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• Operationalisation | A Guide with Examples, Pros & Cons

Operationalisation | A Guide with Examples, Pros & Cons

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

Operationalisation means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.

Through operationalisation, you can systematically collect data on processes and phenomena that aren’t directly observable.

• Self-rating scores on a social anxiety scale
• Number of recent behavioural incidents of avoidance of crowded places
• Intensity of physical anxiety symptoms in social situations

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Table of contents

Why operationalisation matters, how to operationalise concepts, strengths of operationalisation, limitations of operationalisation, frequently asked questions about operationalisation.

In quantitative research , it’s important to precisely define the variables that you want to study.

Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods. Operationalisation reduces subjectivity and increases the reliability  of your study.

Your choice of operational definition can sometimes affect your results. For example, an experimental intervention for social anxiety may reduce self-rating anxiety scores but not behavioural avoidance of crowded places. This means that your results are context-specific and may not generalise to different real-life settings.

Generally, abstract concepts can be operationalised in many different ways. These differences mean that you may actually measure slightly different aspects of a concept, so it’s important to be specific about what you are measuring.

If you test a hypothesis using multiple operationalisations of a concept, you can check whether your results depend on the type of measure that you use. If your results don’t vary when you use different measures, then they are said to be ‘robust’.

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There are three main steps for operationalisation:

• Identify the main concepts you are interested in studying.
• Choose a variable to represent each of the concepts.
• Select indicators for each of your variables.

Step 1: Identify the main concepts you are interested in studying

Based on your research interests and goals, define your topic and come up with an initial research question .

There are two main concepts in your research question:

• Social media behaviour

Step 2: Choose a variable to represent each of the concepts

Your main concepts may each have many variables , or properties, that you can measure.

For instance, are you going to measure the  amount of sleep or the  quality of sleep? And are you going to measure  how often teenagers use social media,  which social media they use, or when they use it?

• Alternate hypothesis: Lower quality of sleep is related to higher night-time social media use in teenagers.
• Null hypothesis: There is no relation between quality of sleep and night-time social media use in teenagers.

Step 3: Select indicators for each of your variables

To measure your variables, decide on indicators that can represent them numerically.

Sometimes these indicators will be obvious: for example, the amount of sleep is represented by the number of hours per night. But a variable like sleep quality is harder to measure.

You can come up with practical ideas for how to measure variables based on previously published studies. These may include established scales or questionnaires that you can distribute to your participants. If none are available that are appropriate for your sample, you can develop your own scales or questionnaires.

• To measure sleep quality, you give participants wristbands that track sleep phases.
• To measure night-time social media use, you create a questionnaire that asks participants to track how much time they spend using social media in bed.

After operationalising your concepts, it’s important to report your study variables and indicators when writing up your methodology section. You can evaluate how your choice of operationalisation may have affected your results or interpretations in the discussion section.

Operationalisation makes it possible to consistently measure variables across different contexts.

Scientific research is based on observable and measurable findings. Operational definitions break down intangible concepts into recordable characteristics.

Objectivity

A standardised approach for collecting data leaves little room for subjective or biased personal interpretations of observations.

Reliability

A good operationalisation can be used consistently by other researchers. If other people measure the same thing using your operational definition, they should all get the same results.

Operational definitions of concepts can sometimes be problematic.

Underdetermination

Many concepts vary across different time periods and social settings.

For example, poverty is a worldwide phenomenon, but the exact income level that determines poverty can differ significantly across countries.

Reductiveness

Operational definitions can easily miss meaningful and subjective perceptions of concepts by trying to reduce complex concepts to numbers.

For example, asking consumers to rate their satisfaction with a service on a 5-point scale will tell you nothing about why they felt that way.

Lack of universality

Context-specific operationalisations help preserve real-life experiences, but make it hard to compare studies if the measures differ significantly.

For example, corruption can be operationalised in a wide range of ways (e.g., perceptions of corrupt business practices, or frequency of bribe requests from public officials), but the measures may not consistently reflect the same concept.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

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Pritha Bhandari

What is operationalization?

Last updated

5 February 2023

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Operationalization is the process of turning abstract concepts or ideas into observable and measurable phenomena. This process is often used in the social sciences to quantify vague or intangible concepts and study them more effectively. Examples are emotions and attitudes.

In this article, we will look at operationalization’s definition, benefits, and limitations. We will also provide a step-by-step guide on how to operationalize a concept, including examples and tips for choosing appropriate indicators.

• Defining operationalization

Operationalization is the process of defining abstract concepts in a way that makes them observable and measurable.

For example, suppose a researcher wants to study the concept of anxiety. They might operationalize it by measuring anxiety levels using a standardized questionnaire or by observing physiological changes, like increased heart rate.

Operationalization is mainly a social sciences tool that is applied in many other disciplines. It allows many unquantifiable concepts in these fields to be directly measured, enabling researchers to study and understand them with more accuracy.

• Why does operationalization matter?

As a qualitative researcher, accurately defining the types of variables you intend to study is vital. Transparent and specific operational definitions can help you measure relevant concepts and apply methods consistently.

Here are a few reasons why operationalization matters:

Improved reliability and validity. Researchers can ensure that their results are more reliable and valid when they clearly define and measure variables. This is especially important when comparing results from different studies, as it gives researchers confidence that they are measuring the same thing.

Enhanced objectivity: Operationalization helps reduce subjectivity in research by providing clear guidelines for measuring variables. This can help minimize bias and lead to more objective results.

Better decision-making. Operationalization allows researchers to collect and analyze quantifiable data . This can be useful for making informed decisions in various settings. For example, operationalization can be used to assess group or individual performance in the workplace, leading to improved productivity and execution.

Enhanced understanding of abstract concepts. Operationalizing abstract concepts helps researchers study and understand them more effectively. This can lead to new insights and a deeper understanding of complex phenomena.

Operationalization can reduce the possibility of research bias, minimize subjectivity, and enhance a study’s reliability.

• How to operationalize concepts

Researchers can operationalize abstract concepts in different ways. They will need to measure slightly varying aspects of a concept, so they must be specific about what they are measuring.

Testing a hypothesis using multiple operationalizations of an abstract concept allows you to analyze whether the results depend on the measure type you use. Your results will be labeled “robust” if there’s a lack of variance when using different measures.

The three main steps of operationalization are:

1. Identifying the main concepts you are interested in studying

Begin by defining your research topic and proposing an initial research question . For example, “What effects does daily social media use have on young teenagers’ attention spans?” Here, the main concepts are social media use and attention span.

2. Choosing variables to represent each concept

Each main concept will typically have several measurable properties or variables that can be used to represent it.

For example, the concept of social media use has the following variables:

Number of hours spent

Frequency of use

Preferred social media platform

The concept of attention span has the following variables:

Quality of attention

Amount of attention span

You can find additional variables to use in your study. Consider reviewing previous related studies and identifying underused or relevant variables to fill gaps in the existing literature.

3. Select indicators to measure your variables

Indicators are specific methods or tools used to numerically measure variables. There are two main types of indicators: objective and subjective.

Objective indicators are based on external, observable data, such as scores on a standardized test. You might use a standardized attention span test to measure the variable “amount of attention span.”

Subjective indicators are based on self-reported data, such as questionnaire responses. You might use a self-report questionnaire to measure the variable “quality of attention.”

Choose indicators that are appropriate for the variables you are studying that will provide accurate and reliable data.

Once you have operationalized your concepts, report your study variables and indicators in the methodology section. Evaluate how your operationalization choice may have impacted your results or interpretations under the discussion section.

• Strengths of operationalization

Operationalizing concepts in research allows you to measure variables across various contexts consistently. Below are the strengths of operationalization for your research purposes:

Objectivity

Data collection using a standardized approach reduces the chance and opportunity for biased or subjective observation interpretation. Operationalization provides clear guidelines for measuring variables, which allows you to interpret observations objectively.

Scientific research relies on observable and measurable findings. Operationalization breaks down abstract, unmeasurable concepts into observable and measurable elements.

Reliability

A good operationalization increases high replicability odds by other researchers. Clearly defining and measuring variables helps you ensure your results are reliable and valid. This is especially important when comparing results from different studies, as it gives you confidence that you’re measuring the same thing.

Better decision-making

Operationalization allows researchers to collect and analyze quantifiable data. It can aid informed decision-making in various settings. For example, operationalization can be used to assess group or individual performance in the workplace, leading to improved productivity and performance.

• Limitations of operationalization

Operationalization has many benefits, but it also has some limitations that researchers should be aware of:

Measurement error

Operationalization relies on the use of indicators to measure variables. These can be subject to measurement errors. For example, response bias can occur with self-reported questionnaires, and the concept being measured may not be accurately captured.

The Mars Climate Orbiter failure is an example of the effects of measurement errors. The expensive satellite disappeared somewhere above Mars, leading to a critical mission failure.

The failure occurred because of a massive error in the thrust force calculation. Engineering teams used different standardized measurements (metric and imperial) in their calculations. This non-standardization of units resulted in the loss of hundreds of millions of dollars and several wasted years of planning and construction.

Limited scope

Operationalization is limited to the specific variables and indicators chosen by the researcher. This issue is further compounded by the fact that concepts generally vary across different time periods and social settings. This means that certain aspects of a concept may be overlooked or captured inaccurately.

Reductiveness

It is relatively easy for operational definitions to miss valuable and subjective concept perceptions by attempting to simplify complex concepts to mere numbers.

Careful consideration is necessary

Researchers must carefully consider their operational definitions and choose appropriate indicators to measure their variables accurately. Failing to do so can lead to inaccurate or misleading results.

For instance, context-specific operationalization can validate real-life experiences. On the other hand, it becomes challenging to compare studies in case the measures vary greatly.

• Examples of operationalization

Operationalization is used to convert abstract concepts into observable and measurable traits.

For example, the concept of social anxiety is virtually impossible to measure directly, but you can operationalize it in different ways.

Using a social anxiety scale to self-rate scores is one such way. You can also measure the total incidents of recent behavioral occurrences related to avoiding crowded places. Observing and measuring the levels of physical anxiety symptoms in almost any social situation is another option.

The following are more examples of how researchers might operationalize different concepts:

Concept: happiness

Variables: life satisfaction, positive emotions, negative emotions

Indicators: self-report questionnaire, daily mood diary, facial expression analysis

Concept: intelligence

Variables: verbal ability, spatial ability, memory

Indicators: standardized intelligence test, reaction time tasks, memory tests

Concept: parenting styles

Variables: authoritative, authoritarian, permissive, neglectful

Indicators: parenting style questionnaire, observations of parent–child interactions, parent-reported child behavior

Operationalization can also be used to conduct research in a typical workplace setting.

• Applications of operationalization

Operationalization can be applied in a range of situations, including research studies, workplace performance assessments, and decision-making processes.

Here are a few examples of how operationalization might be used in different settings:

Research studies: It is commonly used in research studies to define and measure variables systematically and objectively. This allows researchers to collect and analyze quantifiable data that can be used to answer research questions and test hypotheses.

Workplace performance assessments: Operationalization can be used to assess group or individual performance in the workplace by defining and measuring relevant variables such as productivity, efficiency, and teamwork. This can help identify areas for improvement and increase overall workplace performance.

Decision-making processes: It can aid informed decision-making in various settings by defining and measuring relevant variables. For example, a business might use operationalization to compare the costs and benefits of different marketing strategies or to assess the effectiveness of employee training programs.

Business: Operationalization can be used in business settings to assess the performance of employees, departments, or entire organizations. It can also be used to measure the effectiveness of business processes or strategies, such as customer satisfaction or marketing campaigns.

Health: It can be used in the health field to define and measure variables such as disease prevalence, treatment effectiveness, and patient satisfaction. Personnel and organizational performance can also be measured through operationalization.

Education: Operationalization can be used in education settings to define and measure variables such as student achievement, teacher effectiveness, or school performance. It can also be used to assess the effectiveness of educational programs or interventions.

By defining and measuring variables in a systematic and objective way, operationalization can help researchers and professionals make more informed decisions, improve performance, and better understand complex concepts.

