics and resulting relationship outcomes as they evolve over time. Previous research suggests that these processes should occur quickly within relationships, guiding people’s relationship experiences and goals in the moment (e.g., Laurenceau, Barrett, & Pietromonaco, 1998 ; Lemay et al., 2007 ). We hypothesize that the effects of compassionate and self-image goals and responsiveness extend over time, predicting change in people’s relationship experiences and goals from day to day and week to week, and that chronic interpersonal goals predict long-term changes in relationship experiences and interpersonal goals over weeks and months. Thus, we propose that projection of responsiveness and reciprocation of responsiveness dynamically affect short-term fluctuations and long-term changes in relationship outcomes.

Figure 1 illustrates our general model in a relationship between an actor (A) and a relationship partner (P). We highlight intra- and interpersonal aspects of the model and detail them below.

Hypothesized theoretical model of interpersonal goals, responsiveness, and relationship quality.

Intrapersonal Process: A’s Compassionate and Self-Image Goals and Responsiveness Predict A’s Relationship Experience

We hypothesize an intrapersonal model examining how people’s goals contribute to their own experiences of responsiveness and resulting relationship goals and quality. Our model extends the intrapersonal projection of responsiveness described by Lemay and colleagues ( Lemay & Clark, 2008 ; Lemay et al., 2007 ), by showing how actors’ goals can be the starting point for change in their responsiveness to partners, which is projected onto partners and leads to change in actors’ goals and relationship outcomes. Paths A–E in Figure 1 show our hypothesized intrapersonal model of goals and responsiveness: A’s interpersonal goals predict change in A’s responsiveness (Path A), which predicts change in A’s perceptions of P’s responsiveness (Path B), with consequences for change in A’s subsequent responsiveness (Path C), goals (Path D), and relationship quality (Path E).

Paths G–K of Figure 1 are a mirror image of the intrapersonal processes in paths A–E, but for partners rather than actors: P’s compassionate goals predict P’s increased and self-image goals predict P’s decreased responsiveness to A (Path G). P’s responsiveness to A predicts P’s increased perceptions of A’s responsiveness (Path H), which then leads to P’s increased responsiveness (Path I), increased compassionate and decreased self-image goals (Path J), and increased relationship quality (Path K).

Below, we present the rationale for each path in the intrapersonal model.

Path A: A’s compassionate and self-image goals predict change in A’s responsiveness

We propose that two types of relationship goals shape responsiveness to relationship partners. Self-image goals focus on constructing, maintaining, and defending desired public and private images of the self ( Crocker & Canevello, 2008 ). When people have self-image goals, they care about what others think of them, but not what others need; consequently they should be less responsive. Compassionate goals focus on supporting others, not to obtain something for the self, but out of concern for others’ well-being ( Crocker & Canevello, 2008 ). When people have compassionate goals, they want to be a constructive force in their interactions with others, and avoid harming them. We suggest that when people have compassionate goals they are more responsive, because they care about others’ well-being. 1

People with chronically high compassionate goals report greater private self-consciousness, lower psychological entitlement, believe that it is possible for both people in a relationship to have their needs met, and believe that it is important that people look out for one another; they trust in and feel closer to others and report both giving and receiving more social support ( Crocker & Canevello, 2008 ). These findings suggest that when people have compassionate goals they understand and trust that when they are responsive to others, they create an environment in which others will respond to them. In contrast, people with chronically high self-image goals report higher psychological entitlement, believe that good outcomes for one person come at the expense of others, and feel that it is important to look out for themselves, even at the expense of others; they report higher loneliness, more conflict with others, and lower interpersonal trust ( Crocker & Canevello, 2008 ). These findings suggest that when people have self-image goals they feel a sense of scarcity and fear that their needs will not be met in collaboration with others. Based on these findings, we propose Path A: When A has the goal to care for and support P, A will become more responsive to P, whereas when A has the goal to create and maintain desired self-images, A will become less responsive to P.

Path B: A’s responsiveness to P predicts A’s increased perceptions of P’s responsiveness

When actors believe they are responsive to partners, they project their own responsiveness onto partners and perceive partners as more responsive ( Kenny & Acitelli, 2001 ; Lemay & Clark, 2008 ; Lemay et al., 2007 ). Several factors might moderate this association. For example, actors who have low self-esteem might feel their partners do not value them and perceive their partners as unresponsive ( Murray, Griffin, Rose, & Bellavia, 2003 ). Also, because of their over-involvement with others and self-neglect, actors high in unmitigated communion might want to see themselves as self-sacrificing and see their partners as unresponsive ( Helgeson & Fritz, 1998 ). However, despite these specific circumstances, in general we expect a strong association between responsiveness and perceptions of partners’ responsiveness. These considerations lead us to propose Path B: A’s responsiveness to P predicts A’s increased perceptions of P’s responsiveness.

Path C: A’s perception of P’s responsiveness predicts A’s increased responsiveness to P

When actors perceive their partners as responsive, they are more responsive in return; when they perceive their partners as unresponsive, actors are less responsive in return ( Fruzzetti, Jacobson, & Blechman, 1990 ; Gable & Reis, 2006 ; Patterson, 1976 ; Plickert, Côté, & Wellman, 2007 ). This may happen for several reasons. People may reciprocate responsiveness out of caring. Actors’ responsiveness strengthens partners’ social bonds to actors, including feelings of caring, connection, and trust, leading partners to want to be responsive to actors (e.g., Brown & Brown, 2006 ; Clark, Fitness, & Brissette, 2004 ). In established communal relationships, partners experience more positive mood and less negative mood when they reciprocate support to actors, compared to when they do not ( Gleason, Iida, Bolger, & Shrout, 2003 ). Even in new relationships, reciprocity of responsiveness may be the result of social exchange norms in which both partners focus on an equal exchange of responsiveness ( Clark & Mills, 1993 ; Mills & Clark, 1982 ). Given this evidence, we propose Path C: A’s perception of P’s responsiveness predicts A’s increased responsiveness to P.

Path D: A’s perception of P’s responsiveness predicts change in A’s compassionate and self-image goals

Actors’ perceptions of partners’ responsiveness should shape actors’ compassionate and self-image goals toward the partner. Actors who perceive partners as responsive feel validated, understood, and cared for ( Reis et al., 2004 ), which fosters a sense of security and permits a shift in focus from protecting the self to supporting others ( Mikulincer, Shaver, Gillath, & Nitzberg, 2005 ; Murray, Holmes, & Collins, 2006 ). In other words, actors’ feelings that partners are responsive to them should foster compassionate goals for partners. Unresponsiveness, on the other hand, conveys a partners’ lack of interest in or concern for actors. Perceptions of partners’ unresponsiveness may signal to actors that they should protect themselves from uncaring partners ( Clark & Monin, 2006 ; Murray et al., 2003 ; Murray, Rose, Bellavia, Holmes, & Kusche, 2002 ) and, as a result, actors should increase in self-image goals. These considerations lead us to propose Path D: A’s perception of P’s responsiveness predicts A’s increased compassionate and decreased self-image goals.

Path E: A’s perception of P’s responsiveness predicts A’s increased relationship quality

Perceived partner responsiveness is crucial to relationship quality ( Clark & Mills, 1993 ; Laurenceau et al., 2004 ; see Reis et al., 2004 for a review). Actors who believe that partners are responsive feel closer, more intimate, and more satisfied with their relationships ( Berg & Archer, 1982 ; Collins & Feeney, 2000 ; Cutrona, Shaffer, Wesner, & Gardner, 2007 ; Davis, 1982 ; Laurenceau et al., 1998 ; Lemay et al., 2007 ). When actors perceive partners as unresponsive, they experience decreased satisfaction, commitment, and closeness in those relationships ( Fincham & Beach, 1999 ; Gottman & Levenson, 1992 ). Consequently, we predict Path E: A’s perception of P’s responsiveness predicts A’s increased relationship quality.

Interpersonal Process: A’s compassionate and self-image goals and responsiveness lead to P’s relationship experience and goals

In addition to this purely intrapersonal process, we hypothesize an interpersonal model in which people’s goals and responsiveness contribute to relationship partners’ experience of actors’ responsiveness, leading to reciprocation of responsiveness and resulting relationship goals and quality. We draw from previous theory and research suggesting that responsiveness is a dyadic process whereby partners perceive actors’ responsiveness and respond in turn (e.g., Collins & Feeney, 2000 ; Reis & Shaver, 1988 ). We hypothesize that actors’ goals can also be the starting point for creating responsiveness dynamics between relationship partners, with consequences for partners’ responsiveness to actors, goals, and relationship quality. Paths A, F, I, J, and K in Figure 1 depict our interpersonal model, in which A’s goals predict change in A’s responsiveness to partners (Path A), which predicts change in P’s perceptions of A’s responsiveness (Path F), with consequences for change in P’s subsequent responsiveness (Path I), goals (Path J), and relationship quality (Path K).

Paths G, L, C, D, and E of Figure 1 are a mirror image of the interpersonal processes in Paths A, F, I, J, and K, but show effects of partners’ goals and responsiveness on change in actors’ relationship experiences: P’s compassionate and self-image goals predict change in P’s responsiveness to A (Path G). P’s responsiveness to A predicts A’s increased perceptions of P’s responsiveness (Path L), which then leads to A’s increased responsiveness, increased compassionate and decreased self-image goals, and increased relationship quality (Paths C, D, and E).

As described previously in our rationale for the intrapersonal model, we propose Path A: that A’s interpersonal goals predict change in A’s responsiveness to P.

Path F: A’s responsiveness predicts P’s increased perceptions of A’s responsiveness

Relationship researchers assume that partners’ perceptions of actors have some basis in actors’ behaviors ( Kelley et al., 1983 ). Most theories of interpersonal relationships assume that actors’ responsiveness to partners predicts partners’ perceptions of actors’ responsiveness (e.g. Bowlby, 1969 ; Gable & Reis, 2006 ; Kelley & Thibaut, 1978 ; I. G. Sarason, Pierce, & Sarason, 1990 ); empirical research supports this prediction ( Abbey, Andrews, & Halman, 1995 ; Bolger, Zuckerman, & Kessler, 2000 ; Collins & Feeney, 2000 ; Feeney & Collins, 2003 ; Lemay & Clark, 2008 ; Vinokur, Schul, & Caplan, 1987 ). For example, in romantic couples when actors disclosed a stressful problem to partners, partners’ reports of their own responsiveness (i.e., responsiveness, listening, understanding, not criticizing, giving support, and expressing concern) positively predicted actors’ perceptions of partners’ responsiveness ( Collins & Feeney, 2000 ). Consequently, we propose Path F: A’s responsiveness to P predicts P’s increased perceptions of A’s responsiveness.

Paths I, J, and K: P’s perceptions of A’s responsiveness and change in P’s relationship experience

Using the same rationale to describe Paths C, D, and E previously, we propose Paths I, J, and K, respectively: P’s perceptions of A’s responsiveness has consequences for P’s increased responsiveness (Path I), increased compassionate and decreased self-image goals (Path J) and increased relationship quality (Path K).

Overview of Present Studies

In two studies of first-semester college freshman roommates, we tested 1) how interpersonal goals initiate projection and reciprocal responsiveness in close relationships, 2) the self-perpetuating nature of these processes, and 3) how responsiveness evolves dynamically over time through both intrapersonal projection and reciprocal interpersonal relationship processes. First semester college students provide an interesting population for examining these processes. Roommates in these samples did not know each other before living together, so their relationships are relatively unbiased by relationship history and past interactions. Unlike most close relationships, previously unacquainted roommates do not self-select into the relationship. At the same time, many first-year students experience significant disruption of their social lives. When they move away from home to attend college, they must build a social network. Their roommates are often the first people they meet and with whom they spend significant time.

Study 1 tested whether students’ compassionate and self-image goals predict a cycle of projected and reciprocal responsiveness between roommates with implications for both people’s relationship goals. Study 2 reports previously unpublished data from the Roommate Goals Study ( Crocker & Canevello, 2008 , Study 2), examining the implications of these processes for both roommates’ relationship quality.

College roommates completed pretest, posttest, and 10 weekly questionnaires, each including measures of compassionate and self-image goals, responsiveness to roommates, and perceived roommates’ responsiveness. We tested associations between students’ goals and 1) the intrapersonal process predicting their own experiences of responsiveness, and 2) the interpersonal process predicting their roommates’ experiences of responsiveness.

We tested a number of alternative explanations and moderators of these processes in Study 1. First, self-disclosure elicits responsiveness from others (e.g., Greene, Derlega, Mathews, Vangelisti, & Perlman, 2006 ; Reis & Patrick, 1996 ; Reis & Shaver, 1988 ). Associations between goals and responsiveness to roommates could be due to perceptions of roommates’ disclosure, and associations between responsiveness to roommates and perceptions of roommates’ responsiveness could be due to disclosure to roommates.

Second, we sought to distinguish responsiveness from social support. Previous research shows that compassionate and self-image goals predict change in perceived available support and supportive behaviors ( Crocker & Canevello, 2008 ). The present studies focus on responsiveness, which we hypothesize is a specific type of support. Support is often broadly defined, including perceptions of support availability and frequency of supportive behaviors (B. R. Sarason, Shearin, Pierce, & Sarason, 1987 ) and includes structural (e.g., group membership or family relationships) and functional components (e.g., providing tangible or emotional support) ( Uchino, 2004 ). Responsiveness refers to people’s sensitivity to partners and desires that partners feel valued, listened to, and understood. Researchers differ in how they view the relation between responsiveness and support; some argue that support is a component of responsiveness (e.g., Reis et al., 2004 ); others conceptualize responsiveness as a subset of social support, distinguishing between responsive and unresponsive support (e.g., Collins & Feeney, in press ). Regardless, researchers agree that responsiveness and support are distinct but related constructs; support providers may not be perceived as responsive. We tested whether support made available to roommates and perceived available social support from roommates explained the effects of responsiveness to roommates and perceptions of roommates’ responsiveness, respectively.

Third, we examined whether negative mood accounts for or moderates the hypothesized associations. For example, the association between interpersonal goals and responsiveness to others might be spurious, if both are associated with anxious or depressed feelings. Feeling anxious or depressed might also moderate these associations. For example, the relation between responsiveness to roommates and perceptions of roommates’ responsiveness may be particularly strong when people do not feel anxious or depressed.

