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Descriptive Research in Psychology

Sometimes you need to dig deeper than the pure statistics

John Loeppky is a freelance journalist based in Regina, Saskatchewan, Canada, who has written about disability and health for outlets of all kinds.

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Types of Descriptive Research and the Methods Used

• Advantages & Limitations of Descriptive Research

Best Practices for Conducting Descriptive Research

Descriptive research is one of the key tools needed in any psychology researcher’s toolbox in order to create and lead a project that is both equitable and effective. Because psychology, as a field, loves definitions, let’s start with one. The University of Minnesota’s Introduction to Psychology defines this type of research as one that is “...designed to provide a snapshot of the current state of affairs.” That's pretty broad, so what does that mean in practice? Dr. Heather Derry-Vick (PhD) , an assistant professor in psychiatry at Hackensack Meridian School of Medicine, helps us put it into perspective. "Descriptive research really focuses on defining, understanding, and measuring a phenomenon or an experience," she says. "Not trying to change a person's experience or outcome, or even really looking at the mechanisms for why that might be happening, but more so describing an experience or a process as it unfolds naturally.”

Within the descriptive research methodology there are multiple types, including the following.

Descriptive Survey Research

This involves going beyond a typical tool like a LIkert Scale —where you typically place your response to a prompt on a one to five scale. We already know that scales like this can be ineffective, particularly when studying pain, for example.

When that's the case, using a descriptive methodology can help dig deeper into how a person is thinking, feeling, and acting rather than simply quantifying it in a way that might be unclear or confusing.

Descriptive Observational Research

Think of observational research like an ethically-focused version of people-watching. One example would be watching the patterns of children on a playground—perhaps when looking at a concept like risky play or seeking to observe social behaviors between children of different ages.

Descriptive Case Study Research

A descriptive approach to a case study is akin to a biography of a person, honing in on the experiences of a small group to extrapolate to larger themes. We most commonly see descriptive case studies when those in the psychology field are using past clients as an example to illustrate a point.

Correlational Descriptive Research

While descriptive research is often about the here and now, this form of the methodology allows researchers to make connections between groups of people. As an example from her research, Derry-Vick says she uses this method to identify how gender might play a role in cancer scan anxiety, aka scanxiety.

Dr. Derry-Vick's research uses surveys and interviews to get a sense of how cancer patients are feeling and what they are experiencing both in the course of their treatment and in the lead-up to their next scan, which can be a significant source of stress.

David Marlon, PsyD, MBA , who works as a clinician and as CEO at Vegas Stronger, and whose research focused on leadership styles at community-based clinics, says that using descriptive research allowed him to get beyond the numbers.

In his case, that includes data points like how many unhoused people found stable housing over a certain period or how many people became drug-free—and identify the reasons for those changes.

Those [data points] are some practical, quantitative tools that are helpful. But when I question them on how safe they feel, when I question them on the depth of the bond or the therapeutic alliance, when I talk to them about their processing of traumas,  wellbeing...these are things that don't really fall on to a yes, no, or even on a Likert scale.

For the portion of his thesis that was focused on descriptive research, Marlon used semi-structured interviews to look at the how and the why of transformational leadership and its impact on clinics’ clients and staff.

Advantages & Limitations of Descriptive Research

So, if the advantages of using descriptive research include that it centers the research participants, gives us a clear picture of what is happening to a person in a particular moment,  and gives us very nuanced insights into how a particular situation is being perceived by the very person affected, are there drawbacks? Yes, there are. Dr. Derry-Vick says that it’s important to keep in mind that just because descriptive research tells us something is happening doesn’t mean it necessarily leads us to the resolution of a given problem.

I think that, by design, the descriptive research might not tell you why a phenomenon is happening. So it might tell you, very well, how often it's happening, or what the levels are, or help you understand it in depth. But that may or may not always tell you information about the causes or mechanisms for why something is happening.

Another limitation she identifies is that it also can’t tell you, on its own, whether a particular treatment pathway is having the desired effect.