What is the process of operationalization in research?

Operationalization is the process of defining abstract concepts through measurable observations and quantifiable data. It involves identifying the main concepts you are interested in studying, choosing variables to represent each concept, and selecting indicators to measure those variables.

Operationalization helps researchers study abstract concepts in a more systematic and objective way, improving the reliability and validity of their research and reducing subjectivity and bias.

What does it mean to operationalize a variable?

Operationalizing a variable involves clearly defining and measuring it in a way that allows researchers to collect and analyze quantifiable data.

It typically involves selecting indicators to measure the variable and determining how the data will be interpreted.

Operationalization helps researchers measure variables with more accuracy and consistency, improving the reliability and validity of their research.

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ASSOCIATION OF THE UNITED STATES ARMY

Voice for the army - support for the soldier, the value of broadening assignments.

While there is a burgeoning body of literature examining the skills necessary for officer success in joint, interagency, intergovernmental and multinational environments, less has been written about the value of broadening experiences for performing in subsequent operational assignments within the Army at all echelons.

In our careers, broadening experiences outside the Army were ideal preparation for key developmental assignments at both company and field grade levels. Broadening is also about building better leaders within the Army, not solely about preparing officers to excel when dealing with those outside the Army, whether they be uniformed members of other services, civil servants, or representatives of other countries’ militaries and governments.

With the return of officer separation boards and lower promotion rates, many junior officers may view broadening opportunities as too risky to their careers, opting to pursue traditional developmental jobs in their basic branches within the Army. We argue that such views are shortsighted. Some of the best preparation for tough key developmental jobs in the Army can come from exposure to communities outside the service.

We offer the following 10 ways we were better prepared for key developmental jobs because of broadening experiences:

1. Asking good questions. Often the most important skill a leader can contribute to guiding subordinates is asking the right questions to generate the understanding of a problem or task before solving or accomplishing it. Broadening experiences, more than prior operational assignments, expose Army leaders to communities, such as civilian academia, that spend as much time formulating questions as answering them.

2. Being comfortable not knowing everything. While the commander of a theater military intelligence company may not perfectly understand the nuances of all 12 intelligence MOSs within the company, even the purest rifle company contains more than 10 individual MOSs and relies on the support of countless others. Furthermore, the past several years of war have demonstrated that during overseas deployments, few units operate without “enablers” from joint, interagency, intergovernmental and multinational entities.

The gap in training and expertise among leaders, their subordinates and their partners will only grow wider as the world grows increasingly complex. Proper broadening offers experiences wherein leaders learn to work without much training or knowledge of their environment—whether in a foreign country or a commercial company—gaining a level of comfort with “not knowing” that can serve them well when charged with leading diverse formations.

3. Emphasizing empirical evidence over anecdotes. Leaders must always guard against well-intentioned teammates and subordinates who offer compelling, often passionate anecdotes as evidence to support the adoption of a particular course of action. Leaders are especially vulnerable during transitions, when subordinates may be tempted to seek a quick decision from the new person at the helm. At the same time, even cohesive and established teams must be wary of pitfalls.

Implicit trust in our teammates that has been forged over time should not preclude establishing a culture that values the gathering of empirical observations to support decisionmaking. Our graduate education in the social sciences not only reinforced the general value of skepticism, but also instilled in us a persistent need to back up assertions with empirical data—and to expect the same from those with whom we serve.

4. Receiving candid feedback. While the Army has taken steps to incorporate 360-degree assessments into leader-development efforts, it has long faced criticism for an inflated evaluation system. We contend that the most candid, critical, substantive feedback the three of us have received was in graduate school, not the Army. In fact, during one of the authors’ graduate studies, a professor admitted he had to abandon peer assessments for group projects in classes that had concentrations of Army officers, because the Army officers rated their peers—often, other Army officers—as excellent, with little to no room for improvement. Broadening experiences that truly assess performance and then help guide individuals forward are evocative of learning organizations that place a high premium on continuous assessment.

5. Seeking a diversity of viewpoints. While the need for good order and discipline necessitates a healthy degree of deference to those in positions of authority, there is a danger of such top-down thinking resulting in groupthink and confirmation bias. The Army ethos does not instill the impulse to seek input from subordinates in as holistic a way as do the less hierarchical contexts of certain broadening assignments, such as those in civilian academia or joint, interagency, intergovernmental and multinational positions.

6. Valuing consensus building. While leaders in Army units have authority by virtue of their rank and position, the truth is that it is easier to lead soldiers who understand and believe in their assigned mission. And soldiers are more likely to buy in to an organizational vision if they feel their voices are heard. Even within purely Army contexts, mission success often depends on coordination with organizations outside a given unit and well-defined lines of authority. Broadening assignments outside the military may offer valuable perspective on how consensus building can lead to effective action outside defined chains of authority.

7. Expanding sources of authority. By virtue of our positions, each of us exercised considerable authority over hundreds of soldiers. Yet we also recognized that having to invoke positional authority to compel others to do something was probably a last resort. Deference to authority and respect for the chain of command are absolute necessities in the Army, but our most significant accomplishments were often realized through creative, collaborative endeavors. Broadening experiences that offer immersion in the corporate world or graduate degrees in business offer Army leaders unique insights into leading organizational change.

8. Appreciating process as much as outcome. The Army uses the military decisionmaking process (MDMP) as its primary decisionmaking framework. MDMP is a useful, time-tested and effective tool for leading units at various echelons, but it is not alone in the world as a decisionmaking framework. When Army officers serve outside of the Army, whether as students in civilian graduate schools or “embeds” in other departments of government, they are exposed to alternate decisionmaking and management processes. That contrast itself is an education to Army leaders. It makes more apparent MDMP’s strengths and weaknesses, and equips leaders to account for those characteristics in leading their own units through MDMP.

9. Strengthening ties beyond the Army. Whether in garrison or deployed, tactical and operational Army units are more successful when they leverage the capabilities of organizations beyond the Army. Particularly in an era of declining budgets, leaders with contacts beyond the Army can create garrison developmental opportunities for soldiers within their formations, including cultural awareness training with subject-matter experts or sharing broadening experiences in leader professional development contexts. Likewise, while deployed, having an understanding of the organizational culture of other government agencies or nongovernmental organizations can go a long way in minimizing miscommunication.

10. Communicating more broadly. In facing modern security challenges, the U.S. Army rarely deploys alone. Almost always deployed with joint forces, the Army often deploys with partner-nation militaries, the support of other U.S. and partner-nation government agencies, or partners with international and nongovernmental organizations as a key component of accomplishing the mission. Additionally, when facing hybrid or insurgent threats, military leaders often must be able to communicate with local populations and their leaders.

The ability to communicate effectively with this broad array of audiences is often vital to mission accomplishment. This ability can be taught to a degree, but it is often learned best through experience and practice. Broadening experiences out of uniform, embedded in foreign countries or with other government agencies offer a directly applicable opportunity for leaders to develop such skills.

The general direction of the Army regarding the importance of broadening is one we fully support. We must not, however, allow bureaucratic incentives to label every non-key developmental billet as broadening to dilute the intent of the Army’s initiative into something so broad as to be devoid of meaning.

All experiences are valuable, but not all experiences outside of our core competencies are equally broadening. If we are going to institutionalize and incentivize broadening across all ranks, leaders at all levels must encourage subordinates to seek out both the most challenging key developmental jobs and most challenging broadening assignments.

What Is Broadening?

Currently, there is not a uniformly held doctrinal definition of broadening. The closest is the recent revision to Department of the Army Pamphlet 600-3, but the difficulty with this definition is that little is not broadening. The implication is that anything that is not “key developmental” is broadening.

In the past, the distinction within a branch was between developmental assignments and key developmental assignments such as company command, battalion operations officer or battalion executive officer. In the current version of Department of the Army Pamphlet 600-3, some branches make a distinction between developmental and broadening assignments—for example, aviation—while others imply that broadening and developmental are synonymous in both not being key developmental, such as military intelligence.

The 2012 version, the most recent, of Army Doctrine Reference Publication (ADRP) 6-22: Army Leadership is closer to the spirit of a narrower definition of broadening, defining it as an opportunity that provides “exposure outside the leader’s branch or functional area competencies” and “allows development of a wider range of knowledge and skills” or “increases cross-cultural exposure and expands awareness of other governmental agencies, organizations or environments.”

In line with ADRP 6-22, we argue for a narrower definition of broadening and that assignments should meet at least two important criteria to truly be considered broadening. First, such an assignment should foster an environment that puts officers outside their comfort zone, where they cannot solely leverage their own past experiences in the Army in order to excel and where they are exposed to different organizational cultures and dynamics.

This is best, and perhaps only, achieved when the officer becomes a minority in an organization. Serving as an exchange officer in the British Army or as an interagency fellow at the U.S. Agency for International Development are great examples. This caveat naturally rules out most Army assignments. Army assignments in the functional and institutional realms currently labeled as broadening should probably instead be designated as developmental in nature.

Second, the assignment should help cultivate an officer’s critical thinking skills. Broadening opportunities should challenge officers to examine their previously held assumptions and instill in them the value of self-reflection. Attending graduate school full time, preferably not in classrooms entirely full of other military officers, is one obvious example but not the only one. Fellowships and serving as speechwriters, faculty members or on a Commander’s Initiatives Group at the Joint Staff or at a combatant command also stand out as superb broadening opportunities that nourish critical and creative thinking.

—Capt. Zach N. Watson, Maj. Brian C. Babcock-Lumish and Lt. Col. Heidi A. Urben

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11.2: Operational definitions

• Last updated
• Save as PDF
• Page ID 135144

• Matthew DeCarlo, Cory Cummings, & Kate Agnelli
• Open Social Work Education

Learning Objectives

Learners will be able to…

• Define and give an example of indicators and attributes for a variable
• Apply the three components of an operational definition to a variable
• Distinguish between levels of measurement for a variable and how those differences relate to measurement
• Describe the purpose of composite measures like scales and indices

Conceptual definitions are like dictionary definitions. They tell you what a concept means by defining it using other concepts. In this section we will move from the abstract realm (theory) to the real world (measurement).  Operationalization is the process by which researchers spell out precisely how a concept will be measured in their study. It involves identifying the specific research procedures we will use to gather data about our concepts. If conceptually defining your terms means looking at theory, how do you operationally define your terms? By looking for indicators of when your variable is present or not, more or less intense, and so forth. Operationalization is probably the most challenging part of quantitative research, but once it’s done, the design and implementation of your study will be straightforward.

Operationalization works by identifying specific  indicators that will be taken to represent the ideas we are interested in studying. If we are interested in studying masculinity, then the indicators for that concept might include some of the social roles prescribed to men in society such as breadwinning or fatherhood. Being a breadwinner or a father might therefore be considered  indicators  of a person’s masculinity. The extent to which a man fulfills either, or both, of these roles might be understood as clues (or indicators) about the extent to which he is viewed as masculine.

Let’s look at another example of indicators. Each day, Gallup researchers poll 1,000 randomly selected Americans to ask them about their well-being. To measure well-being, Gallup asks these people to respond to questions covering six broad areas: physical health, emotional health, work environment, life evaluation, healthy behaviors, and access to basic necessities. Gallup uses these six factors as indicators of the concept that they are really interested in, which is  well-being .

Identifying indicators can be even simpler than the examples described thus far. Political party affiliation is another relatively easy concept for which to identify indicators. If you asked a person what party they voted for in the last national election (or gained access to their voting records), you would get a good indication of their party affiliation. Of course, some voters split tickets between multiple parties when they vote and others swing from party to party each election, so our indicator is not perfect. Indeed, if our study were about political identity as a key concept, operationalizing it solely in terms of who they voted for in the previous election leaves out a lot of information about identity that is relevant to that concept. Nevertheless, it’s a pretty good indicator of political party affiliation.