We controlled for students’ self-disclosure to their roommates and their perceptions of their roommates’ disclosure, social support made available to and perceived available support from roommates, and anxiety and depression to rule them out as alternative explanations.

Participants

One hundred fifteen first-semester same-sex freshmen roommate dyads at a large Midwestern university who did not know each other prior to college volunteered for a study of goals and roommate relationships during the fall semester. Via advertisements in the campus newspaper and flyers, we offered each roommate $60 for completing 12 surveys over 10 weeks ($10 for each the pretest and posttest and $4 for each weekly survey) plus a $40 bonus for completing all 12 surveys. One hundred nine pairs (95%) completed the pretest, posttest, and at least 8 weekly surveys. Although 6 pairs completed fewer parts of the study, we retained all data for analyses where possible. 2 Eighty-six pairs (75%) were female. Seventy-five percent of participants reported their race as White or European-American, 2% as Black or African-American, 15% as Asian or Asian-American, and 8% selected other. The racial composition of the sample closely approximated the racial composition of the incoming freshman class. Participants ranged in age from 18 to 21 years ( M = 18.1 years, SD = .36).

In groups of 1 to 8, roommate pairs attended a 1.5 hour session to learn about the study, give their consent, complete the pretest survey, and receive instructions for completing the remaining 11 surveys. All surveys were administered using UM Lessons software. After completing the pretest survey, participants were instructed to complete the 10 weekly online surveys in privacy and not to discuss their responses with each other. The weekly surveys took about 30 minutes to complete and roommates were required to complete weekly surveys within no less than 48 hours of each other. To retain as many participants as possible in the study, participants were given up to 11 weeks to complete the 10 weekly surveys. 3 Once roommates had completed 10 weekly surveys, they completed the posttest survey and were paid for their participation.

Participants completed measures of compassionate and self-image goals, responsiveness to roommates, perceptions of roommates’ responsiveness, disclosure to and from roommates, support made available to roommates, available support from roommates, anxiety, and depression at pretest, posttest, and weekly. At pretest, participants completed questions about demographics (gender, race/ethnicity, age, parental income). Additional measures not germane to the goals of the present investigation were also included.

Self-image and compassionate goals for participants’ relationships with their roommates were measured using a modified measure from Crocker & Canevello (2008) . Pretest and posttest items began with the phrase, “In my relationship with my roommate, I want/try to." Weekly items began with “This week, in my relationship with my roommate, I wanted/tried to.” All items were rated on a scale ranging from 1 ( not at all ) to 5 ( extremely ). Eight items assessed compassionate goals: “be supportive of my roommate;” "have compassion for my roommate's mistakes and weaknesses;" "be aware of the impact my behavior might have on my roommate's feelings;" “make a positive difference in my roommate’s life;” "avoid neglecting my relationship with my roommate;" "avoid being selfish or self-centered;" "be constructive in my comments to my roommate;" and "avoid doing things that aren’t helpful to me or my roommate." Six items reflected self-image goals, including "avoid showing my weaknesses;" “avoid revealing my shortcomings or vulnerabilities;” "avoid the possibility of being wrong;" "convince my roommate that I am right;" "get my roommate to do things my way;" and "avoid being blamed or criticized." Both scales had high internal consistency at pretest (self-image α = .79; compassionate α = .75), posttest (self-image α = .87; compassionate α = .94), and across participants and weeks (self-image goals: .83 < α < .91, M α = .88; compassionate goals: .85 < α < .94, M α = .91).

Responsiveness to roommates and perceptions of roommates’ responsiveness were measured with a 6-item modified version of a responsiveness measure used in previous research ( Cutrona, Hessling, & Suhr, 1997 ; Gore, Cross, & Morris, 2006 ). Participants indicated how they acted toward their roommate in general at pretest and posttest. All items were rated on a scale from 1 ( not at all ) to 5 ( very much ). Items included “I try to make my roommate feel comfortable about him/herself and how he/she feels;” "I try to make my roommate feel valued as a person;" "I try to be sensitive to my roommate’s feelings;" “I really try to understand my roommate’s concerns;" “I really listen to my roommate when he/she talks;” and “I behave warmly toward my roommate.” We measured weekly responsiveness using the same items, asking how participants acted toward their roommate that week. Responsiveness was reliable at pretest (α =.93), posttest (α = .97) and in each weekly survey (.94 < α < .98, M α = .97).

A parallel set of items assessed the extent to which participants believed their roommates responded to them. Pretest and posttest items asked about roommates’ general responsiveness. Sample items included “my roommate tries to make me feel comfortable about myself and how I feel;” and "my roommate tries to make me feel valued as a person." We measured weekly roommate responsiveness with the same items, referring to how roommates acted toward participants that week. Perceptions of roommates’ responsiveness was reliable at pretest (α = .95), posttest (α =.98), and in the weekly surveys (.94 < α < .98, M α = .97).

Disclosure to the roommate and perceptions of roommates’ disclosure were measured with a 5-item modified version of a disclosure measure used by Gore and colleagues (Gore et al., 2006a; Miller, Berg, & Archer, 1983 ). Participants were instructed to indicate the extent to which they discussed each topic with their roommates; pretest and posttest items began with the phrase, “In general, I discuss:." All items were rated on a scale from 1 ( discussed not at all ) to 5 ( discussed fully and completely ) and included “my deepest feelings;” “my worst fears;” “what I like and dislike about myself;” “my close relationships with other people;” and “things I have done which I am proud of.” We measured weekly disclosure using the same instructions and items, beginning with the phrase “This week, I discussed:.” Disclosure to roommates was reliable at pretest (α =.85), posttest (α = .94) and from week to week (.85 < α < .95, M α = .92).

A parallel set of items assessed the extent to which participants believed their roommates self-disclosed. Pretest and posttest items began with the phrase, “In general, my roommate discusses:." Sample items included “his/her deepest feelings;” “his/her worst fears;” and “what he/she likes and dislikes about him/herself.” We measured weekly roommate disclosure with the same items, referring to the extent to which roommates self-disclosed that week. Roommate disclosure was reliable at pretest (α = .89), posttest (α =.94), and in weekly surveys (.89 < α < .95, M α = .93).

Perceived social support availability from roommates and support made available to roommates were measured with the Multidimensional Survey of Perceived Social Support ( Zimet, Dahlem, Zimet, & Farley, 1988 ). Perceived availability pretest and posttest items were preceded with the stem “In general, I feel that.” Weekly items were preceded with the stem “This past week, I felt that.” Sample items included “My roommate really tried to help me” and “I could count on my roommate if things went wrong.” Perceived social support availability was reliable at pretest (α =.93), posttest (α = .96) and from week to week (.93 < α < .97, M α = .96).

Social support made available to roommates was also measured at pretest, posttest and weekly using a parallel set of items. Sample items included “I really tried to help my roommate” and “my roommate can count on me when things go wrong.” Social support made available to roommates was reliable at pretest (α = .92), posttest (α =.95), and in weekly surveys (.86 < α < .96, M α = .94).

Anxiety was assessed with the Speilberger State Anxiety Scale ( Spielberger, Vagg, Barker, Donham, & Westberry, 1980 ). At pretest and posttest, participants rated their anxiety in general on a scale ranging from 1 (never) to 5 (always); in the weekly surveys, they rated their anxiety over the past week on the same scale. Anxiety had high internal consistency at pretest (α =.91), posttest (α =.94), and in each of the weekly surveys (.94 < α < .95, Mα = .94).

Depression was assessed at pretest, posttest, and weekly using the Center for Epidemiological Studies Depression Inventory (CES-D; Radloff, 1997). The CES-D was developed to measure depressive symptoms in community samples and consists of 20 depression-related symptom items rated on a 4 point scale (0–3) based on the amount of time during the past week the respondent has experienced each symptom. Scores can range from 0 to 60. The CES-D had high internal consistency at pretest (α =.86), posttest (α =.89) and each of the weekly surveys (.90 < α < .92, Mα = .91).

Factor Analyses

Table 1 shows the means, standard deviations, and intrapersonal (i.e., within-person) intraclass correlations, which adjust for the degree of nonindependence between dyad members ( Griffin & Gonzalez, 1995 ) for all primary variables in Study 1. Because correlations between compassionate goals and responsiveness to roommates and perceptions of roommates’ responsiveness were high, we conducted exploratory factor analyses (EFA) on these items at pretest and confirmatory factor analyses (CFA) on items at posttest and each week, comparing the fit of a model specifying 2 factors with a model specifying 1 factor.

Study 1 means, standard deviations, and intrapersonal intraclass correlations for all primary pretest, posttest, and mean weekly variables.

N = 230 at pretest, N = 218 at posttest. Chronic scores were calculated by averaging across the weekly reports. Self-image and compassionate goals were measured on a scale ranging from 1 (never) to 5 (always). Responsiveness to roommates and perceptions of roommates’ responsiveness were measured on a scale ranging from 1 (not at all) to 5 (very much).

Compassionate goals and responsiveness to roommates

Compassionate goals and responsiveness to roommates, though correlated, are empirically distinct. EFAs on the pretest items suggested that 2 factors accounted for 48% of the variance: all responsiveness items loaded on the first factor, with loadings ranging between .64 and .99; all compassionate goal items loaded on the second factor, with loadings ranging between .38 and .66. Importantly, no secondary loading exceeded |.28|. We conducted CFAs on items at posttest and each of the 10 weeks (yielding 11 separate sets of CFAs), testing two-factor, 136.84 < χ 2 (76, 218 < N < 230) < 232.48, Mχ 2 (76, 218 < N < 230) = 183.77, and single-factor solutions, 336.77 < χ 2 (77, 218 <N < 230) < 726.72, Mχ 2 (77, 218 < N < 230) = 586.71. For all analyses, two-factor solutions provided significantly better fit, 194.33 < Δχ 2 (1, 218 < N < 230) < 554.95, MΔχ 2 (1, 218 < N < 230) = 402.94.

Compassionate goals and perceptions of roommates’ responsiveness

Compassionate goals and perceptions of roommates’ responsiveness, though correlated, are also empirically distinct. EFAs on the pretest items suggested that 2 factors accounted for 51% of the variance: all responsiveness items loaded on the first factor, with loadings ranging between .74 and .93; all compassionate goal items loaded on the second factor, with loadings ranging between .39 and .63. Importantly, no secondary loading exceeded |.23|. We conducted CFAs on items at posttest and each of the 10 weeks (again, yielding 11 separate sets of CFAs), testing two-factor, 110.55 < χ 2 (76, 218 < N < 230) < 247.82, Mχ 2 (76, 218 < N < 230) = 166.94, and single-factor solutions, 448.56 < χ 2 (76, 218 < N < 230) < 948.77, Mχ 2 (76, 218 < N < 230) = 753.14. For all analyses, two-factor solutions provided significantly better fit, 338.01 < Δχ 2 (1, 218 < N < 230) < 747.22, MΔχ 2 (1, 218 < N < 230) = 586.20.

Overview of Primary Analyses

We conducted data analyses in two phases. In Phase 1 we focused on the intrapersonal associations between goals and responsiveness. We hypothesized that students’ goals would predict change in their responsiveness to roommates (Path A; Figure 1 ), which would predict change in their perceptions of their roommates’ responsiveness (Path B), which would in turn, predict change in their compassionate and self-image goals (Path D). In Phase 2 we focused on the interpersonal associations among these variables to examine how actors’ goals predict change in their responsiveness to partners (Path A), which predicts change in partners’ perceptions of actors’ responsiveness (Path F), which predicts change in partners’ subsequent responsiveness to actors (Path I) and goals (Path J). We tested both the intra- and interpersonal associations 1) within weeks, 2) from week to week using lagged analyses, and 3) across the semester from pretest to posttest.

Importantly, all intra- and interpersonal analyses assess change. For example, in weekly analyses we test whether fluctuations in goals (i.e., the difference between goals that week and that person’s average goals across 10 weeks) predict responsiveness that week; in lagged analyses, we test whether Week 1 goals predict change in responsiveness from Weeks 1 to 2; and in pretest and posttest analyses, we test whether chronic goals predict change in responsiveness from pretest to posttest. Thus, these analyses test the dynamic intra- and interpersonal associations between goals, responsiveness, and perceptions of others’ responsiveness.

General Analytic Strategy

In these data, individuals were nested within dyads and dyads were crossed with weeks ( Kashy, Donnellan, Burt, & McGue, 2008 ). Thus, we controlled for the nonindependence of individuals within dyads in all analyses using the MIXED command in SPSS ( Campbell & Kashy, 2002 ; Campbell, Simpson, Boldry, & Kashy, 2005 ; Kenny, Kashy, & Cook, 2006 ), and because individuals within dyads were indistinguishable, we specified compound symmetry so that intercept variances between dyad members were equal. For all analyses, we structured the data so that each dyad was represented by two lines of data, allowing each participant within a dyad to represent both an actor and a partner (see Campbell & Kashy, 2002 , for a sample arrangement of data). Path models were tested sequentially, with a separate regression equation for each path. For each path, we regressed the criterion on the predictor(s), controlling for all variables preceding that path in the model. All Study 1 path analyses are illustrated in Figure 2 and Figure 3 . Estimates outside of brackets indicate the partial correlation for that association, controlling for previous paths in the model; estimates inside brackets indicate tests of the individual path, not controlling for previous paths in the model. Partial correlations for all analyses were calculated using the method described by Rosenthal and Rosnow (1991) .

Study 1: Intrapersonal (within-person) path analyses of weekly, lagged-week, and pretest and posttest data. NOTE: All estimates are partial correlations; estimates in brackets indicate test of the individual path, not controlling for previous paths in the model. *** p < .001, ** p < .01, * p < .05.

Study 1: Interpersonal (between-person) path analyses of weekly, lagged-week, and pretest and posttest data. NOTE: All estimates are partial correlations; estimates in brackets indicate test of the individual path, not controlling for previous paths in the model. *** p < .001, ** p < .01, * p < .05.