“Descriptive research in and of itself can't really tell you whether a specific approach is going to be helpful until you take in a different approach to actually test it.”

Marlon, who believes in a multi-disciplinary approach, says that his subfield—addictions—is one where descriptive research had its limits, but helps readers go beyond preconceived notions of what addictions treatment looks and feels like when it is effective. “If we talked to and interviewed and got descriptive information from the clinicians and the clients, a much more precise picture would be painted, showing the need for a client's specific multidisciplinary approach augmented with a variety of modalities," he says. "If you tried to look at my discipline in a pure quantitative approach , it wouldn't begin to tell the real story.”

Because you’re controlling far fewer variables than other forms of research, it’s important to identify whether those you are describing, your study participants, should be informed that they are part of a study.

For example, if you’re observing and describing who is buying what in a grocery store to identify patterns, then you might not need to identify yourself.

However, if you’re asking people about their fear of certain treatment, or how their marginalized identities impact their mental health in a particular way, there is far more of a pressure to think deeply about how you, as the researcher, are connected to the people you are researching.

Many descriptive research projects use interviews as a form of research gathering and, as a result, descriptive research that is focused on this type of data gathering also has ethical and practical concerns attached. Thankfully, there are plenty of guides from established researchers about how to best conduct these interviews and/or formulate surveys .

While descriptive research has its limits, it is commonly used by researchers to get a clear vantage point on what is happening in a given situation.

Tools like surveys, interviews, and observation are often employed to dive deeper into a given issue and really highlight the human element in psychological research. At its core, descriptive research is rooted in a collaborative style that allows deeper insights when used effectively.

University of Minnesota. Introduction to Psychology .

By John Loeppky John Loeppky is a freelance journalist based in Regina, Saskatchewan, Canada, who has written about disability and health for outlets of all kinds.

Pros and Cons of Descriptive Research

Are you curious about the pros and cons of descriptive research? Well, buckle up because we're about to take a deep dive into this fascinating topic.

Descriptive research, like a pair of binoculars, allows you to zoom in and examine a specific phenomenon. However, just like any tool, it has its limitations.

In this article, we'll explore the advantages, limitations, importance, applications, challenges, ethical considerations, and recommendations associated with descriptive research.

So, get ready to uncover the secrets and complexities of this research method!

Key Takeaways

• Provides valuable insights into the characteristics and behavior of a specific population or phenomenon.
• Allows for the exploration of patterns and trends in data.
• Can be used to generate hypotheses for further research.
• Provides a detailed description of the variables under study.

Advantages of Descriptive Research

You should consider the benefits of using descriptive research for gathering data. Descriptive research allows you to obtain a clear and detailed picture of a particular phenomenon or topic. It provides a comprehensive understanding of the subject matter and helps you to identify patterns, trends, and relationships. By using this research method, you can collect a large amount of data from a wide range of sources, such as surveys, observations, and existing records. This data can then be analyzed to draw meaningful conclusions and make informed decisions.

One of the main advantages of descriptive research is its flexibility. It can be used in various fields and disciplines, making it suitable for a wide range of research objectives. Whether you're studying consumer behavior, conducting market research, or investigating social issues, descriptive research can provide valuable insights.

Additionally, descriptive research is relatively quick and cost-effective. It doesn't require extensive resources or complex procedures, which makes it an accessible option for researchers with limited budgets or time constraints. Moreover, descriptive research can be easily replicated, allowing others to validate and build upon your findings.

Overall, using descriptive research can greatly enhance your understanding of a specific topic or phenomenon. It provides a solid foundation for further research and enables you to make evidence-based decisions. So, next time you need to gather data, consider the benefits of descriptive research.

Limitations of Descriptive Research

While descriptive research provides a comprehensive understanding of a subject, it may have limitations in terms of generalizability to larger populations. Descriptive research is a valuable tool for collecting and analyzing data to gain insights into a specific phenomenon. However, it is important to recognize its limitations when it comes to making broader claims about a population as a whole.