Choosing indicators is not an arbitrary process. As described earlier, utilizing prior theoretical and empirical work in your area of interest is a great way to identify indicators in a scholarly manner. And you conceptual definitions will point you in the direction of relevant indicators. Empirical work will give you some very specific examples of how the important concepts in an area have been measured in the past and what sorts of indicators have been used. Often, it makes sense to use the same indicators as previous researchers; however, you may find that some previous measures have potential weaknesses that your own study will improve upon.

All of the examples in this chapter have dealt with questions you might ask a research participant on a survey or in a quantitative interview. If you plan to collect data from other sources, such as through direct observation or the analysis of available records, think practically about what the design of your study might look like and how you can collect data on various indicators feasibly. If your study asks about whether the participant regularly changes the oil in their car, you will likely not observe them directly doing so. Instead, you will likely need to rely on a survey question that asks them the frequency with which they change their oil or ask to see their car maintenance records.

• What indicators are commonly used to measure the variables in your research question?
• How can you feasibly collect data on these indicators?
• Are you planning to collect your own data using a questionnaire or interview? Or are you planning to analyze available data like client files or raw data shared from another researcher’s project?

Remember, you need  raw data . You research project cannot rely solely on the results reported by other researchers or the arguments you read in the literature. A literature review is only the first part of a research project, and your review of the literature should inform the indicators you end up choosing when you measure the variables in your research question.

Unlike conceptual definitions which contain other concepts, operational definition consists of the following components: (1) the variable being measured and its attributes, (2) the measure you will use, (3) how you plan to interpret the data collected from that measure to draw conclusions about the variable you are measuring.

Step 1: Specifying variables and attributes

The first component, the variable, should be the easiest part. At this point in quantitative research, you should have a research question that has at least one independent and at least one dependent variable. Remember that variables must be able to vary. For example, the United States is not a variable. Country of residence is a variable, as is patriotism. Similarly, if your sample only includes men, gender is a constant in your study, not a variable. A  constant is a characteristic that does not change in your study.

When social scientists measure concepts, they sometimes use the language of variables and attributes. A  variableno post  refers to a quality or quantity that varies across people or situations.  Attributes are the characteristics that make up a variable. For example, the variable hair color would contain attributes like blonde, brown, black, red, gray, etc. A variable’s attributes determine its level of measurement. There are four possible levels of measurement: nominal, ordinal, interval, and ratio. The first two levels of measurement are  categorical , meaning their attributes are categories rather than numbers. The latter two levels of measurement are  continuous , meaning their attributes are numbers.

I exist to frustrate researchers’ categorizations.

Levels of measurement

Hair color is an example of a nominal level of measurement.  Nominal measures are categorical, and those categories cannot be mathematically ranked. As a brown-haired person (with some gray), I can’t say for sure that brown-haired people are better than blonde-haired people. As with all nominal levels of measurement, there is no ranking order between hair colors; they are simply different. That is what constitutes a nominal level of gender and race are also measured at the nominal level.

What attributes are contained in the variable  hair color ? While blonde, brown, black, and red are common colors, some people may not fit into these categories if we only list these attributes. My wife, who currently has purple hair, wouldn’t fit anywhere. This means that our attributes were not exhaustive.  Exhaustiveness means that all possible attributes are listed. We may have to list a lot of colors before we can meet the criteria of exhaustiveness. Clearly, there is a point at which exhaustiveness has been reasonably met. If a person insists that their hair color is  light burnt sienna , it is not your responsibility to list that as an option. Rather, that person would reasonably be described as brown-haired. Perhaps listing a category for  other color  would suffice to make our list of colors exhaustive.

What about a person who has multiple hair colors at the same time, such as red and black? They would fall into multiple attributes. This violates the rule of  mutual exclusivity , in which a person cannot fall into two different attributes. Instead of listing all of the possible combinations of colors, perhaps you might include a  multi-color  attribute to describe people with more than one hair color.

Making sure researchers provide mutually exclusive and exhaustive is about making sure all people are represented in the data record. For many years, the attributes for gender were only male or female. Now, our understanding of gender has evolved to encompass more attributes that better reflect the diversity in the world. Children of parents from different races were often classified as one race or another, even if they identified with both cultures. The option for bi-racial or multi-racial on a survey not only more accurately reflects the racial diversity in the real world but validates and acknowledges people who identify in that manner. If we did not measure race in this way, we would leave empty the data record for people who identify as biracial or multiracial, impairing our search for truth.

Unlike nominal-level measures, attributes at the ordinal  level can be rank ordered. For example, someone’s degree of satisfaction in their romantic relationship can be ordered by rank. That is, you could say you are not at all satisfied, a little satisfied, moderately satisfied, or highly satisfied. Note that even though these have a rank order to them (not at all satisfied is certainly worse than highly satisfied), we cannot calculate a mathematical distance between those attributes. We can simply say that one attribute of an ordinal-level variable is more or less than another attribute.

This can get a little confusing when using rating scales . If you have ever taken a customer satisfaction survey or completed a course evaluation for school, you are familiar with rating scales. “On a scale of 1-5, with 1 being the lowest and 5 being the highest, how likely are you to recommend our company to other people?” That surely sounds familiar. Rating scales use numbers, but only as a shorthand, to indicate what attribute (highly likely, somewhat likely, etc.) the person feels describes them best. You wouldn’t say you are “2” likely to recommend the company, but you would say you are not very likely to recommend the company. Ordinal-level attributes must also be exhaustive and mutually exclusive, as with nominal-level variables.

At the interval level, attributes must also be exhaustive and mutually exclusive and there is equal distance between attributes. Interval measures are also continuous, meaning their attributes are numbers, rather than categories. IQ scores are interval level, as are temperatures in Fahrenheit and Celsius. Their defining characteristic is that we can say how much more or less one attribute differs from another. We cannot, however, say with certainty what the ratio of one attribute is in comparison to another. For example, it would not make sense to say that a person with an IQ score of 140 has twice the IQ of a person with a score of 70. However, the difference between IQ scores of 80 and 100 is the same as the difference between IQ scores of 120 and 140.

While we cannot say that someone with an IQ of 140 is twice as intelligent as someone with an IQ of 70 because IQ is measured at the interval level, we can say that someone with six siblings has twice as many as someone with three because number of siblings is measured at the ratio level. Finally, at the  ratio level, attributes are mutually exclusive and exhaustive, attributes can be rank ordered, the distance between attributes is equal, and attributes have a true zero point. Thus, with these variables, we  can  say what the ratio of one attribute is in comparison to another. Examples of ratio-level variables include age and years of education. We know that a person who is 12 years old is twice as old as someone who is 6 years old. Height measured in meters and weight measured in kilograms are good examples. So are counts of discrete objects or events such as the number of siblings one has or the number of questions a student answers correctly on an exam. The differences between each level of measurement are visualized in Table 11.1.

Table 11.1 Criteria for Different Levels of Measurement

Levels of measurement=levels of specificity

We have spent time learning how to determine our data’s level of measurement. Now what? How could we use this information to help us as we measure concepts and develop measurement tools? First, the types of statistical tests that we are able to use are dependent on our data’s level of measurement. With nominal-level measurement, for example, the only available measure of central tendency is the mode. With ordinal-level measurement, the median or mode can be used as indicators of central tendency. Interval and ratio-level measurement are typically considered the most desirable because they permit for any indicators of central tendency to be computed (i.e., mean, median, or mode). Also, ratio-level measurement is the only level that allows meaningful statements about ratios of scores. The higher the level of measurement, the more complex statistical tests we are able to conduct. This knowledge may help us decide what kind of data we need to gather, and how.

That said, we have to balance this knowledge with the understanding that sometimes, collecting data at a higher level of measurement could negatively impact our studies. For instance, sometimes providing answers in ranges may make prospective participants feel more comfortable responding to sensitive items. Imagine that you were interested in collecting information on topics such as income, number of sexual partners, number of times someone used illicit drugs, etc. You would have to think about the sensitivity of these items and determine if it would make more sense to collect some data at a lower level of measurement (e.g., asking if they are sexually active or not (nominal) versus their total number of sexual partners (ratio).

Finally, sometimes when analyzing data, researchers find a need to change a data’s level of measurement. For example, a few years ago, a student was interested in studying the relationship between mental health and life satisfaction. This student used a variety of measures. One item asked about the number of mental health symptoms, reported as the actual number. When analyzing data, my student examined the mental health symptom variable and noticed that she had two groups, those with none or one symptoms and those with many symptoms. Instead of using the ratio level data (actual number of mental health symptoms), she collapsed her cases into two categories, few and many. She decided to use this variable in her analyses. It is important to note that you can move a higher level of data to a lower level of data; however, you are unable to move a lower level to a higher level.

• Check that the variables in your research question can vary…and that they are not constants or one of many potential attributes of a variable.
• Think about the attributes your variables have. Are they categorical or continuous? What level of measurement seems most appropriate?

Step 2: Specifying measures for each variable

Let’s pick a social work research question and walk through the process of operationalizing variables to see how specific we need to get. I’m going to hypothesize that residents of a psychiatric unit who are more depressed are less likely to be satisfied with care. Remember, this would be a inverse relationship—as depression increases, satisfaction decreases. In this question, depression is my independent variable (the cause) and satisfaction with care is my dependent variable (the effect). Now we have identified our variables, their attributes, and levels of measurement, we move onto the second component: the measure itself.

So, how would you measure my key variables: depression and satisfaction? What indicators would you look for? Some students might say that depression could be measured by observing a participant’s body language. They may also say that a depressed person will often express feelings of sadness or hopelessness. In addition, a satisfied person might be happy around service providers and often express gratitude. While these factors may indicate that the variables are present, they lack coherence. Unfortunately, what this “measure” is actually saying is that “I know depression and satisfaction when I see them.” While you are likely a decent judge of depression and satisfaction, you need to provide more information in a research study for how you plan to measure your variables. Your judgment is subjective, based on your own idiosyncratic experiences with depression and satisfaction. They couldn’t be replicated by another researcher. They also can’t be done consistently for a large group of people. Operationalization requires that you come up with a specific and rigorous measure for seeing who is depressed or satisfied.

Finding a good measure for your variable depends on the kind of variable it is. Variables that are directly observable don’t come up very often in my students’ classroom projects, but they might include things like taking someone’s blood pressure, marking attendance or participation in a group, and so forth. To measure an indirectly observable variable like age, you would probably put a question on a survey that asked, “How old are you?” Measuring a variable like income might require some more thought, though. Are you interested in this person’s individual income or the income of their family unit? This might matter if your participant does not work or is dependent on other family members for income. Do you count income from social welfare programs? Are you interested in their income per month or per year? Even though indirect observables are relatively easy to measure, the measures you use must be clear in what they are asking, and operationalization is all about figuring out the specifics of what you want to know. For more complicated constructs, you will need compound measures (that use multiple indicators to measure a single variable).

How you plan to collect your data also influences how you will measure your variables. For social work researchers using secondary data like client records as a data source, you are limited by what information is in the data sources you can access. If your organization uses a given measurement for a mental health outcome, that is the one you will use in your study. Similarly, if you plan to study how long a client was housed after an intervention using client visit records, you are limited by how their caseworker recorded their housing status in the chart. One of the benefits of collecting your own data is being able to select the measures you feel best exemplify your understanding of the topic.

Measuring unidimensional concepts

The previous section mentioned two important considerations: how complicated the variable is and how you plan to collect your data. With these in hand, we can use the level of measurement to further specify how you will measure your variables and consider specialized rating scales developed by social science researchers.

Measurement at each level

Nominal measures assess categorical variables. These measures are used for variables or indicators that have mutually exclusive attributes, but that cannot be rank-ordered. Nominal measures ask about the variable and provide names or labels for different attribute values like social work, counseling, and nursing for the variable profession. Nominal measures are relatively straightforward.