Table 1 shows the means, standard deviations, and intrapersonal (i.e., within-person) intraclass correlations ( Griffin & Gonzalez, 1995 ), for all primary pretest, posttest, and chronic weekly variables. We created measures of chronic compassionate and self-image goals by averaging each measure across the 10 weeks. In general, compassionate goals, responsiveness, and perceptions of roommates’ responsiveness were strongly correlated at pretest and posttest, and across weeks. Self-image goals were less strongly associated with responsiveness and perceptions of roommates’ responsiveness. Because compassionate and self-image goals were significantly correlated, we regressed all outcome variables on compassionate and self-image goals simultaneously. Table 2 shows the interpersonal (i.e. actor-partner) intraclass correlations for all primary variables. Roommates’ compassionate goals, responsiveness and perceptions of roommates’ responsiveness were moderately correlated across time-points; self-image goals predicted fewer partner variables.

Study 1 interpersonal (i.e. actor-partner) intraclass correlations for all pretest, posttest, and mean weekly variables.

Intrapersonal Processes: Students’ Goals Predicting Their Own Responsiveness and Subsequent Goals

Phase 1 analyses test an intrapersonal model in which students’ compassionate and self-image goals predict change in their responsiveness to roommates (Path A; Figure 1 ), which predicts change in their perceptions of roommates’ responsiveness (Path B), which in turn predicts change in students’ subsequent compassionate and self-image goals (Path D). Thus, all Phase 1 analyses use only actor variables as predictors and outcomes. Note that, because the data are structured so that actors and partners are interchangeable, these analyses simultaneously test the process by which partners’ goals lead to partners’ own responsiveness and goals (i.e., Paths G, H, and J).

Weekly associations

First, we examined our hypothesized model within weeks, testing whether weekly interpersonal goals predicted responsiveness to roommates that same week, which then predicted perceptions of roommates’ responsiveness that week. Coefficients for weekly analyses were derived from random-coefficients models using restricted maximum-likelihood estimation, and models included fixed and random effects for the intercept and each predictor. In weekly analyses we person-centered all predictors so that scores represent differences from each individual’s own average across 10 weeks (e.g., Enders & Tofighi, 2007 ; Kreft & de Leeuw, 1998 ; Raudenbush & Bryk, 2002 ).

Path analyses supported our hypothesized model within weeks (see the top of Figure 2 ). Weekly compassionate goals predicted higher and self-image goals predicted lower weekly responsiveness to roommates. Responsiveness to roommates, in turn, positively predicted higher perceptions of roommates’ responsiveness.

Lagged-week analyses

Next, we tested the lagged-week associations between interpersonal goals, responsiveness to roommates, and perceptions of roommates’ responsiveness. Examination of the temporal sequence of effects across weeks does not demonstrate causality but can shed light on the plausibility or implausibility of causal pathways ( Kenny, 1975 ; Leary, 1995 ; Rogosa, 1980 ; West, Biesanz, & Pitts, 2000 ). For example, evidence that compassionate goals on Week 1 predict responsiveness in Week 2, controlling for responsiveness on Weeks 1 (i.e., testing whether goals one week predict residual change in responsiveness the following week) would be consistent with the hypothesis that compassionate goals cause responsiveness. No association would rule out a causal effect over this time period. Thus, unlike within-week analyses, lagged analyses test the plausibility of causal associations for each hypothesized pathway in our intrapersonal model.

Coefficients for lagged-week analyses were derived from random-coefficients models using restricted maximum-likelihood estimation, with models including fixed and random effects for the intercept and each predictor. We used a residual change strategy to test changes from week to week, regressing the Week N + 1 dependent variable on relevant Week N predictors, controlling for the Week N dependent variable. When change in a variable was a predictor, we entered the Week N and Week N + 1 predictors into the model and interpreted the week N + 1 variable.

We grand mean centered predictors in tests of lagged-week hypotheses because our prediction concerned change in the outcome from week to week. Lagged analyses examine whether change in the outcome from one week to the next is related to levels of the goal (or other predictor), regardless of the source – individual differences or weekly fluctuations around those individual differences. For example, we hypothesize that As’ goals one week predict their responsiveness the following week, controlling for that week’s responsiveness. Person centering predictors tests whether fluctuations in As’ goals from As’ own average goals predict outcomes. Consequently, in our example person-centering predictors in lagged analyses tests whether within-person departures from As’ average goals one week predict As’ responsiveness the following week, controlling for within-person departures from As’ average responsiveness that week. This does not test our lagged hypothesis. Thus, centering on the grand mean for that week is justified and appropriate in these analyses (e.g., Enders & Tofighi, 2007 ). 4

In the lagged-week data, we tested a path model in which goals at Week 1 predict change in responsiveness to roommates from Weeks 1 to 2, which predict simultaneous change in perceptions of roommates’ responsiveness from Weeks 1 to 2, which in turn predict change in compassionate and self-image goals from Weeks 1 to 3. We expected that, in the case of projection, associations between changes in responsiveness to roommates and changes in perceptions of roommates’ responsiveness would be relatively immediate because they occur as a function of perceptions – we expect that when actors become more responsive to roommates, they simultaneously increase their perceptions of roommates’ responsiveness. Accordingly, we hypothesized that change in responsiveness to roommates from Weeks 1 to 2 predicted simultaneous change in perceptions of roommates’ responsiveness.

For each path, we regressed the criterion on the predictor(s), controlling for all variables preceding that path in the model. We tested this path model (i.e., actors’ Week N compassionate and self-image goals predict change in actors’ responsiveness to roommates from Weeks N to N + 1, which predicts change in actors’ perceptions of roommates’ responsiveness from Weeks N to N + 1, which predicts change in actors compassionate and self-image goals from weeks N to N + 2; see the middle of Figure 2 ) in 4 regression equations (except when goals were entered as predictors: because we tested them simultaneously, we were able to test two paths in one equation). Lagged analyses were conducted on all 10 weeks. For simplicity, we refer to Week N as “Week 1,” Week N+1 as “Week 2,” and Week N+2 as “Week 3.”

Lagged-week path analyses supported our hypotheses (see middle of Figure 2 ). Week 1 compassionate goals predicted increased and Week 1 self-image goals predicted decreased responsiveness to roommates from Weeks 1 to 2, which predicted increased perceptions of roommates’ responsiveness from Weeks 1 to 2, which predicted increased compassionate goals and decreased self-image goals from Weeks 1 to 3.

Change across the semester

To test whether and how students’ chronic compassionate and self-image goals contribute to long-term changes in their responsiveness, perceptions of roommates’ responsiveness and goals, we examined a path model in which chronic goals averaged across 10 weeks predicted change in responsiveness to roommates across the semester, which then predicted change in perceptions of the roommates’ responsiveness, which in turn predicted changes in goals from pretest to posttest.

Coefficients for testing change from pretest to posttest were derived from fixed-effects models using restricted maximum-likelihood estimation. We grand mean centered predictors in tests of pretest and posttest hypotheses because we were interested in chronic goals and responsiveness as individual differences. We used a residual change strategy, similar to that used in lagged-week analyses, to test changes from pretest to posttest.

Results partially support our path model (see bottom of Figure 2 ). Chronic compassionate goals predicted increased and chronic self-image goals predicted decreased responsiveness to roommates from pretest to posttest, which predicted change in perceptions of roommates’ responsiveness from pretest to posttest, but perceptions of roommates’ responsiveness did not predict changes in students’ own compassionate and self-image goals from pretest to posttest.

Next, we tested several alternative explanations for and moderators of the associations tested in Figure 2 . We tested whether perceptions of roommates’ responsiveness, disclosure, support, anxiety and depression explained associations in our models by adding the appropriate variables to the path models tested above. Specific analyses for each covariate are described below. Note that our main concern was not whether these covariates were related to each outcome, but whether they could explain or offer an alternative explanation for our findings. Thus we do not report the association between each covariate and outcome variable. Instead, we report associations between our main predictors and outcome variables, controlling for covariates. We also test whether associations in Figure 2 are moderated by anxiety, depression or gender by adding the appropriate main effect and product terms, as described below. Simple slopes for interactions were computed at 1 standard deviation above and below the means of the moderators ( Aiken & West, 1991 ). Because of space considerations, we do not report individual statistics for each covariate test. Instead, we report a summary of results for each covariate; tables of results can be obtained from the first author.

Do perceptions of roommates’ responsiveness explain associations between goals and change in responsiveness to roommates?

Associations between students’ interpersonal goals and changes in their responsiveness to roommates might be attributed to perceptions of roommates’ responsiveness: students’ goals may lead them to be more or less responsiveness to roommates because goals are also associated with perceiving roommates as more or less responsive. We retested the links between compassionate and self-image goals and responsiveness to roommates in all models in Figure 2 , controlling for weekly perceptions of roommates’ responsiveness in weekly analyses, Week 1 perceptions of roommates’ responsiveness in lagged analyses, and chronic perceptions of roommates’ responsiveness in the pretest and posttest analyses. Across all three sets of analyses, all associations between compassionate goals and higher or increased responsiveness remained significant, .17 < pr s < .46, all p s < .001, although perceptions of roommates’ responsiveness predicted higher or increased responsiveness to roommates, .37 < pr s < .40, all p s < .001, across analyses. Thus, students’ perceptions of roommates as more or less responsive do not explain the association between compassionate goals and increased responsiveness to roommates.

On the other hand, 2 of the 3 analyses suggested that associations between students’ self-image goals and lower or decreased responsiveness to roommates could be explained by perceptions of roommates’ responsiveness. Weekly self-image goals no longer predicted weekly responsiveness to roommates, pr = −.03, ns, and chronic self-image goals no longer predicted change in responsiveness from pretest to posttest, pr = −.09, ns. In lagged analyses, Week 1 self-image goals still predicted decreased responsiveness to roommates from Weeks 1 to 2, pr = −.25, p < .001. Thus, students’ self-image goals led to their decreased responsiveness to the extent that they perceived their roommates as less responsive.

Does disclosure explain these associations?

Because others’ disclosure elicits responsiveness and perceptions of roommates’ responsiveness may be a function of people’s own disclosure ( Reis & Shaver, 1988 ), we examined the possibility that associations between responsiveness to roommates and perceptions of roommates’ responsiveness could be explained by perceptions of roommates’ disclosure or disclosure to roommates. We reanalyzed paths in the weekly, lagged-week, and change from pretest to posttest analyses, controlling for the appropriate disclosure variable (i.e., we regressed responsiveness to roommates on goals controlling for perceptions of roommates’ disclosure and we regressed perceptions of roommates’ responsiveness on responsiveness to roommates controlling for disclosure to roommates). In lagged-week analyses we controlled for Week 1 disclosure, or Weeks 1 and 2 disclosure, depending on the specific path we tested. In testing change from pretest to posttest, we controlled for the appropriate chronic or pretest and posttest disclosure variables, again depending on the specific path we tested.

Interpersonal goals predicted responsiveness and responsiveness predicted perceptions of roommates’ responsiveness, independent of disclosure. In 9 of 10 analyses, results remained unchanged when we retested these paths controlling for the appropriate disclosure variables; in the weekly model, the association between weekly self-image goals and responsiveness to roommates became marginally significant when we controlled for perceptions of roommates’ disclosure that week, pr = −.07, p < .06. Thus, people’s interpersonal goals offer an alternative to disclosure in creating responsive close relationships.

Does support availability explain these associations?

These paths might be explained by perceived available support from roommates and support made available to roommates. We reanalyzed all paths, controlling for the appropriate support variable (i.e., when responsiveness to roommates was the criterion, we controlled for support made available to roommates; when perceptions of roommates’ responsiveness or goals were the criterion, we controlled for perceived available support from roommates), using the strategy described above (e.g., in lagged-week analyses we controlled for change in support on the weeks responsiveness variables were included in analyses).

Results remained unchanged when we retested individual paths controlling for the appropriate support variables in 8 of 10 analyses (we did not retest nonsignificant links between perceptions of roommates’ responsiveness and goals). In the weekly model, the association between self-image goals and responsiveness to roommates became nonsignificant when we controlled for perceived available support, pr = −.05, ns, and in the pretest to posttest model, the association between chronic self-image goals and change in responsiveness to roommates became nonsignificant when we controlled for chronic perceived available support, pr = −.09, ns. Thus, self-image goals do not predict change in responsiveness beyond available support: that is, self-image goals may contribute to change in responsiveness because of available support. However, available support cannot explain associations between compassionate goals and change in responsiveness, and support made available to roommates cannot explain the association between students’ responsiveness and their perceptions of roommates’ responsiveness, nor can it explain why students’ perceptions of roommates’ responsiveness predict change in their compassionate goals in the lagged analyses.

Does anxiety or depression explain associations in these models?

We also tested whether the associations in Figure 2 were explained by feeling anxious or depressed. We reanalyzed all paths in weekly, lagged-week, and change from pretest to posttest analyses, controlling for anxiety and depression in separate analyses, using the strategy for testing covariates described above. Results did not change when we controlled for anxiety and depression in 18 of 20 analyses. In the pretest and posttest model, the link between chronic self-image goals and change in responsiveness to roommates became nonsignificant when we controlled for chronic anxiety, pr = −.13, ns, and marginal when we controlled for chronic depression, pr = −.14, p = .07. Thus, anxiety and depression appear to explain why self-image goals lead to longer-term decreases in responsiveness, but they cannot explain why self-image goals lead to decreased responsiveness in weekly and lagged-week analyses, or why compassionate goals lead to higher and increased responsiveness. Anxiety and depression also cannot explain projection of responsiveness or why it leads to increased compassionate goals in the lagged model.

Do associations in these models differ by levels of anxiety or depression?

Because links in the intrapersonal model might depend on negative mood, we tested whether anxiety or depression moderated the simple associations in Figure 2 (i.e., not controlling for other variables in the model), testing 26 separate product terms. Only one was significant: in the weekly model (top of Figure 2 ) anxiety moderated the relation between weekly compassionate goals and weekly responsiveness to roommates, pr = .07, p <.05, such that this association was stronger for those who reported higher anxiety, pr = .33, p <.001, compared to those reporting lower anxiety, pr = .20, p <.001. Results suggested that compassionate goals are beneficial for responsiveness, particularly when anxiety is higher. No other links in the intrapersonal models were moderated by anxiety or depression (all other pr s < |.13|, ns). Thus, results strongly suggest that the processes described by the intrapersonal model do not operate differently depending on negative mood.

Do these associations differ by gender?