Here is a table that outlines the pros and cons of descriptive research:

Descriptive research excels at providing in-depth information and exploring complex phenomena. It is particularly useful for generating hypotheses and understanding the intricacies of a specific subject. However, its limitations in terms of generalizability to larger populations, lack of control over variables, and potential for researcher bias must be taken into account. It is important to consider these factors when interpreting the results of descriptive research and drawing conclusions.

Importance of Descriptive Research

Descriptive research is crucial because it allows you to gather detailed information and gain a deeper understanding of a specific subject. By conducting this type of research, you can explore various aspects of a topic and collect data that's both comprehensive and accurate. This information can then be used to make informed decisions, develop effective strategies, and formulate hypotheses for further investigation.

One of the key benefits of descriptive research is its ability to provide a comprehensive overview of a particular subject. It allows you to collect data from a large sample size, enabling you to generalize the findings to a larger population. This can be especially useful in fields such as psychology, sociology, and market research, where understanding human behavior and preferences is essential.

Another advantage of descriptive research is that it allows you to identify patterns and trends within the data. By analyzing the gathered information, you can uncover relationships between variables, identify potential causality, and make predictions about future outcomes. This can be particularly valuable in fields such as medicine and epidemiology, where understanding the prevalence and risk factors of diseases is crucial for prevention and treatment.

In addition, descriptive research can also help you explore new areas of study and generate hypotheses for further research. By examining a subject in detail, you may uncover new variables, concepts, or relationships that were previously unknown or overlooked. This can lead to the development of new theories and contribute to the advancement of knowledge in a particular field.

However, it's important to acknowledge that descriptive research also has its limitations. It doesn't provide explanations or establish causality between variables, as it primarily focuses on describing and summarizing data. Furthermore, the data collected through descriptive research is often based on self-report measures, which may be subject to biases and inaccuracies.

Applications of Descriptive Research

Descriptive research has various applications that can be beneficial to you. It can help you in:

• Selecting the appropriate research design based on your objectives
• Choosing the most suitable data collection methods
• Analyzing and interpreting the results effectively

Research Design Selection

You should consider the various factors when choosing a research design for your study. The research design you select will have a significant impact on the validity and reliability of your findings.

One important factor to consider is the nature of your research question. If you're seeking to explore relationships between variables, a correlational design may be appropriate. On the other hand, if you're interested in determining cause and effect relationships, an experimental design may be more suitable.

Another factor to consider is the feasibility of implementing the design. You should assess whether you have access to the necessary resources, participants, and time to carry out the design effectively.

Additionally, ethical considerations should be taken into account to ensure that your study doesn't harm participants or violate any ethical guidelines.

Data Collection Methods

While there are various data collection methods available, it's important to consider the strengths and limitations of each method in order to choose the most suitable one for your study.

One common data collection method is surveys, which allow you to gather information from a large number of participants quickly and efficiently. Surveys can be conducted online or in person, and they provide quantitative data that can be easily analyzed. However, surveys may be limited by response bias and the inability to capture in-depth information.

Another method is interviews, which allow for a more in-depth exploration of participants' experiences and perspectives. Interviews can provide rich qualitative data, but they can be time-consuming and may be influenced by interviewer bias.

Lastly, observations involve directly observing and recording behaviors in their natural setting. Observations provide detailed and objective data, but they can be time-consuming and may be influenced by the observer's presence.

Consider the strengths and limitations of each method to ensure that your data collection aligns with your research goals.

Analyzing and Interpreting Results

Once you have gathered the data, it's essential to carefully analyze and interpret the results to gain meaningful insights for your study. This step is crucial in understanding the implications and significance of your findings. Here are some key points to consider when analyzing and interpreting your results:

• Data Accuracy and Reliability
• Ensure that your data is accurate and reliable by checking for any errors or inconsistencies.
• Verify the validity of your data by cross-referencing it with other sources or conducting additional tests if necessary.
• Identifying Patterns and Trends
• Look for patterns or trends in your data that can provide valuable insights into your research question.
• Identify any unexpected or interesting findings that may require further exploration or explanation.