Ordinal measures often use a rating scale. It is an ordered set of responses that participants must choose from. Figure 11.1 shows several examples. The number of response options on a typical rating scale is usualy five or seven, though it can range from three to 11. Five-point scales are best for unipolar scales where only one construct is tested, such as frequency (Never, Rarely, Sometimes, Often, Always). Seven-point scales are best for bipolar scales where there is a dichotomous spectrum, such as liking (Like very much, Like somewhat, Like slightly, Neither like nor dislike, Dislike slightly, Dislike somewhat, Dislike very much). For bipolar questions, it is useful to offer an earlier question that branches them into an area of the scale; if asking about liking ice cream, first ask “Do you generally like or dislike ice cream?” Once the respondent chooses like or dislike, refine it by offering them relevant choices from the seven-point scale. Branching improves both reliability and validity (Krosnick & Berent, 1993). [9]  Although you often see scales with numerical labels, it is best to only present verbal labels to the respondents but convert them to numerical values in the analyses. Avoid partial labels or length or overly specific labels. In some cases, the verbal labels can be supplemented with (or even replaced by) meaningful graphics. The last rating scale shown in Figure 11.1 is a visual-analog scale, on which participants make a mark somewhere along the horizontal line to indicate the magnitude of their response.

Figure 11.1 Example rating scales for closed-ended questionnaire items

Interval measures are those where the values measured are not only rank-ordered, but are also equidistant from adjacent attributes. For example, the temperature scale (in Fahrenheit or Celsius), where the difference between 30 and 40 degree Fahrenheit is the same as that between 80 and 90 degree Fahrenheit. Likewise, if you have a scale that asks respondents’ annual income using the following attributes (ranges): $0 to 10,000, $10,000 to 20,000, $20,000 to 30,000, and so forth, this is also an interval measure, because the mid-point of each range (i.e., $5,000, $15,000, $25,000, etc.) are equidistant from each other. The intelligence quotient (IQ) scale is also an interval measure, because the measure is designed such that the difference between IQ scores 100 and 110 is supposed to be the same as between 110 and 120 (although we do not really know whether that is truly the case). Interval measures allow us to examine “how much more” is one attribute when compared to another, which is not possible with nominal or ordinal measures. You may find researchers who “pretend” (incorrectly) that ordinal rating scales are actually interval measures so that we can use different statistical techniques for analyzing them. As we will discuss in the latter part of the chapter, this is a mistake because there is no way to know whether the difference between a 3 and a 4 on a rating scale is the same as the difference between a 2 and a 3. Those numbers are just placeholders for categories.

Ratio measures are those that have all the qualities of nominal, ordinal, and interval scales, and in addition, also have a “true zero” point (where the value zero implies lack or non-availability of the underlying construct). Think about how to measure the number of people working in human resources at a social work agency. It could be one, several, or none (if the company contracts out for those services). Measuring interval and ratio data is relatively easy, as people either select or input a number for their answer. If you ask a person how many eggs they purchased last week, they can simply tell you they purchased `a dozen eggs at the store, two at breakfast on Wednesday, or none at all.

Commonly used rating scales in questionnaires

The level of measurement will give you the basic information you need, but social scientists have developed specialized instruments for use in questionnaires, a common tool used in quantitative research. As we mentioned before, if you plan to source your data from client files or previously published results

Although Likert scale  is a term colloquially used to refer to almost any rating scale (e.g., a 0-to-10 life satisfaction scale), it has a much more precise meaning. In the 1930s, researcher Rensis Likert (pronounced LICK-ert) created a new approach for measuring people’s attitudes (Likert, 1932). [10]  It involves presenting people with several statements—including both favorable and unfavorable statements—about some person, group, or idea. Respondents then express their agreement or disagreement with each statement on a 5-point scale:  Strongly Agree, Agree, Neither Agree nor Disagree, Disagree, Strongly Disagree . Numbers are assigned to each response and then summed across all items to produce a score representing the attitude toward the person, group, or idea. For items that are phrased in an opposite direction (e.g., negatively worded statements instead of positively worded statements), reverse coding is used so that the numerical scoring of statements also runs in the opposite direction. The entire set of items came to be called a Likert scale, as indicated in Table 11.2 below.

Unless you are measuring people’s attitude toward something by assessing their level of agreement with several statements about it, it is best to avoid calling it a Likert scale. You are probably just using a rating scale. Likert scales allow for more granularity (more finely tuned response) than yes/no items, including whether respondents are neutral to the statement. Below is an example of how we might use a Likert scale to assess your attitudes about research as you work your way through this textbook.

Table 11.2 Likert scale

Semantic differential scales are composite (multi-item) scales in which respondents are asked to indicate their opinions or feelings toward a single statement using different pairs of adjectives framed as polar opposites. Whereas in the above Likert scale, the participant is asked how much they  agree or disagree  with a statement, in a semantic differential scale the participant is asked to indicate how they  feel  about a specific item. This makes the semantic differential scale an excellent technique for measuring people’s attitudes or feelings toward objects, events, or behaviors. Table 11.3 is an example of a semantic differential scale that was created to assess participants’ feelings about this textbook. 

Table 11.3. A semantic differential scale for measuring attitudes towards a textbook

Notice that on a Likert scale, each item is different but the choices for the scale are the same (e.g., strongly agree, agree, etc.). However, for a semantic differential scale, the thing that you are reviewing, in this case, beliefs about research content, remains the same. It is the choices that change. Semantic differential is believed to be an excellent technique for measuring people’s attitude or feelings toward objects, events, or behaviors.

This composite scale was designed by Louis Guttman and uses a series of items arranged in increasing order of intensity (least intense to most intense) of the concept. This type of scale allows us to understand the intensity of beliefs or feelings. Each item in the above  Guttman scale has a weight (this is not indicated on the tool) which varies with the intensity of that item, and the weighted combination of each response is used as an aggregate measure of an observation.

Notice how the items move from lower intensity to higher intensity. A researcher reviews the yes answers and creates a score for each participant.

For more complicated measures, researchers use scales and indices (sometimes called indexes) to measure their variables because they assess multiple indicators to develop a composite (or total) score. Composite scores provide a much greater understanding of concepts than a single item could. Although we won’t delve too deeply into the process of scale development, we will cover some important topics for you to understand how scales and indices developed by other researchers can be used in your project.

Although they exhibit differences (which will later be discussed) the two have in common various factors.

• Both are ordinal measures of variables.
• Both can order the units of analysis in terms of specific variables.
• Both are composite measures .

The previous section discussed how to measure respondents’ responses to predesigned items or indicators belonging to an underlying construct. But how do we create the indicators themselves? The process of creating the indicators is called scaling. More formally, scaling is a branch of measurement that involves the construction of measures by associating qualitative judgments about unobservable constructs with quantitative, measurable metric units. Stevens (1946)\(^{11}\) said, “Scaling is the assignment of objects to numbers according to a rule.” This process of measuring abstract concepts in concrete terms remains one of the most difficult tasks in empirical social science research.

The outcome of a scaling process is a scale , which is an empirical structure for measuring items or indicators of a given construct. Understand that multidimensional “scales”, as discussed in this section, are a little different from “rating scales” discussed in the previous section. A rating scale is used to capture the respondents’ reactions to a given item on a questionnaire. For example, an ordinally scaled item captures a value between “strongly disagree” to “strongly agree.” Attaching a rating scale to a statement or instrument is not scaling. Rather, scaling is the formal process of developing scale items, before rating scales can be attached to those items.

If creating your own scale sounds painful, don’t worry! For most multidimensional variables, you would likely be duplicating work that has already been done by other researchers. Specifically, this is a branch of science called psychometrics. You do not need to create a scale for depression because scales such as the Patient Health Questionnaire (PHQ-9), the Center for Epidemiologic Studies Depression Scale (CES-D), and Beck’s Depression Inventory (BDI) have been developed and refined over dozens of years to measure variables like depression. Similarly, scales such as the Patient Satisfaction Questionnaire (PSQ-18) have been developed to measure satisfaction with medical care. As we will discuss in the next section, these scales have been shown to be reliable and valid. While you could create a new scale to measure depression or satisfaction, a study with rigor would pilot test and refine that new scale over time to make sure it measures the concept accurately and consistently. This high level of rigor is often unachievable in student research projects because of the cost and time involved in pilot testing and validating, so using existing scales is recommended.

Unfortunately, there is no good one-stop=shop for psychometric scales. The Mental Measurements Yearbook provides a searchable database of measures for social science variables, though it woefully incomplete and often does not contain the full documentation for scales in its database. You can access it from a university library’s list of databases. If you can’t find anything in there, your next stop should be the methods section of the articles in your literature review. The methods section of each article will detail how the researchers measured their variables, and often the results section is instructive for understanding more about measures. In a quantitative study, researchers may have used a scale to measure key variables and will provide a brief description of that scale, its names, and maybe a few example questions. If you need more information, look at the results section and tables discussing the scale to get a better idea of how the measure works. Looking beyond the articles in your literature review, searching Google Scholar using queries like “depression scale” or “satisfaction scale” should also provide some relevant results. For example, searching for documentation for the Rosenberg Self-Esteem Scale (which we will discuss in the next section), I found this  report from researchers investigating acceptance and commitment therapy  which details this scale and many others used to assess mental health outcomes. If you find the name of the scale somewhere but cannot find the documentation (all questions and answers plus how to interpret the scale), a general web search with the name of the scale and “.pdf” may bring you to what you need. Or, to get professional help with finding information, always ask a librarian!

Unfortunately, these approaches do not guarantee that you will be able to view the scale itself or get information on how it is interpreted. Many scales cost money to use and may require training to properly administer. You may also find scales that are related to your variable but would need to be slightly modified to match your study’s needs. You could adapt a scale to fit your study, however changing even small parts of a scale can influence its accuracy and consistency. While it is perfectly acceptable in student projects to adapt a scale without testing it first (time may not allow you to do so), pilot testing is always recommended for adapted scales, and researchers seeking to draw valid conclusions and publish their results must take this additional step.

An  index is a composite score derived from aggregating measures of multiple concepts (called components) using a set of rules and formulas. It is different from a scale. Scales also aggregate measures; however, these measures examine different dimensions  or  the same dimension of a single construct. A well-known example of an index is the  consumer price index  (CPI), which is computed every month by the Bureau of Labor Statistics of the U.S. Department of Labor. The CPI is a measure of how much consumers have to pay for goods and services (in general) and is divided into eight major categories (food and beverages, housing, apparel, transportation, healthcare, recreation, education and communication, and “other goods and services”), which are further subdivided into more than 200 smaller items. Each month, government employees call all over the country to get the current prices of more than 80,000 items. Using a complicated weighting scheme that takes into account the location and probability of purchase for each item, analysts then combine these prices into an overall index score using a series of formulas and rules.

Another example of an index is the  Duncan Socioeconomic Index  (SEI). This index is used to quantify a person’s socioeconomic status (SES) and is a combination of three concepts: income, education, and occupation. Income is measured in dollars, education in years or degrees achieved, and occupation is classified into categories or levels by status. These very different measures are combined to create an overall SES index score. However, SES index measurement has generated a lot of controversy and disagreement among researchers.

The process of creating an index is similar to that of a scale. First, conceptualize (define) the index and its constituent components. Though this appears simple, there may be a lot of disagreement on what components (concepts/constructs) should be included or excluded from an index. For instance, in the SES index, isn’t income correlated with education and occupation? And if so, should we include one component only or all three components? Reviewing the literature, using theories, and/or interviewing experts or key stakeholders may help resolve this issue. Second, operationalize and measure each component. For instance, how will you categorize occupations, particularly since some occupations may have changed with time (e.g., there were no Web developers before the Internet)? As we will see in step three below, researchers must create a rule or formula for calculating the index score. Again, this process may involve a lot of subjectivity, so validating the index score using existing or new data is important.

Scale and index development at often taught in their own course in doctoral education, so it is unreasonable for you to expect to develop a consistently accurate measure within the span of a week or two. Using available indices and scales is recommended for this reason.

Differences between scales and indices

Though indices and scales yield a single numerical score or value representing a concept of interest, they are different in many ways. First, indices often comprise components that are very different from each other (e.g., income, education, and occupation in the SES index) and are measured in different ways. Conversely, scales typically involve a set of similar items that use the same rating scale (such as a five-point Likert scale about customer satisfaction).