Because the intrapersonal process from goals to perceptions of roommates’ responsiveness and change in goals might differ for men and women, we tested whether gender moderated each individual path (i.e., not controlling for other variables in the models) in all models in Figure 2 . In all analyses, gender was treated as a fixed effect (i.e., no random effects were specified in weekly and lagged-week models) and coded such that 1 = men and 2 = women. Gender moderated just 2 of the 13 associations tested (all other pr s < |.07|, ns). First, in the lagged model (the middle of Figure 2 ), gender moderated the association between change in responsiveness to roommates and change in perceptions of roommates’ responsiveness, pr = .25, p < .001, such that the relation was stronger for women, pr = .75, p < .001, than men, pr = .48, p < .001. Second, in tests of pretest to posttest change (the bottom of Figure 2 ), gender moderated the association between change in perceptions of roommates’ responsiveness and change in self-image goals, pr = −.16, p < .05, such that perceptions of roommates’ responsiveness predicted decreased self-image goals for women, pr = −.20, p < .01, but not men, pr = .04, ns .

Summary of intrapersonal processes

These data support our hypothesis that interpersonal goals predict change in responsiveness, which leads to projection of responsiveness: compassionate goals predict increased and self-image goals predict decreased responsiveness to roommates, which predicts increased perceptions of roommates’ responsiveness. This process operates within weeks, from week to week, and across 10 weeks, supporting our hypothesis about the dynamic associations between goals and projection of responsiveness. 5 . Covariates did not consistently account for any of these associations, nor were associations moderated by negative mood or gender. 6

Results were mixed with respect to our hypothesis that the relation between goals and projection is self-perpetuating. Lagged-week analyses supported our hypothesis - increased perceptions of roommates’ responsiveness from Weeks 1 to 2 predicted changes in interpersonal goals from Weeks 1 to 3. However, analyses of change from pretest to posttest did not support this hypothesis – changes in perceptions of roommates’ responsiveness from pretest to posttest did not predict changes in goals from pretest to posttest.

Interpersonal Processes: Actors’ Goals and Responsiveness Predicting Partners’ Goals and Responsiveness

The goal of Phase 2 analyses was to test our interpersonal model whereby actors’ compassionate goals predict their increased and self-image goals predict their decreased responsiveness to partners (Path A; Figure 1 ). Actors’ responsiveness to partners then predicts partners’ increased perceptions of actors’ responsiveness (Path F), which then predicts partners’ increased responsiveness to actors (Path I) and increased compassionate and decreased self-image goals (Path J). Again, note that, because the data are structured so that actors and partners are interchangeable, these analyses simultaneously the process by which partners’ goals predict actors’ responsiveness and goals (i.e., Paths G, L, C, and D). We examined this general model within weeks, from week to week using lagged analyses, and the across the semester using the same analytic strategies described to test our projection (i.e., intrapersonal) hypotheses.

Weekly Associations

We examined our hypothesized interpersonal model within weeks, testing whether actors’ weekly interpersonal goals predicted their responsiveness to roommates that same week, which then predicted partners’ perceptions of actors’ responsiveness that week, which then predicted partners’ interpersonal goals and responsiveness to actors.

Within-week analyses support our hypotheses (see the top of Figure 3 ). On weeks when actors had higher compassionate goals they reported being more responsive to partners, and on weeks when actors had higher self-image goals they reported being less responsive to partners. Actors’ responsiveness to partners predicted partners’ higher perceptions of actors’ responsiveness, which predicts partners’ higher responsiveness to actors and partners’ compassionate goals. Partners’ weekly perceptions of actors’ responsiveness did not predict their own self-image goals that same week.

Again, because lagged analyses provide information about the plausibility of causal pathways, we tested whether actors’ compassionate and self-image goals at Week 1 predicted change in their responsiveness from Weeks 1 to 2, which predicted simultaneous change in partners’ perceptions of actors’ responsiveness from Weeks 1 to 2, which then predicted change in partners’ interpersonal goals and responsiveness to actors from Weeks 1 to 3. We predicted that change in actors’ responsiveness to roommates from Weeks 1 to 2 predicted simultaneous change in partners’ perceptions of actors’ responsiveness from Weeks 1 to 2 because responsiveness transactions between roommates should occur simultaneously (i.e., partners should perceive change in actors’ responsiveness as actors report change in their own responsiveness).

Lagged-week analyses did not support our interpersonal hypotheses (see middle of Figure 3 ). Actors’ Week 1 compassionate goals predicted increased responsiveness and Week 1 self-image goals predicted decreased responsiveness to partners from Weeks 1 to 2, but change in actors’ responsiveness to partners from Weeks 1 to 2 did not predict simultaneous change in partners’ perceptions of actors’ responsiveness from Weeks 1 to 2. Change in partners’ perceptions of actors’ responsiveness from Weeks 1 to 2 positively predicted change in partners’ responsiveness to actors and compassionate goals from Weeks 1 to 3, but did not predict change in partners’ self-image goals from Weeks 1 to 3.

These results do not support the plausibility of causal effects of change in actors’ responsiveness to partners on change in partners’ perceptions of actors’ responsiveness. However, changes in partners’ perceptions of actor’s responsiveness led to their increased responsiveness to actors and compassionate goals the following week.

Change from pretest to posttest

To test whether and how actors’ chronic compassionate and self-image goals contribute to long-term changes in their own responsiveness, and partners’ perceptions of actors’ responsiveness, responsiveness to actors, and goals, we examined a path model in which actors’ chronic goals predicted change in actors’ responsiveness to partners across the semester, which predicted change in partners’ perceptions of actors’ responsiveness, which in turn predicted changes in partners’ goals and responsiveness to actors from pretest to posttest.

Results support our interpersonal model (see bottom of Figure 3 ). Actors’ chronic compassionate goals predict increased and chronic self-image goals predict decreased responsiveness to partners. Change in actors’ responsiveness to partners positively predicted change in partners’ perceptions of actors’ responsiveness from pretest to posttest, which positively predicted change in partners’ responsiveness to actors and compassionate goals and marginally negatively predicted change in partners’ self-image goals across the semester.

Next, we tested several alternative explanations for and moderators of the associations tested in Figure 3 . We tested whether disclosure, available support, anxiety or depression explained associations between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness by adding the appropriate variables to the interpersonal path models tested above. Details of these analyses are provided below. Note that, as in tests of covariates in the intrapersonal models, the critical test was whether covariates altered the results of our path models, and not whether the covariates were related to each outcome. Because of this, we do not report the association between each covariate and outcome variable. We also tested whether associations between actors’ responsiveness and partners’ perceptions of actors’ responsiveness were moderated by partners’ own goals, and whether associations unique to the interpersonal models were moderated by anxiety, depression or gender using the strategy described above. Again, because of space considerations, we do not report individual statistics for each covariate test, but instead report a summary of results for each covariate; tables of results can be obtained from the first author.

Do partners’ goals influence how they perceive actors’ responsiveness?

We tested the possibility that the links between actors’ responsiveness and partners’ increased perceptions of actors’ responsiveness were dependent on partners’ goals. For all models in Figure 3 , we tested whether partners’ goals moderated the individual paths (i.e., not controlling for other variables in the models) between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness, testing the moderating effect of each goal separately. In the weekly model we tested whether partners’ weekly goals moderated this association; in the lagged analyses we tested whether partners’ Week 2 goals moderated the link between change in actors’ responsiveness to partners from Weeks 1 to 2 and change in partners’ perceptions of actors’ responsiveness from Weeks 1 to 2; in the pretest to posttest analyses we tested whether partners’ posttest goals moderated the link between change in actors’ responsiveness to partners from pretest to posttest and change in partners’ perceptions of actors’ responsiveness from pretest to posttest. Across analyses, partners’ goals did not moderate this association, compassionate goals: −.07 < pr < .02, all ns; self-image goals: all pr s < .02, all ns. Actors’ and partners’ agreement about actors’ responsiveness to partners does not depend on partners’ compassionate or self-image goals.

Does disclosure, available support, anxiety, or depression explain associations between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness? 7

We tested associations between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness in weekly and change from pretest to posttest models in Figure 3 , separately controlling for partners’ perceptions of actors’ disclosure, partners’ social support available from actors, and partners’ anxiety and depression using a strategy similar to that described for the intrapersonal models. We did not test covariates in the lagged model because there was no association between change in actors’ responsiveness and change in partners’ perceptions of actors’ responsiveness. Results remained unchanged in 7 of 8 tests. Change in actors’ responsiveness to partners from pretest to posttest no longer predicted change in partners’ perceptions of actors’ responsiveness from pretest to posttest when we controlled for change in partners’ support available from actors. Overall, results suggest that actors’ and partners’ agreement about actors’ responsiveness cannot be accounted for by partners’ perceptions of disclosure, anxiety, or depression. However, changes in actors’ responsiveness to partners leads to changes in partners’ perceptions of actors’ responsiveness because actors’ responsiveness is supportive.

Does disclosure, available support, anxiety or depression explain associations between changes in partners’ perceptions of actors’ responsiveness and change in partners’ responsiveness or compassionate goals?

We tested the link from partners’ perceptions of actors’ responsiveness to partners’ responsiveness and compassionate goals, controlling for partners’ perceptions of actors’ disclosure, support available from roommates, anxiety, and depression (in 24 separate analyses). We did not retest nonsignificant links between partners’ perceptions of actors’ responsiveness and partners’ self-image goals. All results remained unchanged, suggesting that partners’ responsiveness reciprocity (i.e., the link between partners’ perceptions of responsiveness and responsiveness to actors) and compassionate goals could not be accounted for by their perceptions of actors’ disclosure, support available from roommates, anxiety, or depression.

Do these associations differ by partners’ levels of anxiety or depression?

We tested whether partners’ anxiety or depression moderated links between actors’ responsiveness and partners’ perceptions of actors’ responsiveness in all models in Figure 3 . We also tested whether partner’s anxiety or depression moderated associations between partners’ perceptions of actors’ responsiveness and partners’ responsiveness to actors in lagged and pretest and posttest models. Partners’ anxiety and depression did not moderate these associations in 9 of 10 tests (all pr s < |.11|, ns). However, in pretest and posttest analyses, depression moderated the link between change in partners’ perceptions of actors’ responsiveness and partners’ responsiveness, pr = −.20, p < .01, such that this association was stronger when partners also reported lower depression (lower depression: pr = .70, p < .001; higher depression: pr = .63, p < .001). Thus, when partners become more depressed, they are less likely to reciprocate increased perceptions of actors’ responsiveness.

We tested whether gender moderated associations unique to the interpersonal models (e.g., links between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness in weekly, lagged-week, and change from pretest to posttest analyses; links between partners’ perceptions of actors’ responsiveness and partners’ goals in weekly analyses; and links between partners’ perceptions of actors’ responsiveness and partners’ responsiveness to actors in lagged-week, and change from pretest to posttest analyses), using the same strategy reported for testing whether gender moderated intrapersonal associations. Gender did not moderate any of the 7 paths tested (all pr s < |.13|, all ns).

Study 1 examined intra- and interpersonal models of responsiveness in first-semester college roommates. Results were generally consistent with our hypotheses: students’ compassionate and self-image goals lead to change in their responsiveness to roommates, with consequences for change in both people’s perceptions of responsiveness in the relationship and interpersonal goals. Thus, people’s goals can create their own and others’ responsiveness and goals. In general, these associations were not due to disclosure, available support, anxiety, or depression.

Students’ goals predict their own experiences of responsiveness - their compassionate and self-image goals predict change in their responsiveness to partners, which then predicts projection of their responsiveness onto partners. We also predicted a self-perpetuating cycle between goals and responsiveness in relationships: actors’ perceptions of roommates’ responsiveness in turn predict change in their own compassionate and self-image goals. We found support for this hypothesis in the lagged-week data, but these effects did not appear to have any cumulative effect from pretest to posttest, suggesting projection processes have relatively short-term consequences for people’s goals, but do not affect their goals over the longer-term.

Tests of the interpersonal associations were partially consistent with our hypothesis. We expected agreement between actors and partners on actors’ responsiveness to partners, which should have predicted change in partners’ responsiveness to actors and goals. We found strong evidence for this hypothesis within weeks and over the semester - when actors reported increased responsiveness to partners, partners perceived increased responsiveness from actors, which then led to partners’ increased responsiveness to actors and increased compassionate goals. However, we did not find these associations in the lagged-week analyses – changes in actors’ responsiveness to partners from Weeks 1 to 2 did not predict partners’ perceptions of actors’ responsiveness over that same period, perhaps due to measurement timing; when students were asked to think about their and their roommates’ behaviors over the past week, the two roommates may have simply recalled or drew their responses from different events. More precise measurements of daily goals and responsiveness might show greater agreement between actors’ and partners’ reports. In study 2, we examined these associations in daily measures across three weeks to investigate this possibility.

Study 1 also did not address the implications of being responsive to others for the relationship itself. We predicted that this process of building (or undermining) projected and actual responsiveness between roommates has implications for both people’s perceived relationship quality. We included a measure of relationship quality in Study 2 to address this issue.

Study 1 ruled out self-disclosure, anxiety, and depression as alternative explanations for these processes. Study 1 also ruled out available support as an alternative explanation for associations between change in partners’ perceptions of actors’ responsiveness and change in partner’s responsiveness and compassionate goals and in 2 of 3 analyses, change in available support also could not account for associations between change in actors’ responsiveness to partners to change in partners’ perceptions of actors’ responsiveness. However, self-esteem or esteem for roommates might also account for these associations. For example, if high self-esteem predicts compassionate goals, responsiveness to roommates, and perceived responsiveness of roommates, the associations observed in Study 1 could be spurious. Esteem for roommates might also produce spurious associations if associations with perceptions of responsiveness are simply due to positive evaluations of roommates. Additionally, esteem for roommates may also produce spurious findings if students who hold their roommates in high esteem have more compassionate goals, are more responsive, and perceive their roommates as more responsive.

Alternatively, self-esteem may moderate associations tested in Study 1. Previous research shows that self-esteem moderates associations between perceptions of partners’ regard and relationship behavior and satisfaction ( Murray, Bellavia, Rose, & Griffin, 2003 ; Murray, Griffin, Rose, and Bellavia, 2003 ). In the present context, associations between actors’ compassionate goals and responsiveness to partners, and between perceptions of roommates’ responsiveness and relationship quality might be stronger when actors’ self-esteem is high. We tested self-esteem and esteem for roommates as covariates and self-esteem as a moderator in Study 2.