Challenges in Descriptive Research

When conducting descriptive research, you may encounter challenges that can impact the quality of your data. One major challenge is ensuring data accuracy, as it requires careful collection and recording to minimize errors.

Additionally, sampling difficulties may arise when selecting a representative sample from a larger population, which can affect the generalizability of your findings.

Data Accuracy Challenges

You need to address the data accuracy challenges in your descriptive research. Ensuring the accuracy of your data is crucial for drawing valid conclusions and making informed decisions.

Here are some key challenges you may encounter:

• Human error: Mistakes made during data collection or entry can introduce inaccuracies that can compromise the validity of your findings.
• Sampling bias: If your sample isn't representative of the population you're studying, the results may not be generalizable.
• Selection bias: Choosing participants based on certain characteristics may lead to biased results.
• Non-response bias: When participants choose not to respond, it can introduce bias into your data.

Sampling Difficulties Faced

Addressing sampling difficulties is crucial in descriptive research to ensure the representativeness of your sample and the validity of your findings. When conducting descriptive research, you need to carefully select your sample in order to obtain accurate and reliable results.

However, sampling difficulties can pose challenges that need to be addressed. One common difficulty is obtaining a truly random sample, as it can be difficult to reach every member of the population of interest. Another challenge is ensuring that your sample is representative of the population, as biased or unrepresentative samples can lead to skewed results.

Additionally, it can be challenging to obtain a large enough sample size to ensure statistical power. Therefore, it's important to carefully plan your sampling strategy and employ appropriate techniques to overcome these difficulties and obtain a sample that accurately reflects the population, ensuring the validity and generalizability of your findings.

Limitations in Generalizability

To ensure the generalizability of your findings in descriptive research, it's important to acknowledge the limitations in generalizability and consider alternative research designs. Descriptive research provides valuable insights into the characteristics and behavior of a specific population or phenomenon. However, it also has its limitations that can affect the generalizability of the findings. Here are some key points to consider:

• Limitations in Sample Size:
• Small sample sizes may not accurately represent the larger population.
• Generalizing findings from a small sample to a larger population can be risky.
• Limitations in Sampling Methods:
• Non-random sampling techniques may introduce bias and limit generalizability.
• Convenience sampling may not accurately represent the target population.

To overcome these limitations, researchers can consider using alternative research designs such as experimental or quasi-experimental designs. These designs allow for better control of variables and provide more robust evidence for generalizability.

Ethical Considerations in Descriptive Research

In descriptive research, it's important to consider the ethical implications of gathering personal data from participants. When conducting studies that involve collecting personal information, researchers must ensure that they obtain informed consent from participants. This means that individuals have the right to know what data will be collected, how it will be used, and how it will be protected. Respecting participants' privacy and confidentiality is crucial, as it helps build trust and fosters a positive research environment.

Additionally, researchers should take steps to minimize any potential harm or discomfort that participants may experience. This could involve anonymizing data to protect individuals' identities or providing support services for participants who may be affected by the research. It's important to remember that participants aren't just data points but human beings with rights and feelings.

Moreover, researchers must consider the potential consequences of their work on society as a whole. If the research findings have the potential to be misused or cause harm, it's the responsibility of the researcher to address these concerns and take appropriate action to mitigate any negative impacts.

Recommendations for Descriptive Research

You should carefully consider these recommendations for conducting descriptive research in order to gather accurate and meaningful data.