Second, indices often combine objectively measurable values such as prices or income, while scales are designed to assess subjective or judgmental constructs such as attitude, prejudice, or self-esteem. Some argue that the sophistication of the scaling methodology makes scales different from indexes, while others suggest that indexing methodology can be equally sophisticated. Nevertheless, indexes and scales are both essential tools in social science research.

Scales and indices seem like clean, convenient ways to measure different phenomena in social science, but just like with a lot of research, we have to be mindful of the assumptions and biases underneath. What if a scale or an index was developed using only White women as research participants? Is it going to be useful for other groups? It very well might be, but when using a scale or index on a group for whom it hasn’t been tested, it will be very important to evaluate the validity and reliability of the instrument, which we address in the rest of the chapter.

Finally, it’s important to note that while scales and indices are often made up of nominal or ordinal variables, when we analyze them into composite scores, we will treat them as interval/ratio variables.

• Look back to your work from the previous section, are your variables unidimensional or multidimensional?
• Describe the specific measures you will use (actual questions and response options you will use with participants) for each variable in your research question.
• If you are using a measure developed by another researcher but do not have all of the questions, response options, and instructions needed to implement it, put it on your to-do list to get them.

If we were operationalizing blood pressure, the cuff and reader would be the measure…but how do we interpret what is high, low, and normal blood pressure?

Step 3: How you will interpret your measures

The final stage of operationalization involves setting the rules for how the measure works and how the researcher should interpret the results. Sometimes, interpreting a measure can be incredibly easy. If you ask someone their age, you’ll probably interpret the results by noting the raw number (e.g., 22) someone provides and that it is lower or higher than other people’s ages. However, you could also recode that person into age categories (e.g., under 25, 20-29-years-old, generation Z, etc.). Even scales may be simple to interpret. If there is a scale of problem behaviors, one might simply add up the number of behaviors checked off–with a range from 1-5 indicating low risk of delinquent behavior, 6-10 indicating the student is moderate risk, etc. How you choose to interpret your measures should be guided by how they were designed, how you conceptualize your variables, the data sources you used, and your plan for analyzing your data statistically. Whatever measure you use, you need a set of rules for how to take any valid answer a respondent provides to your measure and interpret it in terms of the variable being measured.

For more complicated measures like scales, refer to the information provided by the author for how to interpret the scale. If you can’t find enough information from the scale’s creator, look at how the results of that scale are reported in the results section of research articles. For example, Beck’s Depression Inventory (BDI-II) uses 21 statements to measure depression and respondents rate their level of agreement on a scale of 0-3. The results for each question are added up, and the respondent is put into one of three categories: low levels of depression (1-16), moderate levels of depression (17-30), or severe levels of depression (31 and over).

One common mistake I see often is that students will introduce another variable into their operational definition. This is incorrect. Your operational definition should mention only one variable—the variable being defined. While your study will certainly draw conclusions about the relationships between variables, that’s not what operationalization is. Operationalization specifies what instrument you will use to measure your variable and how you plan to interpret the data collected using that measure.

Operationalization is probably the trickiest component of basic research methods, so please don’t get frustrated if it takes a few drafts and a lot of feedback to get to a workable definition. At the time of this writing, I am in the process of operationalizing the concept of “attitudes towards research methods.” Originally, I thought that I could gauge students’ attitudes toward research methods by looking at their end-of-semester course evaluations. As I became aware of the potential methodological issues with student course evaluations, I opted to use focus groups of students to measure their common beliefs about research. You may recall some of these opinions from  Chapter 1 , such as the common beliefs that research is boring, useless, and too difficult. After the focus group, I created a scale based on the opinions I gathered, and I plan to pilot test it with another group of students. After the pilot test, I expect that I will have to revise the scale again before I can implement the measure in a real social work research project. At the time I’m writing this, I’m still not completely done operationalizing this concept.

Key Takeaways

• Operationalization involves spelling out precisely how a concept will be measured.
• Operational definitions must include the variable, the measure, and how you plan to interpret the measure.
• There are four different levels of measurement: nominal, ordinal, interval, and ratio (in increasing order of specificity).
• Scales and indices are common ways to collect information and involve using multiple indicators in measurement.
• A key difference between a scale and an index is that a scale contains multiple indicators for one concept, whereas an indicator examines multiple concepts (components).
• Using scales developed and refined by other researchers can improve the rigor of a quantitative study.

Use the research question that you developed in the previous chapters and find a related scale or index that researchers have used. If you have trouble finding the exact phenomenon you want to study, get as close as you can.

• What is the level of measurement for each item on each tool? Take a second and think about why the tool’s creator decided to include these levels of measurement. Identify any levels of measurement you would change and why.
• If these tools don’t exist for what you are interested in studying, why do you think that is?

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Operational Planning

Operational planning definition.

What does operational planning mean? Operational planning creates a detailed roadmap based on a strategic plan. The operational plan aligns timelines, action items and key milestones that finance or the business needs to complete to execute on the strategic plan. In this way, an operational plan outlines the organization’s key objectives and goals and clarifies how the organization will achieve them.

During the operational planning process, finance or the business responsibilities are described in detail based on the timeline for the operational plan. The timeframe should depend on typical organizational velocity; creating an annual operational plan is a fluid, changing process, so keeping clarity and collaboration is vital for success.

A well-conceived business operational plan keeps team members collaborating smoothly, ensures everyone knows what needs to be done and what their part in it is, and guides critical decisions about long-term strategy.

Key steps of operational planning

• Define the goal or vision for the operational plan clearly
• Analyze and identify key business stakeholders, resources and budgets team members, budgets, and resources
• Consistently track and inform team members and stakeholders on progress
• Adapt the operational plan to wider company goals as needed

What Is Operational Planning?

Operational planning faqs.

What is operational planning for finance or the business? Operational planning is the result of a team or department working to execute a strategic plan. It is a future-oriented process that maps out department goals, capabilities, and budgets to promote the success of team-based activities designed to support the strategic plan.

Operational business plans are most effective when there is buy-in from the entire team or department, ensuring issues are reported, goals identified and timelines get delivered,, and business collaboration is more effective. When communication across finance and the business exists, operational plans work even more efficiently to ensure that the entire organization reaches its goals.

An example of operational planning would be a manufacturer creating a plan to increase revenue by 30%. Finance partners with sales, the marketing team, operations and other key business areas to align on the strategies needed to support revenue growth and achieve business goals together. Another operational planning example might be a brand looking to introduce a new product. It would need to leverage and expand existing capabilities, harness new tools, and create a roadmap for doing so.

Other operational planning examples in management include mapping business or production output to meet other new goals, planning for new or expanded solutions, sales and operational planning, providing a roadmap or increased clarity surrounding business goals, or creating a strategy for increased business partnership.

Strategic Planning vs Operational Planning

There is a difference between strategic planning, tactical planning, and operational planning. However, strategic, tactical, and operational planning need to be considered together and build upon one another.

What is a strategic plan?

A strategic plan describes the high-level goals, long-term vision, and organizational mission, usually over the next three to five years. It also details the major projects or initiatives that will happen to meet them, and how the organization will measure the goals, broadly. This is a big picture view of goals, but it can’t really show a team how to achieve those goals step-by-step.

What is an operational plan ?

An operational plan (also known as an operations plan, work plan, or operation plan) is a detailed outline of what a team or department will focus on in the immediate future—typically within the upcoming year. The operational plan answers questions about things like weekly goals and tasks, such as what they are generally, what they will achieve, who will do them, and how often.

What is a tactical plan?

Tactical planning is a step organizations or teams sometimes take after they create strategic and operational plans. The idea is to break the plans into smaller goals and objectives, to define them and determine which steps and actions will be most effective in achieving them. In other words, the operational plan may just have set a goal or task for person A about goal 1, but a tactical plan might set forth the detailed steps person A will need to execute every week.

Tactical planning and operational planning differ in the kinds of questions they ask. Operational plans ask how the team should do something so they can both adhere more broadly to the organizational mission and specific strategic goals. Tactical plans ask specific questions about how to accomplish strategic and operational goals. They are the most microscopic version of planning.

In summary, a strategic plan is a business-level, long-term strategy plan over the next three to five years. It is a visionary plan, the big picture. Its focus is not on implementation. An operational plan is smaller in timeline and both scope, and the goal of operational planning is both to describe a more granular view of how to achieve strategic goals and to focus on implementation in the form of weekly actions, specific Key Performance Indicators (KPIs), etc. A tactical plan is the narrowest view that is focused on implementation only, and things like daily tasks for one person or a small team and smaller goals.

Strategic and operational planning work together; operational planning is an important part of a whole strategy. Tactical planning helps teams achieve their strategic and operational planning goals.

The goal of an operational plan is to give particular tasks to specific departments, not the company as a whole, whereas it is strategic vs operational planning that sets forth long-term goals for the next three to five years.

What is the Operational Planning Process?

Going through the operational planning cycle, keep these best practices and operational planning techniques in mind.

Research and Identify Goals

The goal of an operational plan and its creation process should be to address some foundational questions:

• Start with the strategic plan: how will it shape the actions we take?
• What is the budget? How will it compare to previous years?
• What is the current status, considering budget, resources, and team members? What is the goal status in one, two, three years, etc.?
• How can the team practically achieve the goal? What operational planning methodology informs the approach? What are the operational planning tools we will use?
• What benchmarks should be used to assess our progress? They might include 5-star reviews, customer service cases closed, launch deadlines met, number of goods manufactured, new customers acquired, revenue increases, etc.

Ask team members the questions, and prioritize responses based on how difficult they are to execute, and how critical.

Visualize the Operational Plan

Make sure the vision for the plan is clearly articulated. Clearly defined goals, charts and visualizations, and project management software can help offer a high-level view of tasks and progress for all stakeholders. Identify which operational business planning techniques and tools will work best for achieving the organization’s goals.

Assign People and Budget

The budgeting process in operational planning consists of assigning tasks and allocating resources and budget for team members to complete them. Each piece of the budget should map out to a financial goal in the operational plan with corresponding timetables and deliverables.

Tracking and Informing Progress

Build out a reporting process that corresponds to the clear objectives with goals, targets, deliverables, resource allocation, and timetables in the operational plan. This way the stakeholders can report progress as the plan moves forward.

Adjust the Operational Plan as Needed

A well-conceived operational plan should allow you to understand precisely which activities and aspects of the plan failed to perform. This in turn allows the team to pivot, involve new team members as needed, and continue to the next benchmark with a refined operational plan.

Consider the Right Indicators

Use key performance metrics or indicators that are predictive, not just lagging indicators. You need some lagging indicators such as past sales or attendance figures, but leading indicators such as market trends should also contribute to both reporting progress and adjusting the operational plan.

What Should Operational Planning Include?

Approaches to operational planning vary, but each team has as its main objective producing a functional operational plan that reflects a practical approach to the organization’s mission and strategic plan.

What should an operational plan include? This strategic document should plan all of the daily processes and operations that a business and its teams or departments including marketing, recruitment, and finance need to do to achieve company goals.

A well-defined operational plan should ensure that each manager and employee understands what their specific responsibilities are, and how and when to execute them.

The operation plan itself should have several components:

• A title page. This summarizes the operational plan.
• An executive summary. This provides a few sentences with a rough idea of the overall plan and its basic sections.
• Mission and objectives. This section defines the organization’s broader mission and objectives. It also describes goals and milestones for the coming year that relate to the operational plan.
• KPIs. Evaluate metrics and KPIs that will measure results.
• Financial summary. This offers an overview and a financial breakdown of all projects included in the operational plan to demonstrate there is sufficient capital to execute the plan.
• Hiring plan. Determine how many monthly/quarterly team members to hire across different departments.
• Key assumptions and risks. Provide this risk analysis so mitigation can be performed.
• Next steps. Suggest next steps, if any.

What are the Steps in Operational Planning?

The purpose of the operational planning process is not to generate new goals or plans, but to create an operational plan in support of existing strategic goals:

Start with a strategic plan

Create the strategic plan first. Before considering immediate tasks and day-to-day details, it’s important to see the long-term vision and goals. As the leadership team creates the strategic plan, they determine the position of the organization and develop its strategy. They should also monitor the strategic plan, and adjust it as needed.