Study 2 used data from the Roommate Goals Study (Crocker & Canevello, Study 2) to examine how students’ goals predict projected and reciprocal responsiveness between roommates that ultimately influences both peoples’ relationship quality. The general design was similar to Study 1, but over a different time frame; same-sex freshmen roommate dyads completed a series of pretest, posttest, and 21 daily questionnaires, each including measures of compassionate and self-image goals, responsiveness to roommates, perceived roommates’ responsiveness, relationship quality, and self-esteem and esteem for roommates.

Again, we expected both intrapersonal and interpersonal consequences of compassionate and self-image goals. The design of Study 2 allowed us to more closely examine interactions between roommates by assessing students daily instead of weekly. Also, we followed roommate pairs over a shorter time period to examine whether these processes occur in daily interactions. Finally, we included measures of self-esteem and esteem for roommates to rule out these variables as alternative explanations for our hypotheses.

Sixty-five first-semester freshmen roommate dyads at a large Midwestern university who did not know each other prior to college volunteered for a study of goals and roommate relationships during the fall semester. Students completed a pretest survey, 21 daily surveys, and a posttest survey. Sixty-two pairs (95%) completed all 23 surveys; 46 pairs (71%) were female, and 19 (29%) were male. Sixty-eight percent of participants reported their race as White or European-American, 4% as Black or African-American, 16% as Asian or Asian-American, 5% were Latino(a), and 6% selected other. The racial composition of the sample closely approximates the racial composition of the incoming freshman class. Participants ranged in age from 18 to 22 years ( M = 18.2 years).

Roommate pairs attended an initial lab session to learn about the study, give their informed consent, complete the pretest survey, and receive instructions for completing the remaining 22 online surveys. The daily surveys took about 10 min. to complete and roommates were required to complete daily surveys on the same day. Participants were instructed to complete the surveys in privacy and not to discuss their responses with each other. To retain as many participants as possible in the study, participants were given up to 28 days to complete the 21 daily surveys. 8 Once roommates had completed 21 daily surveys, they completed the posttest survey and were paid for their participation. For a detailed description of the procedure see Crocker and Canevello (2008) , Study 2.

Participants completed measures of their compassionate and self-image goals, perceptions of roommates’ responsiveness, responsiveness to the roommate, relationship quality, self-esteem, and esteem for roommates at pretest, posttest, and daily. The pretest measure also included questions about demographics (gender, race/ethnicity, age, parental income). Additional measures not germane to the goals of the present investigation were also included.

Compassionate and self-image goals for participants’ relationships with their roommates were measured at pretest, posttest, and daily, using the measure described in Study 1. 9 Based on exploratory factor analyses, we modified the scales in the following ways: for the compassionate goals scale we dropped “be supportive of my roommate” and “make a positive difference in my roommate’s life” and added "be aware of the impact my behavior might have on my roommate's feelings" and "avoid doing anything that would be harmful to my roommate." For the self-image goals scale we dropped “avoid revealing my shortcomings and vulnerabilities” and added "avoid coming across as unintelligent or incompetent" and "demonstrate my intelligence.” Both scales had high internal consistency at pretest (self-image α = .80; compassionate α = .84), posttest (self-image α = .87; compassionate α = .93), and across days (self-image goals: .75 < α < .87, M α = .83; compassionate goals: .88 < α < .96, M α = .94).

Responsiveness to the roommate and perceptions of roommates’ responsiveness were measured using a 12-item version of the scale used in Study 1. Additional items included “I do things to show my roommate that I care about him/her;” “I try to see things from his/her point of view;” “I am uncaring toward my roommate” (reverse); “I try to show respect for my roommate’s capabilities and talents;” “I don’t really take my roommate’s concerns seriously” (reverse); and “I am sincere when I interact with my roommate.” We measured daily responsiveness with 8 items from the pretest and posttest measure asking how participants acted toward their roommate that day. Responsiveness was reliable at pretest (α =.93), posttest (α = .95) and from day to day (.89 < α < .95, M α = .93).

A parallel set of 12 items assessed the extent to which participants believed their roommates responded to them at pretest and posttest. Sample items included "My roommate seems sensitive to my feelings" and "My roommate is sincere when he/she interacts with me." We removed “My roommate seems uncaring” and “My roommate makes me feel comfortable about myself and my feelings” because in factor analyses, they overlapped with relationship quality. We measured daily roommate responsiveness with 7 items from the pretest and posttest measure asking how roommates acted toward participants that day. Roommate responsiveness was reliable at pretest (α = .96), posttest (α =.94), and from day to day (.84 < α < .92, M α = .89).

Relationship quality included measures of satisfaction, commitment and closeness. Because all scales were highly correlated at each time point (all r s > .67), and we had the same predictions for these indicators of relationship quality, we standardized and averaged these scales to create pretest, posttest, and daily composite relationship quality scores.

In the pretest and posttest, we measured relationship satisfaction with 6 questions: “In general, how satisfied are you with your relationship with your roommate?” “How well does your roommate meet your needs?” “How good is your relationship with your roommate compared to most?” “How many problems are there in your relationship with your roommate?” (reverse scored) “How often do you wish you hadn’t moved in with your roommate?” (reverse scored) and “To what extent has your relationship with your roommate met your original expectations?” Students responded on a scale from 1 ( poorly/not at all/never ) to 5 ( extremely well/completely/often ). We measured commitment using an abbreviated version of the measure developed by Rusbult and colleagues ( Rusbult, Verette, Whitney, Slovik, & Lipkus, 1991 ): “To what extent are you committed to your relationship with your roommate?” “For what length of time would you like your relationship with your roommate to last?” “To what extent are you attached to your roommate?” and “How likely is it that you will end your relationship with your roommate in the near future?” (reverse scored). Students rated items on a 9-point scale (0 = Not at all/< 1 month , 8 = Extremely/ 5+ years ). Two items assessed closeness (e.g., Gore et al, 2006 ): “Relative to all other relationships, how would you characterize your relationship with your roommate?” and “Relative to what you know about other people’s roommate relationships, how would you characterize your relationship with your roommate?” and were rated on a 1 ( not as close as others ) to 5 ( much closer than others ) scale. The relationship quality composite measures had high internal consistency at pretest (α =.90) and posttest (α =.92)

We measured daily relationship satisfaction with 3 questions from the pretest/posttest measure: “How well does your roommate meet your needs today?” “How good is your relationship with your roommate today, compared to most?” and “Today, to what extent do you wish you hadn’t moved in with your roommate?” (reversed). Students responded on a scale from 1 ( poorly/not at all/never ) to 5 ( extremely well/completely/often ). We measured commitment using the four commitment items from the pretest measure, with each item referring to how they felt that day. A single item assessed closeness: “How close do you feel to your roommate today?” and was rated on a 1 ( not at all ) to 5 ( extremely ) scale. The daily relationship quality composite measure had high internal consistency each day of the study (.82 < α < .88, M α = .86).

Self-esteem . The Rosenberg Self-Esteem Inventory ( Rosenberg, 1965 ) was used to assess global self-esteem. In the pretest and posttest surveys, self-esteem was measured using the original 10-item measure. We used an abbreviated version of this measure in the daily surveys, with the stem “Today, to what extent did you feel:” followed by 4 questions: “that you are a person of worth,” “that you are a failure,” (reverse scored) “satisfied with yourself,” and “that you are no good at all,” (reverse scored). Self-esteem had adequate internal consistency at pretest (α = .89), posttest (α = .90), and each day of the study (.83 < α < .93, M α = .90).

Esteem for roommates was measured at pretest, posttest, and in each of the daily surveys. In the pretest and posttest, esteem for roommates was measured with 10 questions, adapted from the Rosenberg self-esteem measure ( Rosenberg, 1965 ), with items assessing evaluation of roommates (not perceptions of roommates’ self-esteem). Sample items included: “I certainly feel my roommate is useless at times,” (reverse scored) “I feel that my roommate has a number of good qualities,” and “I feel that my roommate is a person of worth, at least on an equal basis with others.” Students responded on a scale from 1 ( strongly disagree ) to 5 ( strongly agree ). In the daily surveys, we measured esteem for roommates with the stem “Right now, to what extent do you feel that:” followed by 4 questions: “your roommate is a person of worth,” “your roommate is a failure,” (reverse scored) “you are satisfied with your roommate,” and “your roommate is no good at all,” (reverse scored). Esteem for roommates had adequate internal consistency at pretest (α =.88), posttest (α =.89), and across days (.78 < α < .91, M α = .86).

Table 3 shows the intrapersonal (i.e., within-person) intraclass correlations, which adjust for the degree of nonindependence between dyad members ( Griffin & Gonzalez, 1995 ), means, and standard deviations for all of the main variables in Study 2. As in Study 1, compassionate goals correlated strongly with responsiveness to roommates and perceptions of roommates’ responsiveness. Also, perceptions of roommates’ responsiveness correlated strongly with relationship quality. Following Study 1, we conducted a series of factor analyses to determine whether these measures were empirically distinct.

Study 2 means, standard deviations, and intrapersonal intraclass correlations for all pretest, posttest, and mean daily variables.

N = 130 at pretest, N = 124 at posttest. Chronic scores were calculated by averaging across the 21 daily reports. Responsiveness to the roommate and roommate responsiveness was measured on a scale ranging from 1 (not at all) to 5 (very much). Relationship quality was standardized. Chronic self-image and compassionate goals were measured on a scale ranging from 1 (never) to 5 (always).

Perceptions of roommates’ responsiveness and relationship quality

Perceptions of roommates’ responsiveness and relationship quality are empirically distinct. We conducted EFAs on pretest relationship quality and perceptions of roommates’ responsiveness items. In an initial EFA on pretest items, all but two perceptions of roommates’ responsiveness items (“my roommate makes me feel comfortable about myself and my feelings” and “my roommate seems uncaring”) loaded on the two factors as predicted. After removing those items, a 2-factor solution accounted for 57% of the variance: all perceptions of roommate’s responsiveness items loaded on the first factor, with loadings ranging between |.49| and |.87|; all relationship quality items loaded on the second factor, with loadings ranging between |.49| and |.86|. Importantly, the highest loading on a secondary factor was |.22|. We conducted CFAs on items for each day of the study and at posttest, excluding the two items removed from the EFA above (for a total of 22 separate sets of analyses), testing 2-factor, 120.46 < χ 2 (df = 89 and 208, 124 < N < 130) < 553.274, Mχ 2 (df = 89 and 208, 124 < N < 130) = 232.09, and single-factor solutions, 188.97 < χ 2 (df = 90 and 209, 124 < N < 130) < 860.03, Mχ 2 (df = 90 and 209, 124 < N < 130) =371.45. For all sets of analyses, 2-factor solutions fit significantly better, 63.54 < Δχ 2 (1, 124 < N < 130) < 306.752, MΔχ 2 (1, 124 < N < 130) = 139.36. Because of their overlap with relationship quality, we did not include the two cross-loading items in our final scoring of perceptions of roommates’ responsiveness, nor did we include them in future factor analyses of perceptions of roommates’ responsiveness items.

As in Study 1, the items used to assess compassionate goals and responsiveness to roommates were empirically distinct. We conducted EFAs on pretest compassionate goals and responsiveness to roommate items and CFAs on these items at posttest and each day, comparing the fit of a 2-factor model with that of a single-factor model. In an initial EFA on pretest items, a 2-factor solution accounted for 50% of the variance: all responsiveness items loaded on the first factor, with loadings ranging between |.41| and |.88|; all compassionate goal items loaded on the second factor, with loadings ranging between .50 and .74. No secondary loading exceeded |.17|. We conducted CFAs on items at posttest and across the 21 days, testing 2-factor, 133.52 < χ 2 (df = 89 and 151, 124 < N < 130) < 350.19, M χ2 (df = 89 and 151, 124 < N < 130) = 198.89, and single-factor solutions, 252.81 < χ 2 (df = 90 and 152, 124 < N < 130) < 500.52, M χ 2 (df = 90 and 152, 124 < N < 130) = 394.18. For all sets of analyses, the 2-factor solution fit significantly better, 104.41 < Δχ 2 (1, 124 < N < 130) < 308.15, M Δχ 2 (1, 124 < N < 130) = 195.29.

The items used to assess compassionate goals and perceptions of roommates’ responsiveness are also empirically distinct. We conducted EFAs on pretest compassionate goals and perceptions of roommates’ responsiveness items and CFAs on these items at posttest and each day, comparing the fit of a 2-factor model with that of a single-factor model. An EFA on pretest items showed that a 2-factor solution accounted for 53% of the variance: all perceptions of roommates’ responsiveness items loaded on the first factor, with loadings ranging between |.58| and |.88|; all compassionate goal items loaded on the second factor, with loadings ranging between .44 and .78. No secondary loading exceeded |.27|. We conducted CFAs on items at posttest and across the 21 days, testing 2-factor, 111.09 < χ 2 (df = 76 and 118, 124 < N < 130) < 234.48, M χ2 (df = 76 and 118, 124 < N < 130) = 150.34, and single-factor solutions, 241.75 < χ 2 (df = 77 and 119, 124 < N < 130) < 531.71, M χ 2 (df = 77 and 119, 124 < N < 130) = 395.46. For all sets of analyses, the 2-factor solution fit significantly better, 102.44 < Δχ 2 (1, 124 < N < 130) < 376.90, M Δχ 2 (1, 124 < N < 130) = 245.12.

Overview of Analyses

Data analyses proceeded in two phases. Phase 1 focused on intrapersonal associations and included only actor variables: we tested a model in which students’ goals predict their responsiveness to roommates (Path A), which predicts their perceptions of roommates’ responsiveness (Path B), which then predicts students’ own goals (Path D) and relationship quality (Path E). Phase 2 focused on interpersonal associations, and included actor and partner variables: we tested a model in which actors’ goals predict their responsiveness to partners (Path A), which predicts partners’ perceptions of actors’ responsiveness (Path F), which predicts partners’ goals (Path J) and relationship quality (Path K). As in Study 1, we tested associations in each phase within a given day, from day to day using lagged analyses, and across three weeks (i.e., from pretest to posttest). As in Study 1, all intra- and interpersonal analyses assess change.