• Begin by clearly defining the research objectives and questions you want to answer. This will help guide your research process and ensure that you stay focused on the key areas of investigation.
• Take the time to select an appropriate sample size and sampling method. This will help ensure that your findings are representative of the larger population you're studying.
• Consider using random sampling techniques to increase the generalizability of your results.
• Alternatively, purposive or convenience sampling can be used when specific criteria need to be met or when access to participants is limited.
• Use multiple data collection methods to gather a comprehensive set of data. This can include surveys, interviews, observations, or document analysis. By using a variety of methods, you can triangulate your findings and increase the validity and reliability of your results.
• Finally, carefully analyze and interpret your data. Look for patterns, themes, and trends that emerge from your findings. This will help you draw meaningful conclusions and make informed recommendations based on your research.

Frequently Asked Questions

How does descriptive research differ from other types of research methods.

Descriptive research differs from other research methods by focusing on describing and observing a phenomenon, rather than explaining or predicting it. It provides a detailed snapshot of a situation, allowing for a better understanding of the subject matter.

What Are Some Common Examples of Descriptive Research Studies?

Common examples of descriptive research studies include surveys, observations, and case studies. Surveys allow you to gather data from a large sample, while observations provide in-depth insights. Case studies offer detailed analysis of specific individuals or groups.

How Can the Findings From Descriptive Research Be Applied in Real-World Situations?

When applying findings from descriptive research in real-world situations, you can gain valuable insights into patterns and behaviors. This information can help you make informed decisions, develop strategies, and address specific needs or issues.

Are There Any Potential Biases or Limitations in Descriptive Research?

Yes, there are potential biases and limitations in descriptive research. However, these can be mitigated by using diverse samples, rigorous data collection methods, and transparent reporting to ensure validity and reliability.

What Ethical Considerations Should Be Taken Into Account When Conducting Descriptive Research?

When conducting descriptive research, you should consider ethical considerations. These include obtaining informed consent, protecting participant confidentiality, and ensuring that the research does not cause harm or discomfort to participants.

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• What is descriptive research?

Last updated

5 February 2023

Reviewed by

Cathy Heath

Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia.

Read on to understand the characteristics of descriptive research and explore its underlying techniques, processes, and procedures.

Analyze your descriptive research

Dovetail streamlines analysis to help you uncover and share actionable insights

Descriptive research is an exploratory research method. It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.

As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses . This can be reported using surveys , observational studies, and case studies. You can use both quantitative and qualitative methods to compile the data.

Besides making observations and then comparing and analyzing them, descriptive studies often develop knowledge concepts and provide solutions to critical issues. It always aims to answer how the event occurred, when it occurred, where it occurred, and what the problem or phenomenon is.

• Characteristics of descriptive research

The following are some of the characteristics of descriptive research:

Quantitativeness

Descriptive research can be quantitative as it gathers quantifiable data to statistically analyze a population sample. These numbers can show patterns, connections, and trends over time and can be discovered using surveys, polls, and experiments.

Qualitativeness

Descriptive research can also be qualitative. It gives meaning and context to the numbers supplied by quantitative descriptive research .

Researchers can use tools like interviews, focus groups, and ethnographic studies to illustrate why things are what they are and help characterize the research problem. This is because it’s more explanatory than exploratory or experimental research.

Uncontrolled variables

Descriptive research differs from experimental research in that researchers cannot manipulate the variables. They are recognized, scrutinized, and quantified instead. This is one of its most prominent features.

Cross-sectional studies

Descriptive research is a cross-sectional study because it examines several areas of the same group. It involves obtaining data on multiple variables at the personal level during a certain period. It’s helpful when trying to understand a larger community’s habits or preferences.

Carried out in a natural environment

Descriptive studies are usually carried out in the participants’ everyday environment, which allows researchers to avoid influencing responders by collecting data in a natural setting. You can use online surveys or survey questions to collect data or observe.

Basis for further research

You can further dissect descriptive research’s outcomes and use them for different types of investigation. The outcomes also serve as a foundation for subsequent investigations and can guide future studies. For example, you can use the data obtained in descriptive research to help determine future research designs.

• Descriptive research methods

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey.

You can use surveys to gather data in descriptive research. This involves gathering information from many people using a questionnaire and interview .