Sharpen the scope

Narrow the scope of the operational plan to a department, team, or focus area to ensure it is detail-oriented and targeted. The size of the organization determines the scope of your operational plan. In other words, you start big with the strategic plan, and then narrow down to the operational plan and the focus area of the team who will execute it—and then create various supporting action plans for execution.

Identify key stakeholders

Identify stakeholders in the operational planning process before creating an operational plan. The team members who create the operational plan should lead and inform others around the operational plan, so you’ll need to know who they are before execution.

Create the operational plan

Your operational plan sets forth the timeframe, the goals to achieve, and explains the actions the team will take to achieve those goals on time. It must include objectives, deliverables, quality standards (if any), desired outcomes, operating budget, staffing and resource requirements, and progress and monitoring information.

For example:

An organization’s strategic plan sets forth the goal of the marketing team increasing brand awareness by at least 10% in the next year. This will mean increased engagement with potential customers and more eyes on new marketing materials.

This will require support from the design team, who will have new goals: update the website and create new promotional materials. To achieve those goals, they will collaborate with the development team on the update and hire social media engagement team members. The team will use software and management tools to report and track their progress.

Share the operational plan

Share the operational plan with key stakeholders so they understand mission critical goals and the daily tasks that support them. Track progress in real-time for best results. This also allows you to update the operational plan and report on progress as needed to team members and stakeholders. Like project planning, operational planning is never a one-and-done task, but remains a continuous process.

Why is Operational Planning Important?

At the organizational level, project success demands a strong operational plan. Chaos and confusion often reign without an operational plan, as budgets rise and team members lose sight of tasks and deadlines.

The importance of operational planning is in the creation of a single source of truth that enables comprehensive understanding of mission, strategic goals, and how to achieve them. An operational plan helps teams identify areas that cause lack of clarity, missed revenue generation opportunities, inefficient strategies, or areas of reduced business partnership.

What are the Benefits of Operational Planning?

The advantages of operational planning can impact organizations of any size. An operational plan helps teams reach strategic goals by connecting teams and their individual tasks to company goals. A detail-oriented operational plan has many benefits.

It clarifies organizational goals. Operational planning helps leadership define responsibilities, daily tasks, and activities in detail. It also sets out how individual team members support overall department and organizational goals and defines outcomes for them to measure daily tasks against.

It also boosts team productivity. Operational planning enhances efficiency, productivity, and profits by ensuring employees in each department and across the company know their daily responsibilities and objectives.

Operational planning disadvantages include creating an operational plan based on human error, or whose success is overly dependent upon effective coordination of diverse cross-functional teams. Singular focus only on coordination and not connecting the business is a primary disadvantage of implementing an operations planning process.

Who is Responsible for Operational Planning?

Create an operational plan at the department or team level to best precisely capture the roles and tasks. At a larger organization, an operational plan might even be specific to a particular initiative—much like a detailed tactical or work plan.

There several considerations that determine who creates operational plans:

• Scope. For every activity, the operational plan includes the who, what, and when and must be laser-focused on the initiative itself and the team. Watch to ensure scope is not too broad.
• Timeline. An operational plan should cover a quarter, six months, or a fiscal year, depending on organizational speed and velocity.
• Stakeholders. To accurately predict what work to include in the plan, ensure operational planning stakeholders stay close to the work. Finance must unit the business from tactical details to strategic execution.

Typically, the operational plan is the realm of middle-management, in contrast to the top-down execution style from the C-suite the strategic plan receives. Its scope is also narrower and as routine tasks are mapped out, which continuously evolves Changes to the strategic plan will be less frequent.

Given the focus on day-to-day activities, allocation of resources, and tasks, middle-managers are often best-suited to map out and implement the operational plan.

Does Planful Help With Operational Planning?

Yes. Planful’s financial performance platform unites the demand for structured planning originating in finance with the business need for dynamic planning. Planful empowers organizations to make smarter decisions more confidently, rapidly, and strategically and ensures the data collection process for operational planning isn’t a time-consuming, manual process.

Use Planful to build collaborative financial plans that align resources with strategic objectives. Adjust and pivot as business conditions change, model hundreds of different scenarios reliably, and turn annual plans into quarterly or monthly rolling forecasts, all based on what the organization needs now.

Find out more about Planful’s Operational Planning solution here.

Get Started with Planful

Operational vs. Generating Force Assignments

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What Is Operations Management (OM)?

Understanding operations management (om), operations and supply chain management (oscm), what operations managers do.

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Operations Management: Understanding and Using It

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

Operations management (OM) is the administration of business practices to create the highest level of efficiency possible within an organization. It is concerned with converting materials and labor into goods and services as efficiently as possible to maximize the profit of an organization.

Operations management teams attempt to balance costs with revenue to achieve the highest net operating profit possible.

Key Takeaways

• Operations management (OM) is the administration of business practices to create the highest level of efficiency possible within an organization.
• Operations management is concerned with converting materials and labor into goods and services as efficiently as possible.
• Corporate operations management professionals try to balance costs with revenue to maximize net operating profit.

Katie Kerpel / Investopedia

Operations management involves utilizing resources from staff, materials, equipment, and technology. Operations managers acquire, develop, and deliver goods to clients based on client needs and the abilities of the company.

Operations management handles various strategic issues, including determining the size of manufacturing plants and project management methods and implementing the structure of information technology networks. Other operational issues include the management of inventory levels, including work-in-process levels and raw materials acquisition, quality control, materials handling, and maintenance policies.

Operations management entails studying the use of raw materials and ensuring that minimal waste occurs. Operations managers use numerous formulas, such as the economic order quantity formula, to determine when and how large an inventory order to process and how much inventory to hold on hand.

The combination of understanding and coordinating the work of a company is central to becoming a successful operations manager.

A critical function of operations management relates to the management of inventory through the supply chain. This process is known as operations and supply chain management (OSCM). To be an effective operations management professional, one must be able to understand the processes that are essential to what a company does and get them to flow and work together seamlessly. The coordination involved in setting up business processes in an efficient way requires a solid understanding of logistics . 

An operations management professional understands local and global trends, customer demand, and available resources for production. Operations management approaches the acquisition of materials and the use of labor in a timely, cost-effective manner to deliver customer expectations. Inventory levels are monitored to ensure that excessive quantities are on hand. Operations management is responsible for finding vendors that supply the appropriate goods at reasonable prices and have the ability to deliver the product when needed.

Another large facet of operations management involves the delivery of goods to customers. This includes ensuring that products are delivered within the agreed time commitment. Operations management also typically follows up with customers to ensure that the products meet quality and functionality needs. Finally, operations management takes the feedback received and distributes the relevant information to each department to use in process improvement.

Operations managers are involved in coordinating and developing new processes while reevaluating current structures. Organization and productivity are two key drivers of being an operations manager, and the work often requires versatility and innovation. As part of their daily responsibilities, operations managers must possess a variety of skill sets, including:

• Technical expertise in areas such as production automation, data entry, budget tracking, and design.
• Organizational ability and attention to detail to include keeping track of project files, employee reports, budgets, schedules, and other details related to company processes.
• Motivational prowess in the form of strong leadership skills that provide the expertise to motivate others, inspire ideas, and foster a supportive and diverse team.
• Analytical aptitude , including skill in risk analysis and mitigation when initiating new projects. Operations managers also must analyze processes to identify challenges and offer solutions in the event that negative situations develop.
• Decision-making proficiency , especially under stress when there is very little time to assess all factors.
• Ability to maintain quality standards , including as they relate to raw materials, machinery, manufacturing procedures, packaging, delivery processes, and the finished product.

A master of business administration (MBA) degree in operations management can provide global perspective on industry trends and an awareness of financial regulations and political uncertainties that can affect an organization. It also provides a solid grasp of the inherent complexities and the tools needed to respond well to change.

What is the purpose of operations management (OM)?

Operations management (OM) is concerned with controlling the production process and business operations in the most efficient manner possible. OM professionals attempt to balance operating costs with revenue to maximize net operating profit.

What are some systems of operations management?

Modern operations management revolves around four theories:

• Business process redesign (BPR) , which is focused on analyzing and designing  workflow  and business processes within a company. The goal of BPR is to help companies dramatically restructure the organization by designing the business process from the ground up.
• Reconfigurable manufacturing systems , designed to incorporate accelerated change in structure, hardware, and software components. This allows systems to adjust rapidly to the capacity to which they can continue production and how efficiently they function in response to market or intrinsic system changes.
• Six Sigma , an approach that focuses on quality. The word “six” references the control limits, which are placed at six  standard deviations  from the normal distribution mean. Tools used within the Six Sigma process include trending charts, potential defect calculations, and other ratios.
• Lean manufacturing , which is the systematic elimination of waste within the manufacturing process. This theory sees resource use for any reason other than value creation for customers as wasteful and seeks to eliminate wasteful resource expenditures as much as possible.

What is an example of operations management?

Operations management is prevalent in the healthcare sector . The current healthcare system overuses expensive, technological, and emergency-based treatment. High costs from care often remain uncompensated due to uninsured patients. A prevalence of services in expensive settings creates a burden on taxpayers, health insurance holders, and healthcare institutions.

In simple terms, operations management (OM) is the process of prioritizing and employing business practices designed to achieve maximum efficiency as a means to achieve maximum profitability. Within the process, operations managers utilize organization and productivity to achieve their primary goals.

Balancing the efficient use of resources, including staff, materials, equipment, and technology, is key to a successful OM process and, by extension, to the success of the company.

University at Buffalo, School of Management. “ Operations and Supply Chain Management .”

Indeed. “ 15 Essential Operational Management Skills .”

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Operational Definition Psychology – Definition, Examples, and How to Write One

Elizabeth Research

Every good psychology study contains an operational definition for the variables in the research. An operational definition allows the researchers to describe in a specific way what they mean when they use a certain term. Generally, operational definitions are concrete and measurable. Defining variables in this way allows other people to see if the research has validity . Validity here refers to if the researchers are actually measuring what they intended to measure.

Definition: An operational definition is the statement of procedures the researcher is going to use in order to measure a specific variable.

We need operational definitions in psychology so that we know exactly what researchers are talking about when they refer to something. There might be different definitions of words depending on the context in which the word is used. Think about how words mean something different to people from different cultures. To avoid any confusion about definitions, in research we explain clearly what we mean when we use a certain term.

Operational Definition Examples

Example one:.

A researcher wants to measure if age is related to addiction. Perhaps their hypothesis is: the incidence of addiction will increase with age. Here we have two variables, age and addiction. In order to make the research as clear as possible, the researcher must define how they will measure these variables. Essentially, how do we measure someone’s age and how to we measure addiction?

Variable One: Age might seem straightforward. You might be wondering why we need to define age if we all know what age is. However, one researcher might decide to measure age in months in order to get someone’s precise age, while another researcher might just choose to measure age in years. In order to understand the results of the study, we will need to know how this researcher operationalized age. For the sake of this example lets say that age is defined as how old someone is in years.

Variable Two: The variable of addiction is slightly more complicated than age. In order to operationalize it the researcher has to decide exactly how they want to measure addiction. They might narrow down their definition and say that addiction is defined as going through withdrawal when the person stops using a substance. Or the researchers might decide that the definition of addiction is: if someone currently meets the DSM-5 diagnostic criteria for any substance use disorder. For the sake of this example, let’s say that the researcher chose the latter.

Final Definition: In this research study age is defined as participant’s age measured in years and the incidence of addiction is defined as whether or not the participant currently meets the DSM-5 diagnostic criteria for any substance use disorder.

Example Two

A researcher wants to measure if there is a correlation between hot weather and violent crime. Perhaps their guiding hypothesis is: as temperature increases so will violent crime. Here we have two variables, weather and violent crime. In order to make this research precise the researcher will have to operationalize the variables.