Because the structure of the data was similar to Study 1 (persons within dyads and dyads crossed with days; Kashy et al., 2008 ), we arranged the data and conducted analyses using the same strategy as in Study 1, controlling for the appropriate levels of nonindependence using the MIXED command in SPSS, specifying compound symmetry so that intercept variances between dyad members were equal, and testing change using residuals. Again, path models were tested sequentially; for each path, we regressed the criterion on the predictor, controlling for all variables preceding that path in the model. Partial correlations are reported for all analyses ( Rosenthal & Rosnow, 1991 ).

Table 3 shows the means, standard deviations, and intrapersonal (i.e., within-persons) intraclass correlations for all pretest, posttest, and mean daily variables. We created measures of chronic compassionate and self-image goals by averaging each measure across the 21 days. Compassionate goals, responsiveness, and perceptions of roommates’ responsiveness were strongly correlated at pretest and posttest, and across days. Self-image goals were less strongly correlated with responsiveness and perceptions of roommates’ responsiveness, particularly at posttest and in the chronic measures. As in Study 1, compassionate and self-image goals were significantly correlated. To test the independent effects of goals on outcomes, we regressed all outcome variables on compassionate and self-image goals simultaneously. Table 4 shows the interpersonal (i.e. actor-partner) intraclass correlations for all variables. Roommates’ reported compassionate goals, responsiveness and perceptions of roommates’ responsiveness were moderately correlated across time-points; actors’ self-image goals predicted fewer partner variables.

Study 2 interpersonal (i.e. actor-partner) intraclass correlations for all pretest, posttest, and mean daily variables.

Intrapersonal Processes: Students’ Goals Predicting Their Own Responsiveness, Perceptions of Roommates’ Responsiveness and Subsequent Goals and Relationship Quality

Phase 1 analyses test an intrapersonal model in which students’ compassionate and self-image goals predict their responsiveness to roommates (Path A; Figure 1 ), which predicts their perceptions of roommates’ responsiveness (Path B), which in turn predict their subsequent compassionate and self-image goals (Path D) and relationship quality (Path E). All Phase 1 analyses use only actor variables as predictors and outcomes. As in Study 1, because actors and partners are interchangeable, these analyses simultaneously test the process by which partners’ goals lead to partners’ own goals and relationship quality (i.e., Paths G, H, J and K).

Same Day Associations

First, we examined our hypothesized model within days, testing whether daily interpersonal goals predicted daily responsiveness to roommates, which predicted daily perceptions of roommates’ responsiveness, which then predicted daily relationship quality. Following Study 1, coefficients for daily analyses were derived from random-coefficients models using restricted maximum-likelihood estimation, where models included fixed and random effects for the intercept and each predictor. Also, following the rationale for centering described in Study 1 within-week analyses, we person-centered all predictors.

Within-day analyses supported our hypothesized intrapersonal model (see top of Figure 4 ). Daily compassionate goals predicted higher responsiveness to roommates; daily self-image goals did not predict responsiveness to roommates. Responsiveness to roommates predicted higher perceptions of roommates’ responsiveness on that day, which then positively predicted relationship quality on that day.

Study 2: Intrapersonal (within-person) path analyses of daily, lagged-day, and pretest and posttest data. NOTE: All estimates are partial correlations; estimates in brackets indicate test of the individual path, not controlling for previous paths in the model. *** p < .001, ** p < .01, * p < .05.

Lagged-Day Analyses

We next examined our intrapersonal model using lagged-day analyses to test the plausibility of causal paths in our model. Using the strategy described in Study 1 lagged analyses, i.e., random-coefficients models using restricted maximum-likelihood estimation with all predictors grand mean centered, we constructed MIXED models that regressed one day’s criterion on the previous day’s predictor and criterion.

Using the logic from Study 1’s intrapersonal lagged-week path model, we hypothesized a lagged-day path model in which goals at Day 1 predict change in responsiveness to roommates from Days 1 to 2, which predict simultaneous change in perceptions of roommates’ responsiveness from Days 1 to 2, which in turn predict change in compassionate and self-image goals and relationship quality from Days 1 to 3.

Results supported the intrapersonal model (see middle of Figure 4 ). Day 1 compassionate goals predicted increased responsiveness to roommates and Day 1 self-image goals predicted decreased responsiveness to roommates from Days 1 to 2. Change in responsiveness to roommates from Days 1 to 2 positively predicted change in perceptions of roommates’ responsiveness from Days 1 to 2, which in turn, positively predicted change in relationship quality and compassionate goals and but did not predict change in self-image goals from Days 1 to 3.

These analyses support the plausibility of a causal chain from goals to responsiveness to roommates and perceptions of roommates’ responsiveness to relationship quality. Compassionate goals predict increased responsiveness and perceptions of roommates’ responsiveness across days, which results in increased relationship quality and compassionate goals and decreased self-image goals, while self-image goals predict decreased responsiveness and perceptions of roommates’ responsiveness across days, which results in decreased relationship quality and compassionate goals and increased self-image goals.

To test whether and how students’ chronic compassionate and self-image goals contribute to changes in their responsiveness, perceptions of roommates’ responsiveness, goals, and relationship quality over time, we tested a path model in which chronic goals averaged across 21 days predicted change in responsiveness to roommates from pretest to posttest, which then predicted change in perceptions of the roommates’ responsiveness, which in turn predicted changes in goals and relationship quality from pretest to posttest. Again, following Study 1, coefficients for testing change from pretest to posttest were derived from fixed-effects models using restricted maximum-likelihood estimation and we grand mean centered all predictors.

Results testing change across 3 weeks supported our intrapersonal model (see bottom of Figure 4 ) and suggest that effects of goals accumulate over time, resulting in longer-term changes in compassionate and self-image goals and relationship quality. Chronic compassionate goals predict increased and chronic self-image goals predict decreased responsiveness to roommates from pretest to posttest, which predicted increased perceptions of roommates’ responsiveness from pretest to posttest, which predicted increased relationship quality and compassionate goals, and decreased self-image goals.

As in Study 1, we tested several alternative explanations for and moderators of these associations, using the same statistical strategies described in Study 1. We tested whether perceptions of roommates’ responsiveness, self-esteem, or esteem for roommates explained associations in Figure 4 . We also test whether these associations were moderated by self-esteem or gender. Finally, we also tested an alternative model suggesting that perceptions of roommates’ responsiveness result from relationship quality (as compared to our hypothesis that relationship quality results from perceptions of roommates’ responsiveness). As in Study 1, we used the procedure recommended by Aiken and West (1991) to examine simple slopes. Specific analyses for each covariate are described below. As in Study 1, our main concern was whether these covariates offered an alternative explanation for our findings. Thus, we report the associations between our predictor and outcome variables, controlling for covariates, but do not report whether covariates were related to each outcome, not controlling for our predictors. Again, tables of these analyses including covariates can be obtained from the first author.

We tested whether associations between students’ interpersonal goals and changes in their responsiveness to roommates could be explained by reciprocity of perceptions of roommates’ responsiveness by retesting the links between compassionate and self-image goals and responsiveness to roommates (all models in Figure 4 ), controlling for daily perceptions of roommates’ responsiveness in daily analyses, Day 1 perceptions of roommates’ responsiveness in lagged analyses, and chronic perceptions of roommates’ responsiveness in the pretest and posttest analyses. As in Study 1, perceptions of roommates responsiveness predicted higher or increased responsiveness to roommates across all three sets of analyses, .19 < pr s < .60, all p s < .001. Associations between compassionate goals and higher and increased responsiveness remained significant in daily and lagged analyses, daily: pr = .26, p < .001; lagged: pr = .33, p < .001. In pretest and posttest analyses, chronic compassionate goals no longer predicted change in responsiveness to roommates when we controlled for chronic perceptions of roommates’ responsiveness, pr = .10, ns.

The associations between students’ self-image goals and decreased responsiveness to roommates were not explained by perceptions of roommates’ responsiveness. In lagged analyses and analyses of change from pretest to posttest, self-image goals predicted decreased responsiveness to roommates, when we controlled for perceptions of roommates’ responsiveness, both pr s = −.21, both p s < .05. The association between daily self-image goals and responsiveness to roommates was not significant in the original model. Overall, these results suggest that students’ interpersonal goals predict change in their responsiveness to roommates, but not because they also perceive roommates as more or less responsive.

Does self-esteem or esteem for roommates explain these associations?

We tested whether the hypothesized processes were due simply to students’ esteem for themselves or their roommates. We reanalyzed all models in the daily, lagged-day, and change from pretest to posttest data, controlling for these covariates separately, using the analytic strategy from Study 1. Results remained unchanged in 31of 32 analyses. In the pretest to posttest model (bottom of Figure 4 ) the association between change in perceptions of roommates’ responsiveness and change in self-image goals became marginal when we controlled for change in esteem for roommates, pr = −.21, p < .08, however change in esteem for roommates did not significantly predict change in self-image goals, pr = −.10, ns. Thus, the associations tested here cannot be accounted for by self-esteem or esteem for roommates.

Does self-esteem moderate these associations?

We also tested whether each individual path in the models in Figure 4 was moderated by self-esteem (not controlling for other variables in the model). Overall, the intrapersonal process did not depend on self-esteem. Only 2 of 16 interactions were significant; self-esteem moderated the association between self-image goals and decreased responsiveness so that it was stronger with lower self-esteem in the lagged day analyses, pr = .14, p < .05 (low self-esteem, pr = −.21, p < .001, high self-esteem, pr = −.02, ns), but weaker with lower self-esteem in pretest to posttest analyses, pr = −.20, p < .05, (low self-esteem, pr = −.19, p < .05, high self-esteem, pr = −.36, p < .001). For all other moderation analyses pr s < |.12|, ns.

As in Study 1, responsiveness dynamics may differ by gender. We tested whether gender moderated each individual path (i.e., not controlling for other variables in the models) in each intrapersonal model, using the strategy described in Study 1. Gender did not moderate any of the 16 associations in Figure 4 , all pr s < |.17|, ns.

Does relationship quality lead to perceptions of roommates’ responsiveness?

It is possible that students’ relationship quality leads to changes in their perceptions of roommates’ responsiveness. To address this, we tested alternative orders of perceptions of roommate’s responsiveness and relationship quality in lagged-day and change from pretest to posttest analyses. (We did not test this alternative order in the daily data because the cross-sectional nature of those data do not speak to the plausibility of causal associations and either order would provide virtually the same result.) Change in relationship quality from Days 1 to 2 did not predict change in perceptions of roommates’ responsiveness from Days 1 to 3, pr = .06, ns. Chronic perceptions of roommates’ responsiveness averaged across 21 days predicted increased relationship quality, pr = .23, p < .05; chronic relationship quality averaged across 21 days did not predict change in perceptions of roommates’ responsiveness, pr = .10, ns. (We did not test whether change in relationship quality from pretest to posttest would predict change in perceptions of roommates’ responsiveness from pretest to posttest because this test of simultaneous change would provide the same result for either ordering.) Thus, these analyses do not support the idea that perceptions of roommates’ responsiveness are simply a function of relationship quality.

These results replicate and extend the intrapersonal process found in Study 1: goals predict change in responsiveness to roommates, which predicts increased projection of responsiveness, which predicts changes in goals and relationship quality. 10 Self-esteem and esteem for roommates did not account for these associations and they were not moderated by self-esteem. 11

Thus, goals have immediate implications for responsiveness, projection of responsiveness, goals, and relationship quality. These effects held in lagged day analyses and predicted change in goals and relationship quality across three weeks. However, these analyses do not address whether students’ goals predict their roommates’ experiences. The second phase of analyses addresses this issue.

Interpersonal Processes: Actors’ Goals and Responsiveness Predicting Partners’ Responsiveness and Relationship Quality

Phase 2 analyses test whether partners perceive actors’ responsiveness and whether these perceptions predict partners’ own goals and relationship quality. We tested an interpersonal model in which actors’ goals predict actors’ responsiveness to partners (Path A), which predicts partners’ perceptions of actors’ responsiveness (Path F), which predicts partners’ goals and relationship quality (Paths J and K, respectively). These analyses simultaneously examine the process by which partners’ goals predict actors’ goals and relationship quality (i.e., Paths G, L, D, and E). We examined evidence supporting this model within days, from day to day, and across three weeks, using the same analytic strategy described in Study 1 interpersonal analyses.

Within-day analyses support our hypotheses (see top of Figure 5 ). On days actors had higher compassionate goals, they reported being more responsive to partners; actors’ daily self-image goals were unrelated to their daily responsiveness to partners. Actors’ daily responsiveness to partners predicted partners’ higher perceptions of actors’ responsiveness, which then predicted partners’ higher relationship quality and compassionate goals, but did not predict self-image goals.

Study 2: Interpersonal (between-person) path analyses of daily, lagged-day, and pretest and posttest data. NOTE: All estimates are partial correlations; estimates in brackets

Following the logic from Study 1’s interpersonal lagged-week path model, we hypothesized an interpersonal lagged-day path model in which actors’ goals at Day 1 predict change in their responsiveness to roommates from Days 1 to 2, which predict simultaneous change in partners’ perceptions of actors’ responsiveness from Days 1 to 2, which in turn predict change in partners’ compassionate and self-image goals and relationship quality from Days 1 to 3.

Lagged-day tests of the path model support the plausibility of causal effects in our hypothesized interpersonal model (see middle of Figure 5 ). Actors’ compassionate goals on Day 1 predicted increased responsiveness to partners from Days 1 to 2; actors’ self-image goals on Day 1 predicted decreased responsiveness to partners from Days 1 to 2. Change in actors’ responsiveness to partners from Days 1 to 2 positively predicted simultaneous change in partners’ perceptions of actors’ responsiveness from Days 1 to 2, which positively predicted change in partners’ relationship quality and compassionate goals from Days 1 to 3. Change in partners’ perceptions of actors’ responsiveness from Days 1 to 2 did not predict change in partners’ self-image goals from Days 1 to 3.

Changes Across 3 Weeks

The interpersonal effects of actors’ goals accumulated over time, indirectly predicting change in partners’ relationship quality and goals over three weeks (see bottom of Figure 5 ). Actors’ chronic compassionate goals predicted increased responsiveness and actors’ chronic self-image goals predicted decreased responsiveness to partners from pretest to posttest. Change in actors’ responsiveness to partners positively predicted change in partners’ perceptions of actors’ responsiveness, which positively predicted change in partners’ relationship quality and compassionate goals from pretest to posttest, but did not predict change in partners’ self-image goals.