Surveys remain the dominant research tool for descriptive research design. Researchers can conduct various investigations and collect multiple types of data (quantitative and qualitative) using surveys with diverse designs.

You can conduct surveys over the phone, online, or in person. Your survey might be a brief interview or conversation with a set of prepared questions intended to obtain quick information from the primary source.

Observation

This descriptive research method involves observing and gathering data on a population or phenomena without manipulating variables. It is employed in psychology, market research , and other social science studies to track and understand human behavior.

Observation is an essential component of descriptive research. It entails gathering data and analyzing it to see whether there is a relationship between the two variables in the study. This strategy usually allows for both qualitative and quantitative data analysis.

Case studies

A case study can outline a specific topic’s traits. The topic might be a person, group, event, or organization.

It involves using a subset of a larger group as a sample to characterize the features of that larger group.

You can generalize knowledge gained from studying a case study to benefit a broader audience.

This approach entails carefully examining a particular group, person, or event over time. You can learn something new about the study topic by using a small group to better understand the dynamics of the entire group.

• Types of descriptive research

There are several types of descriptive study. The most well-known include cross-sectional studies, census surveys, sample surveys, case reports, and comparison studies.

Case reports and case series

In the healthcare and medical fields, a case report is used to explain a patient’s circumstances when suffering from an uncommon illness or displaying certain symptoms. Case reports and case series are both collections of related cases. They have aided the advancement of medical knowledge on countless occasions.

The normative component is an addition to the descriptive survey. In the descriptive–normative survey, you compare the study’s results to the norm.

Descriptive survey

This descriptive type of research employs surveys to collect information on various topics. This data aims to determine the degree to which certain conditions may be attained.

You can extrapolate or generalize the information you obtain from sample surveys to the larger group being researched.

Correlative survey

Correlative surveys help establish if there is a positive, negative, or neutral connection between two variables.

Performing census surveys involves gathering relevant data on several aspects of a given population. These units include individuals, families, organizations, objects, characteristics, and properties.

During descriptive research, you gather different degrees of interest over time from a specific population. Cross-sectional studies provide a glimpse of a phenomenon’s prevalence and features in a population. There are no ethical challenges with them and they are quite simple and inexpensive to carry out.

Comparative studies

These surveys compare the two subjects’ conditions or characteristics. The subjects may include research variables, organizations, plans, and people.

Comparison points, assumption of similarities, and criteria of comparison are three important variables that affect how well and accurately comparative studies are conducted.

For instance, descriptive research can help determine how many CEOs hold a bachelor’s degree and what proportion of low-income households receive government help.

• Pros and cons

The primary advantage of descriptive research designs is that researchers can create a reliable and beneficial database for additional study. To conduct any inquiry, you need access to reliable information sources that can give you a firm understanding of a situation.

Quantitative studies are time- and resource-intensive, so knowing the hypotheses viable for testing is crucial. The basic overview of descriptive research provides helpful hints as to which variables are worth quantitatively examining. This is why it’s employed as a precursor to quantitative research designs.

Some experts view this research as untrustworthy and unscientific. However, there is no way to assess the findings because you don’t manipulate any variables statistically.

Cause-and-effect correlations also can’t be established through descriptive investigations. Additionally, observational study findings cannot be replicated, which prevents a review of the findings and their replication.

The absence of statistical and in-depth analysis and the rather superficial character of the investigative procedure are drawbacks of this research approach.

• Descriptive research examples and applications

Several descriptive research examples are emphasized based on their types, purposes, and applications. Research questions often begin with “What is …” These studies help find solutions to practical issues in social science, physical science, and education.

Here are some examples and applications of descriptive research:

Determining consumer perception and behavior

Organizations use descriptive research designs to determine how various demographic groups react to a certain product or service.

For example, a business looking to sell to its target market should research the market’s behavior first. When researching human behavior in response to a cause or event, the researcher pays attention to the traits, actions, and responses before drawing a conclusion.