Variable One: The first variable is weather. The researcher needs to decide how to define weather. Researchers might chose to define weather as outside temperature in degrees Fahrenheit. But we need to get a little more specific because there is not one stable temperature throughout the day. So the researchers might say that weather is defined as the high recorded temperature for the day measured in degrees Fahrenheit.

Variable Two: The second variable is violent crime. Again, the researcher needs to define how violent crime is measured. Let’s say that for this study it they use the FBI’s definition of violent crime . This definition describes violent crime as “murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault”.

However, how do we actually know how many violent crimes were committed on a given day? Researchers might include in the definition something like: the number of people arrested that day for violent crimes as recorded by the local police.

Final Definition: For this study temperature was defined as high recorded temperature for the day measured in degrees Fahrenheit. Violent crime was defined as the number of people arrested in a given day for murder, forcible rape, robbery, and aggravated assault as recorded by the local police.

How to Write an Operational Definition

For the last example take the opportunity to see if you can write a clear operational definition for yourself. Imagine that you are creating a research study and you want to see if group therapy is helpful for treating social anxiety.

Variable One: How are you going to define group therapy? here are some things you might want to consider when creating your operational definition:

• What type of group therapy?
• Who is leading the therapy group?
• How long do people participate in the therapy group for?
• How can you “measure” group therapy?

There is no one way to write the operational definition for this variable. You could say something like group therapy was defined as a weekly cognitive behavioral therapy group led by a licensed MFT held over the course of ten weeks. Remember there are many ways to write an operational definition. You know you have written an effective one if another researcher could pick it up and create a very similar variable based on your definition.

Variable Two: The second variable you need to define is “effective treatment social anxiety”. Again, see if you can come up with an operational definition of this variable. This is a little tricky because you will need to be specific about what an effective treatment is as well as what social anxiety is. Here are some things to consider when writing your definition:

• How do you know a treatment is effective?
• How do you measure the effectiveness of treatment?
• Who provides a reliable definition of social anxiety?
• How can you measure social anxiety?

Again, there is no one right way to write this operational definition. If someone else could recreate the study using your definition it is probably an effective one. Here as one example of how you could operationalize the variable: social anxiety was defined as meeting the DSM-5 criteria for social anxiety and the effectiveness of treatment was defined as the reduction of social anxiety symptoms over the 10 week treatment period.

Final Definition: Take your definition for variable one and your definition for variable two and write them in a clear and succinct way. It is alright for your definition to be more than one sentence.

Why We Need Operational Definitions

There are a number of reasons why researchers need to have operational definitions including:

• Replicability
• Generalizability
• Dissemination

The first reason was mentioned earlier in the post when reading research others should be able to assess the validity of the research. That is, did the researchers measure what they intended to measure? If we don’t know how researchers measured something it is very hard to know if the study had validity.

The next reason it is important to have an operational definition is for the sake of replicability . Research should be designed so that if someone else wanted to replicate it they could. By replicating research and getting the same findings we validate the findings. It is impossible to recreate a study if we are unsure about how they defined or measured the variables.

Another reason we need operational definitions is so that we can understand how generalizable the findings are. In research, we want to know that the findings are true not just for a small sample of people. We hope to get findings that generalize to the whole population. If we do not have operational definitions it is hard to generalize the findings because we don’t know who they generalize to.

Finally, operational definitions are important for the dissemination of information. When a study is done it is generally published in a peer-reviewed journal and might be read by other psychologists, students, or journalists. Researchers want people to read their research and apply their findings. If the person reading the article doesn’t know what they are talking about because a variable is not clear it will be hard to them to actually apply this new knowledge.

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Assignment Problem: Meaning, Methods and Variations | Operations Research

After reading this article you will learn about:- 1. Meaning of Assignment Problem 2. Definition of Assignment Problem 3. Mathematical Formulation 4. Hungarian Method 5. Variations.

Meaning of Assignment Problem:

An assignment problem is a particular case of transportation problem where the objective is to assign a number of resources to an equal number of activities so as to minimise total cost or maximize total profit of allocation.

The problem of assignment arises because available resources such as men, machines etc. have varying degrees of efficiency for performing different activities, therefore, cost, profit or loss of performing the different activities is different.

Thus, the problem is “How should the assignments be made so as to optimize the given objective”. Some of the problem where the assignment technique may be useful are assignment of workers to machines, salesman to different sales areas.

Definition of Assignment Problem:

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Suppose there are n jobs to be performed and n persons are available for doing these jobs. Assume that each person can do each job at a term, though with varying degree of efficiency, let c ij be the cost if the i-th person is assigned to the j-th job. The problem is to find an assignment (which job should be assigned to which person one on-one basis) So that the total cost of performing all jobs is minimum, problem of this kind are known as assignment problem.

The assignment problem can be stated in the form of n x n cost matrix C real members as given in the following table:

If you were alone in the woods, would you rather encounter a bear or a man? Answers to that hypothetical question have sparked a debate about why the vast majority say they would feel more comfortable choosing a bear.

The topic has been hotly discussed for weeks as men and women chimed in with their thoughts all over social media.

Screenshot HQ , a TikTok account, started the conversation, asking a group of women whether they would rather run into a man they didn't know or a bear in the forest. Out of the seven women interviewed for the piece, only one picked a man.

"Bear. Man is scary," one of the women responds.

A number of women echoed the responses given in the original video, writing in the comments that they, too, would pick a bear over a man. The hypothetical has people split, with some expressing their sadness over the state of the world and others cracking jokes. Some men were flabbergasted.

Here's what we know.

A bear is the safer choice, no doubt about it, many say

There were a lot of responses, more than 65,000, under the original post. Many wrote that they understood why the women would choose a bear.

"No one’s gonna ask me if I led the bear on or give me a pamphlet on bear attack prevention tips," @celestiallystunning wrote.

@Brennduhh wrote: "When I die leave my body in the woods, the wolves will be gentler than any man."

"I know a bear's intentions," another woman wrote. "I don't know a man's intentions. no matter how nice they are."

Other TikTok users took it one step further, posing the hypothetical question to loved ones. Meredith Steele, who goes by @babiesofsteele , asked her husband last week whether he would rather have their daughter encounter a bear or a man in the woods. Her husband said he "didn't like either option" but said he was leaning toward the bear.

"Maybe it's a friendly bear," he says.

Diana, another TikTok user , asked her sister-in-law what she would choose and was left speechless.

"I asked her the question, you know, just for giggles. She was like, 'You know, I would rather it be a bear because if the bear attacks me, and I make it out of the woods, everybody’s gonna believe me and have sympathy for me," she said. "But if a man attacks me and I make it out, I’m gonna spend my whole life trying to get people to believe me and have sympathy for me.'"

Bear vs. man debate stirs the pot, woman and some men at odds

The hypothetical has caused some tension, with some women arguing that men will never truly understand what it's like to be a woman or the inherent dangers at play.

Social media users answered this question for themselves, producing memes, spoken word poetry and skits in the days and weeks since.

So, what would you choose?

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Middle East crisis — explained

The conflict between Israel and Palestinians — and other groups in the Middle East — goes back decades. These stories provide context for current developments and the history that led up to them.

What to know about a possible Israeli military offensive in Rafah

Becky Sullivan

Aya Batrawy

Amid the rubble of a collapsed building in Rafah, in southern Gaza, a woman and a girl search for items on April 24, following reported Israeli airstrikes overnight. Mohammed Abed/AFP via Getty Images hide caption

Amid the rubble of a collapsed building in Rafah, in southern Gaza, a woman and a girl search for items on April 24, following reported Israeli airstrikes overnight.

RAFAH, Gaza Strip — Some of the more than 1 million displaced Palestinians in Gaza's southern city of Rafah are weighing the risks of whether to stay or flee as concerns grow of a military offensive there that Israel says is necessary for its war aims.

Israeli Prime Minister Benjamin Netanyahu approved the military's plan for an attack on Rafah last month, but there's still no publicly announced time frame for an assault.

Several recent developments, though, indicate the military may be laying the groundwork for its plans. This week, the military announced it had called up two reserve brigades for duty in Gaza. Satellite images examined by NPR show several new tent encampments, which can hold thousands of people, have been erected this month in areas north of Rafah. There have also been almost daily airstrikes on the city in recent weeks, indicating increased pressure by the military on Rafah.

Middle East

Even before a planned israeli ground offensive, airstrikes have made rafah unsafe.

Israel says an offensive is necessary to eliminate Hamas and free the remaining 133 hostages taken captive in the Oct. 7 attack, most of whom are believed to be alive. Israel earlier this year freed two hostages from Rafah in an operation that killed scores of Palestinians.

Abdullah Omar is one of the many displaced people sheltering in Rafah. The accountant, who fled Gaza City with his family months ago, said the thought of an invasion makes him feel paralyzed before his children.

Like many people in Rafah, he's sheltering in a crowded apartment with other families. There are babies, elderly and ill people among them, he said. The idea of relocating to a tent is painful, he said, because his wife is still breastfeeding their youngest child and needs privacy. The prospect of an offensive is "one of the most terrifying things for us," he said.

Refugee tents near Rafah on April 23. Since April 1, hundreds of new tents have appeared at the very northern edge of Rafah. Maxar hide caption

Refugee tents near Rafah on April 23. Since April 1, hundreds of new tents have appeared at the very northern edge of Rafah.

Threats of an invasion have been a tool that Israel has used in negotiations with Hamas for a temporary cease-fire to release hostages, but those talks — mediated by Qatar and Egypt — have stalled, in large part over the duration of a cease-fire, as Israel insists it must take Rafah and dismantle Hamas battalions it says operate there.

But with those talks at an impasse now for weeks, there's growing worry in Rafah of an impending assault. There's a "deep anxiety prevailing in the south about the possible, looming, upcoming military offensive, which seems to be back on the table," Philippe Lazzarini, the head of the U.N. relief agency for Palestinians, said this week.

What we know about Israel's plans

A senior Egyptian official told NPR that Israeli intelligence officials have indicated five areas in Rafah where they say tunnels and militant hideouts are present. The official spoke on condition of anonymity to discuss the sensitive nature of the discussions. The official disputed Israeli claims of tunnels in some of those areas.

"Hamas should know that when the IDF [Israel Defense Forces] goes into Rafah, it would do best to raise its hands in surrender. Rafah will not be the Rafah of today," Israeli Brig. Gen. Itzik Cohen told Israeli public broadcaster Kan on Tuesday, adding that the city would be free of arms and hostages.

Why Egypt won't allow vulnerable Palestinians across its border

For weeks, senior Israeli officials and members of the Biden administration have been in talks about Israel's plans for an offensive in Rafah. Egyptians officials tell NPR that some of those plans have also been shared with Egypt.

Ahmed Barhoum, displaced and now in Rafah, holds the body of his daughter Alaa, 5, at a morgue on April 20. Israeli airstrikes the night before killed the child, her mother and seven other people, most of them children sheltering in a home in Rafah. Abed Rahim Khatib/Anadolu via Getty Images hide caption

Ahmed Barhoum, displaced and now in Rafah, holds the body of his daughter Alaa, 5, at a morgue on April 20. Israeli airstrikes the night before killed the child, her mother and seven other people, most of them children sheltering in a home in Rafah.

Multiple officials with knowledge of the discussions have expressed worry about the possibility of a high death toll among Palestinian civilians as Israel targets areas with suspected tunnels.

The U.S. has pressed Israel to pursue "alternate ways" of addressing a Hamas military presence in Rafah, said Ambassador David Satterfield, the U.S. special envoy for Middle East humanitarian issues.

"We think there are other ways to deal with this. And if there is not a credible, executable humanitarian plan, then we cannot support a ground operation," Satterfield told reporters Tuesday.

For its part, Egypt issued a statement this week denying "any dealings with Israel" regarding Rafah, and it reiterated its strong opposition to an offensive in Gaza along Egypt's border, saying it "will lead to massacres, massive human losses, and widespread destruction."

Tents are being built that can hold thousands of people

NPR has identified at least four large tent encampments erected north of Rafah over the past month. Commercial satellite imagery shows two were erected near Khan Younis over the past week.