As in Study 1, we tested whether partners’ goals moderate their perceptions of actors’ responsiveness, whether self-esteem or esteem for roommates explain associations unique to the interpersonal model, and whether those associations are moderated by self-esteem or gender, using the same analytic strategies described in Study 1.

As in Study 1, we tested the possibility that the links between actors’ responsiveness and partners’ perceptions of actors’ responsiveness were dependent on partners’ goals. For all models in Figure 5 , we tested whether partners’ goals moderated the individual paths (i.e., not controlling for other variables in the models) between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness, using the strategy described in Study 1. Across daily, lagged, and pretest and posttest analyses, partners’ goals did not moderate this association, compassionate goals: −.09 < pr < .02, all ns; self-image goals: −.04 < pr < .02, all ns. Again, actors’ and partners’ agreement about actors’ responsiveness does not depend on partners’ compassionate or self-image goals.

To rule out the possibility that esteem for roommates or self-esteem explained associations unique to the interpersonal model, we retested the links between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness, controlling for partners’ self-esteem or esteem for roommates in separate analyses. We tested these links in all models in Figure 5 using the analytic strategy described previously. In the weekly data, we also tested whether partners’ self-esteem or esteem for roommates accounted for associations between partners’ daily perceptions of actors’ responsiveness and partners’ compassionate and self-image goals. Results remained unchanged in all 5 analyses.

We also tested whether self-esteem moderated associations between actors’ responsiveness and partners’ perceptions of actors’ responsiveness in all models and associations between partners’ perceptions of actors’ responsiveness and partners’ compassionate and self-image goals in the weekly model. None of the 5 product terms tested were significant (all pr s < |.15|, ns).

We tested whether gender moderated associations unique to the interpersonal models (i.e., paths from actors’ responsiveness to partners to partners’ perceptions of actors’ responsiveness in daily, lagged-day, and change from pretest to posttest analyses; and paths from perceptions of actors’ responsiveness to partners’ compassionate and self-image goals in daily analyses); gender did not moderate any of the 5 paths tested (all pr s < |.13|, ns).

Study 2 results strongly support our overall hypothesis that people’s goals can create their own and others’ responsiveness and relationship quality. First, we replicated and extended the intrapersonal associations found in Study 1. Again, students’ goals predicted their responsiveness to roommates, led them to project their responsiveness onto roommates, which then contributed to their goals and relationship quality. We found evidence for this process within days, from day to day, and across the study. Second, interpersonal results supported our hypothesis that students’ goals predict their responsiveness to their roommates, which lead roommates’ to feel responded to, which in turn lead to roommates’ relationship quality and goals. In contrast to Study 1, interpersonal effects in Study 2 were quite clear, suggesting that these processes occur in a more immediate time frame (daily rather than weekly), possibly because in daily reports actors and partners are more likely to base their reports on the same events.

GENERAL DISCUSSION

People who perceive others as responsive become responsive themselves and perceive their partners as more responsive, leading to high quality relationships for both the partner and the self. But what creates perceived partner responsiveness in relationships? The studies reported here explore the processes that promote or undermine responsiveness over time. We incorporate and build on two models in the responsiveness literature: an intrapersonal projection model in which responsiveness predicts perceptions of others’ responsiveness and one’s own relationship outcomes (e.g., Lemay & Clark, 2008 ), and an interactive dyadic model in which actors’ responsiveness to partners predicts partners’ perceptions of actors’ responsiveness (e.g., Reis & Shaver, 1988 ).

Indeed, our data are consistent with the hypothesis that students project their responsiveness onto their roommates. Across data sets, results consistently show that responsiveness to roommates strongly predicts perceptions of roommates’ responsiveness (i.e., projection). 12  However, they also support the hypothesis that responsiveness is a dyadic process, as suggested by Reis and Shaver (1988) – in 5 of 6 models tested, actors’ responsiveness to partners predicted partners’ increased perceptions of actors’ responsiveness. Approximately one-third of the total variance in responsiveness to roommates occurred at the dyad level (30% in Study 1 and 39% in Study 2). Just over one-third of the total variance in perceptions of roommates’ responsiveness occurred at the dyad level (34% in Study 1 and 38% in Study 2).  If responsiveness was strictly an individual projection phenomenon, then we should not find variance in responsiveness and perceptions of roommates’ responsiveness at the dyad level.  Because a portion of variance in responsiveness and perceptions of roommates’ responsiveness occurs at the dyad level, and these roommates did not have a long relationship history or self-select into their roommate relationships, the portion of variance that occurs at the dyad level likely indicates dyadic processes.  Given inconsistent findings in the literature regarding whether responsiveness is a real behavioral phenomenon, our data are important because they consistently support the hypothesis that responsiveness is a dyadic process, in addition to a process of projection.

Results from the current studies also suggest that interpersonal goals provide one avenue for creating responsiveness in relationships, initiating a self-perpetuating relation between goals and responsiveness projection and reciprocation, and demonstrate that these processes are dynamic and extend over time. We consider each of these contributions in turn.

Compassionate and Self-Image Goals Predict Responsiveness in Relationships

Although Reis and Shaver (1988) speculate that actors’ goals and motives play a role in responsiveness processes, no research that we know of has examined these associations. Our data indicate that interpersonal goals can create or undermine responsiveness in close relationships. Across two studies, when people had compassionate goals, they became more responsive to relationship partners. When people had self-image goals, they became less responsive.

Our data show that interpersonal goals, particularly compassionate goals, predict responsiveness. Reis and Shaver’s theorizing focused mainly on disclosure as the mechanism by which responsiveness is created -- actors reveal personally relevant thoughts and feelings to partners and when partners are responsive, actors perceive that responsiveness. Although empirical research supports the Reis and Shaver hypothesis that self-disclosures lead to perceptions of partners’ responsiveness ( Laurenceau et al., 1998 ; Laurenceau, Barrett, & Rovine, 2005 ; Manne et al., 2004 ), disclosure cannot explain associations between compassionate goals, responsiveness to roommates, and perceptions of roommates’ responsiveness in these data. In Study 1, disclosure did not account for 10 of the 11 associations leading to responsiveness or perceptions of roommate’s responsiveness. Thus, these data suggest that responding to partners is an alternative route to creating responsive partners, and that actors’ goals to support partners out of concern for their well-being (versus trying to control what partners think of them) can begin this process. Through their compassionate and self-image goals, people create their own relationship experiences; when they are responsive to others, people project their responsiveness onto others, perceiving them as more responsive, with consequences for people’s own relationship quality. Through their goals, people also create others’ experiences; when, because of their interpersonal goals, people are responsive to partners, partners perceive their responsiveness, which has consequences for partners’ relationship quality.

Perceptions of roommates’ responsiveness did not explain why compassionate goals predict responsiveness to roommates. In only 1 of 6 analyses across both samples did the association between compassionate goals and responsiveness drop to nonsignificance when we controlled for perceptions of roommates’ responsiveness. Thus, people with compassionate goals are not responsive simply because they reciprocate perceived responsiveness in their partners. Perceptions of roommates’ responsiveness may, however, explain why self-image goals predict lower responsiveness. Perceptions of roommates’ responsiveness accounted for the link between weekly self-image goals and responsiveness and between chronic self-image goals and change in responsiveness from pretest to posttest in Study 1. Thus, people with self-image goals are less responsive, apparently, because they perceive others as less responsive.

The association between compassionate goals and responsiveness to roommates was remarkably robust. We examined whether several potential confounds could explain the effects of compassionate goals on responsiveness to roommates. Neither support availability nor negative affect (i.e., anxiety and depression; Study 1) or self-esteem (Study 2) consistently accounted for associations between actors’ compassionate goals, responsiveness, and perceptions of roommates’ responsiveness, nor did they account for associations between actors’ responsiveness, partners’ perceptions of actors’ responsiveness, partners’ responsiveness, and partners’ goals. 13 We also examined whether negative affect in Study 1, or self-esteem in Study 2 moderated the effects of compassionate goals; they did not. The processes we describe – compassionate goals lead to increased responsiveness, which leads to both people’s increased perceptions of the others responsiveness, which then leads to changes in both people’s goals and partners’ increased reciprocated responsiveness -- cannot be attributed to, nor do they depend on, affect or feeling that support is available. Compassionate goals appear to have unique implications for, responsiveness, perceptions of others’ responsiveness, and change in people’s own and partners’ interpersonal goals and relationship quality.

In contrast, the effects of self-image goals on responsiveness to roommates were explained by perceived available support, anxiety, and depression in several analyses, although the effects were inconsistent across weekly, lagged, and pretest to posttest analyses (Study 1). These results suggest that self-image and compassionate goals relate to responsiveness to others through different processes. Self-image goals predict responsiveness through affect and feeling that support is available; compassionate goals predict responsiveness through some other process, unrelated to affect and available support.

Self-Perpetuating Associations between Goals and Responsiveness in Relationships

These studies provide strong evidence for a powerful self-perpetuating virtuous cycle from compassionate goals to responsiveness to relationship quality and subsequent goals. Across all analyses, compassionate goals predicted increased responsiveness and perceptions of roommates’ responsiveness predicted increased compassionate goals in 10 of 12 analyses. Only two findings did not support an effect of perceptions of responsiveness on compassionate goals: first, in Study 1, change in perceptions of partners’ responsiveness from pretest to posttest did not predict change in compassionate goals from pretest to posttest; second, in Study 2, change in perceptions of partners’ responsiveness from Days 1 to 2 only marginally predicted increased compassionate goals from Days 1 to 3. Both of these exceptions occurred in intrapersonal models, suggesting that projection of responsiveness alone may not always be sufficient to create this mutually reinforcing process; it may depend on partners’ responses or behaviors. Overall, these data support our hypothesis that compassionate goals perpetuate future compassionate goals in the self and others.

Of course, upward spirals of compassionate goals and responsiveness cannot continue indefinitely. Future research should address the conditions or situations that cause responsiveness dynamics to asymptote or reverse. We speculate that responsiveness uses attentional resources. Even when people want to support others, they may not always have the attentional capacity to be sensitive to others’ emotional states. Thus, attentional demands may interrupt the upward spiral of compassionate goals and responsiveness. Furthermore, demands on partners’ attention and partners’ lack of motivation to be supportive may interrupt the interpersonal aspects of this upward spiral.

The present studies provide weaker evidence for a vicious cycle from self-image goals to responsiveness. Self-image goals predicted decreased responsiveness to roommates, but perceptions of responsiveness did not predict subsequent self-image goals. However, self-image goals may indirectly have consequences for future compassionate goals; self-image goals contribute to decreased responsiveness, which ultimately leads to decreased compassionate goals for both partners. Thus, the present studies more strongly indicate the benefits of one person’s compassionate goals for both people’s relationship quality than the detriments of self-image goals.

Responsiveness Processes are Dynamic and Extend over Time

The present studies demonstrate the dynamic nature of associations among goals, responsiveness reciprocation, and relationship quality over time. Perceptions of partners’ responsiveness vary from interaction to interaction (e.g., Laurenceau et al., 1998 ), but little research has investigated whether and how these discrete interactions influence long-term relationship experiences. Both the intrapersonal and the interpersonal responsiveness processes in the present studies occurred within a day, and had lingering consequences over several days, suggesting the power of interpersonal goals to create lasting effects on responsiveness. Goals and responsiveness also have cumulative consequences over a semester. Roommates’ chronic goals over weeks and months predicted long-term changes in both people’s responsiveness and perceptions of the others’ responsiveness, with consequences for both people’s goals and relationship quality. Because they operate over the long-term, compassionate goals may create a foundation for enduring, sustainable high-quality relationships.

Examining these processes over different time periods also allowed us to rule out alternative explanations. Within-day and -week analyses suggest that these processes fluctuate within people and are not due to individual differences. For example, on days and weeks when people have higher compassionate goals, they are more responsive to roommates; on days and weeks when those same people have higher self-image goals they are less responsive to roommates. Individual differences (e.g., chronic goals, gender, or social desirability) cannot account for daily and weekly within-person fluctuations in these processes. The lagged analyses and tests of change from pretest to posttest indicate the plausibility of causal paths in these models. Overall, these data do not allow us to rule out causal paths from interpersonal goals to responsiveness to both people’s perceptions of partners’ responsiveness to both people’s compassionate goals and relationship quality in daily assessment across 3 weeks. Tests of change across three weeks and a semester also speak to the cumulative effect of these processes over time. Students’ chronic compassionate goals accumulate to predict long-term changes in responsiveness between roommates. Taken together, these data suggest that interpersonal goals may provide a potential point of intervention – changing people’s daily and weekly goals toward others can change both people’s experiences that day or week, and the following days and weeks, with potential consequences for long-term goal and relationship change.

As a methodological note, results of these studies suggest that the interpersonal processes captured by diary methods may depend on the length of diary intervals. Our data show that the links between actors’ responsiveness to partners and partners’ perceptions of actors’ responsiveness differ depending on whether diaries were completed daily or weekly. In daily and lagged-day analyses (Study 2), roommates showed strong agreement on actors’ responsiveness, suggesting that roommates were drawing from similar events in recalling and reporting their daily relationship experiences. However, we did not find strong actor-partner agreement in weekly assessments (Study 1). Specifically, in lagged-week analyses actors’ Week 1 self-reported responsiveness did not predict change in partners’ perceptions of actors' responsiveness from Weeks 1 to 2. Further, in pretest to posttest change analyses, the association between actors’ responsiveness and partners’ perceptions of actors’ responsiveness became nonsignificant when we controlled for change in partners’ perceived available support. These data suggest that participants report on similar events and experiences in daily reports, however there may be a disconnect between what actors report and what partners perceive when reporting their experiences over longer periods of time (in this case 1 week). Actors may recall their own intentions to be responsive to partners, but these may be independent of partners’ accounts, which may be based on roommates’ supportive behaviors rather than perceptions of roommates’ responsiveness. Thus, daily measures may capture each person’s experiences, but weekly measures may capture each person’s most salient or personally relevant experiences, which may differ for each relationship partner.