Scientific classification

Scientific descriptive research enables the classification of organisms and their traits and constituents.

Measuring data trends

A descriptive study design’s statistical capabilities allow researchers to track data trends over time. It’s frequently used to determine the study target’s current circumstances and underlying patterns.

Conduct comparison

Organizations can use a descriptive research approach to learn how various demographics react to a certain product or service. For example, you can study how the target market responds to a competitor’s product and use that information to infer their behavior.

• Bottom line

A descriptive research design is suitable for exploring certain topics and serving as a prelude to larger quantitative investigations. It provides a comprehensive understanding of the “what” of the group or thing you’re investigating.

This research type acts as the cornerstone of other research methodologies . It is distinctive because it can use quantitative and qualitative research approaches at the same time.

What is descriptive research design?

Descriptive research design aims to systematically obtain information to describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem rather than the why.

How does descriptive research compare to qualitative research?

Despite certain parallels, descriptive research concentrates on describing phenomena, while qualitative research aims to understand people better.

How do you analyze descriptive research data?

Data analysis involves using various methodologies, enabling the researcher to evaluate and provide results regarding validity and reliability.

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Psychological Research

Descriptive Research

Learning objectives.

• Differentiate between descriptive, experimental, and correlational research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

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• Modification and adaptation. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike

CC licensed content, Shared previously

• Approaches to Research. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/2-2-approaches-to-research . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction.
• Descriptive Research. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ . License : CC BY-SA: Attribution-ShareAlike

research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured

tests whether a relationship exists between two or more variables

tests a hypothesis to determine cause and effect relationships

observational research study focusing on one or a few people

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

the collection of individuals on which we collect data.

a larger collection of individuals that we would like to generalize our results to.

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

Advantages and disadvantages of descriptive research

Descriptive research

Descriptive research  is a type of research that is responsible for describing the population situation or phenomenon around which his study focuses. It seeks to provide information about the what, how, when, and where of the research problem, without giving priority to answering the “why” of the problem. As its name says, this way of investigating “describes”, it does not explain. Advantages and disadvantages of descriptive research

In addition, it obtains information on the phenomenon or situation to be studied, using techniques such as observation and survey, among others. For example, research studying the morphology and mechanism of action of SARS-CoV-2 is descriptive. Answer the “what”, not the “why”.

This type of research is very useful when conducting studies, for example, when you want to know which brand of soda is most consumed in a supermarket, where you only want to know which is the most consumed, and not why it is the most consumed. consumed.

Descriptive investigations, unlike other types of investigations, carry out their study without altering or manipulating any of the variables of the phenomenon, limiting themselves only to their measurement and description. Additionally, it is possible to make future forecasts, although they are considered premature or basic.

Descriptive research characteristics

Here are some of the most important characteristics of descriptive research :

Has no control over variables

In descriptive research, the researcher has no control over any of the variables that affect the event or problem under investigation. Advantages and disadvantages of descriptive research

Existence of variables

To carry out a descriptive research , it is necessary to know in advance the variables that will be analyzed, since this type of research is not dedicated to the search for variables, but to their study.

Although, when obtaining data on the variables , it is possible to make forecasts, these are not entirely reliable, since they are considered premature.

Quantitative information

In most cases, descriptive research gets data on quantities, not qualities . It is for this reason that it can be said that a descriptive research is quantitative. Advantages and disadvantages of descriptive research

Even so, there is also the possibility of obtaining qualitative data.

As in all types of research , the data provided by descriptive research must be both accurate and reliable.

Information classification

Descriptive research can be used to classify the data collected in the study that is being carried out, separating them into different categories of description.

Usually, the cross-sectional or transectional design is the most used to carry out this type of research , although it is also possible to use the pre-experimental design. Advantages and disadvantages of descriptive research

Descriptive research design

The research design is used to draw up the work plan to follow in the research. It is where the conceptual phase of the research, such as the statement of the problem , meets the operational phase, such as the method and instruments of the investigation.