A mass evacuation of Palestinians from Rafah could take weeks, and there's no confirmation these tents are being set up for that purpose. The Israeli military declined to comment when asked by NPR about the new tents as seen on satellite imagery.

Israel's Ministry of Defense said this month that it planned to purchase 40,000 tents to house Palestinians displaced from Rafah. With a capacity of up to 12 people in each, the tents could, in theory, house up to 480,000 Palestinians — about a third of the number of people thought to be sheltering in Rafah.

Palestinians in the Gaza Strip who fled Israeli attacks and took refuge in Rafah live in makeshift tents with limited means and under harsh conditions, as seen here on April 22. Abed Rahim Khatib/Anadolu via Getty Images hide caption

Palestinians in the Gaza Strip who fled Israeli attacks and took refuge in Rafah live in makeshift tents with limited means and under harsh conditions, as seen here on April 22.

The U.S. says it wants to see a humanitarian plan for Rafah that includes shelters and access to health care and aid for civilians.

"There are currently somewhere around 1.4 million people in Rafah — many of them displaced from other parts of Gaza. It's imperative that people are able to get out of the way of any conflict, and doing so is a monumental task for which we have yet to see a plan," U.S. Secretary of State Antony Blinken said last week.

He said it's not enough to just move people out of harm's way. He said it's imperative that "they can be supported with humanitarian assistance."

An official with the Egyptian Red Crescent told NPR that the aid organization is involved in building new tents in Gaza, though he would not comment on the specific location of these efforts, citing security concerns. The official spoke anonymously to discuss the ongoing work. A spokesperson at the Egyptian Red Crescent declined to comment.

Egypt is concerned that an Israeli assault on Rafah could forcibly displace Palestinians into Egypt's Sinai Peninsula . Egyptian President Abdel Fattah el-Sisi has said this could drag Egypt into the war and facilitate a mass exodus of Gazans who may never be allowed to return.

Aid groups say a Rafah operation would be "catastrophic"

Humanitarian groups and U.N. agencies have based their operations out of Rafah for much of the war. It's the only exit point for wounded Palestinians seeking treatment abroad and the few who can afford expensive visas to leave. It's also how aid workers and much of Gaza's humanitarian aid enters.

For the 68 orphans saved from Rafah, the road to recovery is likely to be a long one

War has forced half of Gaza into Rafah. Palestinians there are at a breaking point

A military offensive in Rafah would be "catastrophic," says Samah Hadid, a spokesperson for the Norwegian Refugee Council, which provides food, water and other humanitarian supplies to displaced Palestinians. "Any offensive would just cause the aid response to come to a complete collapse," she says.

Like most major aid groups, the Norwegian Refugee Council does not currently have set plans to evacuate, says Hadid. In the event that an offensive does begin, she says, "we would hope to stay and to deliver support to the displaced population as much as possible and as safely as possible."

Several humanitarian organizations had already suspended operations in Gaza this month after an Israeli airstrike on a World Central Kitchen aid convoy killed seven of the organization's workers.

A majority of organizations working in Rafah have contingency plans for an evacuation. But those plans cannot be fully effective without more credible information from Israel, said Joseph Kelly, the director of the Association of International Development Agencies, an organization that coordinates with aid groups working in Gaza.

"To the best of their ability, they're stockpiling aid. They're looking at certain locations [north of Rafah] such as Al-Mawasi, Deir al-Balah and parts of Khan Younis where there's some level of structural integrity to serve people that would eventually be pushed there," he said.

COGAT, the Israeli agency responsible for Palestinian affairs, has said it will notify aid groups "in a reasonable amount of time," Kelly said, but the agency has not specified how soon that warning will come.

Palestinians say there's no safe place to go

In March, a targeted raid on Gaza City's Al-Shifa hospital killed 200 militants, according to Israel's military, which hailed the raid as a model. The fighting also decimated the hospital, and the Palestinian civil defense says hundreds of bodies, many of them civilians, are still being recovered in the city, which is sparsely populated these days compared with Rafah.

What is Gaza's Rafah crossing and why is it important?

The United Nations says more than a million Palestinians are sheltering in Rafah, a fourfold increase from the city's prewar population and more than half of the Gaza Strip's total population. Many of the displaced individuals have crowded by the dozens into apartments or houses owned by extended family or friends. Others have lived for months in tents or other temporary structures .

Hadi Al-Sayyed, who was displaced from his home in Gaza City and now lives in an unused storefront with his family, says people are slowly dying in Rafah and being targeted in airstrikes there too.

"When they tell us to go to any place, we will go, but only if they provide us with a place to live and provide water and food — not just throw us in the desert and tell us 'survive,'" he says.

Bodies of employees of the aid organization World Central Kitchen who were killed in an Israeli attack on April 1 are taken from Al-Najjar hospital and sent by ambulances to Egypt through the Rafah border crossing in the Gaza Strip on April 3. Abed Rahim Khatib/Anadolu via Getty Images hide caption

Bodies of employees of the aid organization World Central Kitchen who were killed in an Israeli attack on April 1 are taken from Al-Najjar hospital and sent by ambulances to Egypt through the Rafah border crossing in the Gaza Strip on April 3.

Already, though, Rafah is unsafe for civilians. Israeli airstrikes have ramped up over the past month. More than 230 people have been killed in airstrikes in Rafah since March 21, with three-quarters of the victims women and children, according to Yousef Ibrahim, who works with the Palestinian Centre for Human Rights and compiles data from hospitals about the strikes on his city.

Al-Sayyed says people's patience has run out. He says people might try to climb the border fence with Egypt or try to return to their homes in Gaza City, where Israeli tanks have cut off access, saying militants could try to regroup there.

He says people don't want foreign aid or assistance or even a cease-fire. "We want the war to end," he said.

Reporting by Becky Sullivan in Tel Aviv, Israel; Aya Batrawy in Dubai, United Arab Emirates; Anas Baba in Rafah, Gaza Strip; and Jane Arraf in Amman, Jordan. Additional reporting from Michele Kelemen and Itay Stern in Tel Aviv; Ahmed Abuhamda in Cairo; and Geoff Brumfiel in Washington, D.C.

• Israel Hamas War

College protesters are demanding schools 'divest' from companies with ties to Israel. Here's what that means.

In addition to a cease-fire in Gaza, protesters on college campuses across the country are calling on their schools to divest from all financial support of Israel.

Divestment usually refers to selling shares in companies doing business with a given country. Divestment has long been a goal of a movement that seeks to limit what it considers hostile operations by Israel and an end to expanding what the United Nations has ruled are illegal settlements.

Now, college protesters are hoping to force their universities to divest to put financial pressure on companies doing business in Israel to meet those two objectives.

“The university should do something about what we’re asking for, about the genocide that’s happening in Gaza,” said Columbia University student and protest leader Mahmoud Khalil, who is Palestinian, and noted that students have been pushing for Columbia to divest from Israel since 2002. “They should stop investing in this genocide.”

Israel launched its Gaza campaign soon after the Oct. 7 attack by Hamas , a U.S.-designated terror group that left 1,200 Israelis dead, according to officials, with an estimated 250 people taken hostage. The subsequent military response by Israel has killed more than 34,000 Palestinians, according to the Gaza Health Ministry.

Like many universities, Columbia owns shares of various companies as part of its financial operations and endowment. However, information on Columbia’s exact holdings was not immediately available, and it was not clear whether investment information published by Columbia University Apartheid Divest (CUAD), the group leading the protests at the school, was accurate.

Whatever the case, while some of the shares Columbia owns may be directly held stock investments, other assets are likely held indirectly through investment instruments like mutual funds or exchange-traded funds that are designed to expose investors to a variety of firms.

And as students at Brown University acknowledged in a separate proposal targeting their school’s alleged Israel-tied investments, excluding specific investments from these indirect stock holding products “would be logistically challenging.”

In fact, they concluded that none of their school’s current direct investments appeared to be in individual companies violating its anti-Israel screening criteria.

Meanwhile, mutual fund and ETF holdings are constantly changing, the Brown students said.

The actual mechanics of divestment thus make it a more difficult undertaking than it may first appear, said Alison Taylor, clinical associate professor at New York University’s Stern School of Business.

“You get into questions of, ‘What percentage of a company’s business is actually tied to the activities in question?’” Taylor said.

Columbia’s Investment Management Company, which oversees the school’s market assets, does have an advisory committee on socially responsible investing.

This committee has pledged that it will screen against investing in firms that operate private prisons; derive significant revenues from thermal coal; and engage in tobacco manufacturing. It also has had a policy against investing in companies doing business in Sudan. 

So there is precedent for Columbia to limit its financial exposure to socially irresponsible firms, CUAD says. At present, Columbia’s investment in the companies that CUAD accuses of having ties to Israel makes it “complicit in genocide,” CUAD says. 

“By withdrawing from holdings that profit off of Israeli human rights violations, Columbia can invest in other, more worthwhile companies,” CUAD says in a December proposal submitted to the socially responsible investing committee calling for divestment.

A representative for Columbia’s responsible investment committee did not respond to a request for comment.

Columbia President Minouche Shafik has not specifically addressed the divestment calls in her statements on the campus turmoil. Her predecessor, Lee Bollinger, rejected calls for divestment in 2020, saying that a vote by students calling for one merely represented “particular views about a complex policy issue” and that there was “no consensus across the University community about” the issue.

Sylvia Burwell, the president of American University in Washington, D.C., said a student vote calling for divestment did not represent the school and would not be recognized.

“It is AU’s longstanding position to oppose boycotts, divestment from Israel, and other related actions known as BDS,” she said in a statement last week, referring to a Palestinian-led movement called Boycott, Divestment and Sanctions.

“Such actions threaten academic freedom, the respectful free expression of ideas and views, and the values of inclusion and belonging that are central to our community.”

There is debate about the efficacy of divestment. Some evidence suggests that the buyers of shares being sold off as part of a divestment campaign can sometimes turn out to be worse actors than their original holders, NYU’s Taylor said.

She gave the example of Myanmar, where energy giant Chevron ending up selling off an asset to an entity that human rights groups said was even less accountable.

Divestment supporters often cite the successful campaign to dismantle South Africa’s apartheid regime as an example of what can be accomplished.

But Taylor and others have said the groundswell of international and civil-society support that was needed to end apartheid has not materialized in the Israel-Palestine conflict.

“It required everyone to be at the table,” she said. “South Africa is a  good story, but I don’t know whether we’re there yet.”

In a 2021 study of the impact of divestment, business school professors at the University of Pennsylvania and Stanford University found the practice had little impact.

Instead, they said, activists seeking to change a company’s behavior should instead hold on to their investments and exercise any rights of control they may have to change corporate policy.

The Anti-Defamation League, a nonprofit that seeks to counter antisemitism, likewise concluded that shareholder resolutions have proven effective at changing corporate behavior, even as it criticized such efforts as “simplistic” and not constructive.

“Organizations file resolutions repeatedly until they get the change they want, often garnering more votes each year,” the ADL said in a 2022 article on its website . “Even if a proposal fails, sometimes just the fact that it is filed may be enough to make a company wary of doing business with Israel.”

Rob Wile is a breaking business news reporter for NBC News Digital.

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• Specifically, the final rule provides that it is an unfair method of competition—and therefore a violation of Section 5 of the FTC Act—for employers to enter into noncompetes with workers after the effective date.
• Fewer than 1% of workers are estimated to be senior executives under the final rule.
• Specifically, the final rule defines the term “senior executive” to refer to workers earning more than $151,164 annually who are in a “policy-making position.”
• Reduced health care costs: $74-$194 billion in reduced spending on physician services over the next decade.
• New business formation: 2.7% increase in the rate of new firm formation, resulting in over 8,500 additional new businesses created each year.
• This reflects an estimated increase of about 3,000 to 5,000 new patents in the first year noncompetes are banned, rising to about 30,000-53,000 in the tenth year.
• This represents an estimated increase of 11-19% annually over a ten-year period.
• The average worker’s earnings will rise an estimated extra $524 per year. 

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