Responsiveness and Social Support

Responsiveness and support are theoretically distinct constructs; responsiveness involves tuning into and reacting to others’ states, whereas support involves providing help, information, and assistance, but not necessarily attending to others’ emotional and psychological needs. In the current data, only 2 of 17 significant associations in Figure 2 and Figure 3 could be explained by support, providing empirical evidence that support and responsiveness, although related, are distinct constructs. Previous work also suggests a distinction between support and responsiveness. For example, Collins and colleagues ( Collins, Guichard, Ford, & Feeney, 2006 ) propose that effective caregiving (i.e., support) involves both responsiveness and sensitivity to partners’ signals. Caregiving lacking either responsiveness or sensitivity to partners’ signals is ineffective, consistent with our distinction between responsiveness and support. Whereas effective caregiving predicts improved perceived relationship quality, mood, and self-esteem for recipients, ineffective caregiving does not ( Feeney, 2004 ; Feeney & Collins, 2003 ).

The distinction between support and responsiveness raises many questions about the nature and functions of support versus responsiveness. For example, is responsiveness a subset of support or vice versa? What consequences does each have for relationship outcomes and functioning, and for both relationship partners’ mental and physical health? Theoretical and empirical distinctions between responsiveness and support may clarify their different effects and perhaps resolve issues such as why support is sometimes unhelpful.

These studies are consistent with the hypotheses that interpersonal goals can promote or undermine reciprocal responsiveness between relationship partners and that process can lead to both partners’ relationship quality and interpersonal goals. However, the generalizability of these findings to other types of relationships has yet to be established. The close relationships in these studies were relatively new and the processes shown here were relatively unaffected by relationship history or self-selection into relationships, making them ideally suited for examining relationship processes because they rule out several alternative explanations. We expect that these responsiveness dynamics apply to other types of relationships, for example, between romantic, family, or work dyads. Factors such as relationship history or being in a high-stakes relationship may attenuate or amplify the effects of goals. For example, in relationships with long histories, responsiveness may be affected more strongly by past events rather than by current goals or perceptions of partners’ responsiveness. In high-stakes relationships, for example between firefighters whose lives depend on coworkers, the effects of goals may be amplified because the responsiveness of others to current needs may have life or death consequences. Of course, these are empirical questions to be addressed in future studies.

The nature of the samples may also limit the generalizability of the findings. Both samples were largely female (75% in Study 1 and 71% in Study 2). Despite the relatively small number of men, we tested whether gender moderated the associations found in both studies. Few associations differed by gender relative to the number of tests conducted -- only 2 of 40 product terms between the two samples were significant. Furthermore, results of these moderation analyses were inconsistent between samples – the two paths that were moderated by gender in Study 1 were not consistent between models and did not replicate in Study 2. Additional research with larger samples involving more males would clarify the generalizability of the findings across genders.

Because goals and responsiveness were not manipulated, these studies do not enable us to specify causal relationships among variables. However, several aspects of the results are consistent with causal associations among the variables. Temporal associations among variables in the lagged-week and lagged-day analyses and tests of change from pretest to posttest are consistent with causal links in our path models. Although these effects could be due to unmeasured variables, we ruled out disclosure, social support, self-esteem, and esteem for roommates as alternative explanations. Both studies also provided evidence of an interpersonal process in which actors’ goals and responsiveness lead to changes in partners’ perceptions, behaviors, and goals. Overall, these results support the plausibility of a causal effect of interpersonal goals on responsiveness to roommates, reciprocal associations between people’s responsiveness, and causal effects of perceived partners’ responsiveness on relationship quality and interpersonal goals.

Finally, although exploratory and confirmatory factor analyses across samples suggest that compassionate goals, responsiveness to roommates, perceptions of roommates’ responsiveness, and relationship quality are separate constructs, examination of zero-order correlations in Table 1 and Table 3 suggest a relatively high degree of overlap between these variables. This may lead some to question our findings. However, it is important to remember that our analyses assess change in these constructs over time . If our goals, responsiveness, and relationship quality measures did, in fact, tap the same construct, tests of change would not be as strong or consistent across analyses and samples. At best, we would expect instability in our effects (e.g., compassionate goals would carry significant variance in some cases, but not others).

That said, we acknowledge this limitation and suggest that future research include more specific measures of responsiveness to others and perceptions of others’ responsiveness. In this research, we used standard measures of responsiveness, which included items assessing both participants’ intentions (i.e., what they were trying to do) and behaviors (i.e., what they actually did). Given that our goals measure captures intentions, it is not surprising that these scales were strongly correlated. Future research should distinguish between enacted responsiveness and responsive intentions.

Additional Theoretical Mechanisms

Overall, results from these two studies support our argument that interpersonal goals lead to both people’s relationship quality through the projection of responsiveness and responsiveness dynamics between relationship partners. However, these results generate additional questions, including how interpersonal goals lead to responsiveness and how responsiveness is communicated between partners. We suggest that goals translate to responsiveness through affect and constructive and destructive beliefs about relationship problems.

When people have self-image goals, they report feeling afraid and confused when interacting with their roommates (e.g., Crocker & Canevello, 2008 ) and endorse unconstructive beliefs about relationships (e.g,. believing that people should take care of themselves, even at the expense of others and blaming the other person for relationship problems; Canevello & Crocker, 2009 ; Crocker & Canevello, 2008 ). We suggest that when people have compassionate goals, fear, confusion, and unconstructive beliefs about relationship problems render them insensitive to partners’ needs, resulting in decreased responsiveness.

When people have compassionate goals, they report feeling caring and connected when they interact with their roommates, (e.g., Crocker & Canevello, 2008 ) and they endorse constructive beliefs about relationship problems (e.g., believing that people should take care of each other and sharing responsibility for relationship problems; Canevello & Crocker, 2009 ; Crocker & Canevello, 2008 ). We suggest that when people have compassionate goals, caring, connection, and constructive beliefs about relationship problems lead to a heightened sensitivity to partners’ needs, resulting in increased responsiveness.

We also suggest that responsiveness is communicated to relationship partners through responsive support behaviors (e.g., Collins & Feeney, in press ). That is, partners’ perceptions of actors’ responsiveness should depend on the match between actors’ behaviors and partners’ needs. When actors’ responsive behaviors attend to partners’ needs, partners should report higher perceptions of actors’ responsiveness. When actors’ responsive behaviors do not address supports sought by partners, partners should report lower perceptions of actors’ responsiveness.

Perceived partner responsiveness is a core feature of close, satisfying relationships and can be created in at least two ways: through an intrapersonal process of projecting own responsiveness onto others or through an interpersonal process whereby people’s reported responsiveness is perceived by partners. But how does responsiveness originate? Our findings suggest that people’s interpersonal goals (to either support others or construct and maintain desired images of the self) can initiate or inhibit responsiveness and its projection and reciprocation in relationships, which predicts relationship quality and reinforces interpersonal goals for both relationship partners. In light of these data, we suggest that people not only can create the types of relationships that they want - those characterized by high responsiveness, and consequently, higher quality, but they also can create responsive, high quality relationships for others.

Acknowledgments

The project described was supported by Grant Number R01MH058869 from the National Institute of Mental Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.

We are grateful to Samir Adhia, Laura Diskin, George Payapilly, Cristina Popa, Robby Uppal, Megan Villwock, and Claire Woodward for their assistance with data collection and to Riia Luhtanen, Kate Oddi, Juliana G. Breines, Dominik Mischkowski, and Mary Liu for their invaluable assistance in both of these studies. We are also grateful to our friends at Learning as Leadership for inspiring our research on self-image and compassionate goals.

The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other third parties. The published version is available at www.apa.org/journals/psp

Similarly, being responsive does not necessitate having compassionate goals. People may be responsive to others without intending to be caring or compassionate. For example, Cathy may be responsive to Dan, not because she has compassionate goals, but because she wants Dan to like her. She may make Dan feel comfortable about himself and valued as a person, be sensitive to his feelings, and understand his concerns, but Cathy may not have compassionate goals to make a positive difference in Dan’s life or avoid being selfish or self-centered. Although compassionate goals are distinguishable from responsiveness, we hypothesize that when people have compassionate goals, they typically are responsive.

2 Of the remaining five percent of respondents, one pair completed the pretest and 9 weekly surveys; two pairs completed the pretest, posttest, and 5 weekly surveys; and three pairs completed the pretest and less than 6 weekly surveys.

3 Participants reported that completing the weekly surveys did not strongly affect their reports or their roommate relationships during Study 1. In the posttest measure, students rated the influence of the weekly records on scales ranging from 1 ( not at all ) to 7 ( very much ). Although we do not have a control group for comparison, participants did not report that it was particularly difficult to complete the weekly surveys ( M = 3.58) and felt that their weekly surveys were accurate ( M = 5.33). Completing the records did increase how much they thought about their roommates and relationships ( M = 4.34) and positive thoughts about the relationships ( M = 3.35). However, they did not report that completing the surveys affected their behavior ( M = 2.61) or the occurrence of negative ( M = 1.94) or positive ( M = 2.67) events between roommates.

4 Although several authors provide guidelines for centering in multilevel models (e.g., Kreft & de Leeuw, 1998 ; Raudenbush & Bryk, 2002 ), they also discourage “thoughtless application” of these guidelines ( Enders & Tofighi, 2007 , p. 136) and, instead, encourage centering strategies based on specific substantive research questions.

5 We examined whether compassionate and self-image goals interacted to predict responsiveness to roommates. Weekly goals did not interact to predict weekly responsiveness or change in responsiveness to the following week, both pr s < |.04|, ns. Chronic goals did interact to predict change in responsiveness to roommates from pretest to posttest, pr = .15, p < .05, such that the association between chronic compassionate goals and increased responsiveness to roommates was stronger with lower self-image goals, pr = .59, p < .001, compared to higher self-image goals pr = .58, p < .001.

6 Previous data from our lab suggests that social desirability positively predicts chronic compassionate goals and negatively predicts chronic self-image goals ( Crocker & Canevello, 2008 , Study 1). In Study 1, pretest social desirability positively correlated with chronic self-image goals, r = .23, p < .01, and negatively correlated with chronic compassionate goals, r = −.22, p < .01. Importantly, when we regressed posttest outcomes on pretest social desirability and pretest outcome variables, social desirability did not predict change in responsiveness to roommates ( pr = .09, ns), nor did it predict change in perceptions of roommates’ responsiveness ( pr = .05, ns). Thus, although social desirability is associated with interpersonal goals, it cannot account for associations between goals and changes in outcomes from pretest to posttest. Social desirability also cannot account for within week analyses because person centered predictors remove the influence of individual differences. Finally, lagged analyses test whether goals on a particular week predict changes in outcomes the following week. Because social desirability is a stable personality factor, we see no reason why it would explain the lagged associations between goals and outcomes.

7 We tested alternative explanations for associations unique to the interpersonal models in Figure 3 . Results for other paths are redundant with test of covariates in the intrapersonal models in Figure 2 .

8 Participants reported that completing daily surveys did not strongly affect their reports or their roommate relationships during Study 2. As in Study 1, students rated the influence of the daily records on scales ranging from 1 ( not at all ) to 7 ( very much ) in the Study 2 posttest measure. Again, we do not have a control group for comparison, but participants did not report that it was particularly difficult to complete the weekly surveys ( M = 3.62) and felt that their weekly surveys were accurate ( M = 5.16). Completing the records did increase how much they thought about their roommates and relationships ( M = 4.88) and positive thoughts about the relationships ( M = 3.72). However, although they reported that that completing the surveys affected their behavior somewhat ( M = 3.19), they did not affect the occurrence of negative ( M = 2.01) or positive ( M = 2.95) relationship events.

9 Compassionate goal items differ between studies because Study 2 preliminary factor analyses suggested that we include “be aware of the impact my behavior might have on my roommates’ feelings,” and “avoid doing anything that would be harmful to my roommate.” We included “be supportive of my roommate” and “make a positive difference in my roommates’ life” in the compassionate goals measure in Study 1 because in factor analyses, it loaded strongly with other compassionate goal items and did not load with responsiveness items. Because factor analyses in Study 2 suggested that it loaded equally with compassionate goal and responsiveness items, we removed this item in Study 2 to reduce overlap between measures. Self-image goal items differ between studies because preliminary factor analyses suggested different solutions. Importantly, although the goals measures differ slightly between studies, they provide consistent results.

10 We examined whether compassionate and self-image goals interacted to predict responsiveness to roommates. Goals did not interact to predict responsiveness or change in responsiveness in daily, lagged, or pretest to posttest analyses, all pr s < |.10|, ns.

11 In Study 2, pretest social desirability does not predict chronic self-image ( r = .13, ns) or compassionate goals ( r = −.05, ns). Importantly, when we regress posttest outcomes on pretest social desirability and pretest outcome variables, social desirability does not predict change in responsiveness to roommates, perceptions of roommates’ responsiveness, relationship quality, compassionate goals, or self-image goals, −.05 < prs < = .05, all ns. Again, social desirability also cannot account for within week analyses because person centered predictors remove the influence of individual differences, and because social desirability is a stable personality factor, we see no reason why it would explain the lagged associations between goals and outcomes.

12 We also tested whether compassionate goals moderate projection (i.e., the relation between A’s responsiveness and A’s perceptions of Ps’ responsiveness). In Study 1 weekly analyses, A’s compassionate goals moderated this association, pr = −.07, p <.05, such that the association was stronger for lower compassionate goals, pr = .38, p <.001, than higher compassionate goals, pr = .28, p <.001. In Study 2 daily analyses, the product between A’s compassionate goals and A’s responsiveness was marginal, pr = −.09, p <.07. Again, the association was stronger for lower compassionate goals, pr = .63, p <.001, than higher compassionate goals, pr = .55, p <.001. Thus, these data are consistent with the hypothesis that goals moderate projection, such that projection is stronger when people have lower compassionate goals. However, they do not suggest that people never project when they have compassionate goals. They simply project less.

13 It is important to note that support accounts for the association between change in actors’ responsiveness to partners and change in partners’ perceptions of actors’ responsiveness from pretest to posttest. This result may indicate that partners detect responsiveness through supportive behavior. However, we hesitate to overstate or over-interpret this finding because 1) it did not replicate in within-week or lagged-week analyses and 2) we did not specifically test mediation.

Contributor Information

Amy Canevello, Research Center for Group Dynamics, Institute for Social Research, University of Michigan.

Jennifer Crocker, Research Center for Group Dynamics, Institute for Social Research, and Department of Psychology, University of Michigan.

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