For the case of the design of a descriptive investigation, most of the time it is necessary to obtain data that refers to the quantity. To achieve this task, the researcher can choose between two different types of research designs, which have specific characteristics that differentiate them from each other.

The two types of designs used in descriptive research are described below:

Cross-sectional or   transectional design

In cross-sectional designs, the variables are not affected by any type of process, which is why they only dedicate themselves to observing the event as it happens, limiting themselves only to analyzing them. Advantages and disadvantages of descriptive research

They basically consist of making a description of the variables to be measured in a phenomenon, and analyzing the incidence at the time that event occurs.

Pre-experimental design

There are occasions where the pre- experimental design is used as a test to get a first contact with the research problem in a real way, being used, on some occasions, as a test of experiments with a greater degree of control.

This type of design does not allow to establish causal relationships, since they do not have the possibility of controlling variables , and their internal validity is not very reliable. Furthermore, it is applied only to a group, over which it has no control whatsoever.

There are two ways to carry out a pre- experimental design, which are as follows:

• Case study with a single measurement  : in this type of design, a stimulus is applied to a group and then the data obtained from the variable or variables to be measured are taken. The simplicity of the design makes it unreliable, since there is no reference to the level of the variable (s) before the stimulus is applied, as well as no control over them.
• Test and post-test design with a single group  : for this type of design, a test is carried out before and after applying the stimulus to the group, thus allowing the visualization of the differences that may exist between the measurements of the studied variable (s) . Although, using this design it is possible to differentiate the levels of the variables , before and after the stimulus is applied, it does not allow to visualize causality, since there is no comparison group, nor is there the possibility of manipulating the variables. Advantages and disadvantages of descriptive research

Techniques used in descriptive research

In the case of descriptive research , there are three techniques to carry it out:

Observation

Observation is one of the most used information, of the quantitative or qualitative type:

• To obtain quantitative information , statistical and numerical study methodologies are used, where information about values ​​such as weight, scale and years, among others, is obtained. So it can be said that fundamentally numerical values ​​are obtained.
• On the other hand, to obtain qualitative information, the type of data obtained does not have to do with numbers or statistics , but with the dynamics that occur in the group on which the research is being developed. Advantages and disadvantages of descriptive research

Using the case study it is possible to carry out a slightly more detailed analysis of the event, as well as to study in detail groups or subjects separately.

In addition, it is possible to present a hypothesis and to expand the degree of knowledge about the event under investigation. However, due to its low precision in forecasting, it is not possible to specify the causes and effects of the phenomenon studied.

Research survey

The research survey is one of the most widely used instruments when conducting descriptive research, where the number of samples to be taken is large. Advantages and disadvantages of descriptive research

The selection of questions should include both open and closed questions, thus guaranteeing a balance between them and making it possible to collect good quality information.

Like all different types of research , descriptive research has both advantages and disadvantages. Some of the most important are listed below.

• The brevity by which descriptive investigations are carried out means that their costs are not high, compared to other types of investigations.
• It enables both the collection of quantitative data and qualitative data.
• They allow to formulate hypotheses, as well as provide a large amount of valuable data for the development of future investigations. Advantages and disadvantages of descriptive research
• By using descriptive research , the data is collected in the place where it occurs, without any type of alteration, ensuring the quality and integrity of the same.

Disadvantages

• If the questions are not well formulated, the answers obtained may not be entirely reliable, which makes it difficult to carry out a credible investigation.
• The types of variables that allow the study of descriptive investigations make it impossible to visualize the causes and effects of the event.
• The data obtained by conducting a descriptive research , being collected randomly, make it impossible to obtain valid data that represent the entire population.

Descriptive Research Examples

Some examples of descriptive investigations may be the following:

Penguin census

Studying the penguin population that exists in the South Georgia Islands is a descriptive investigation that answers the what and where. Advantages and disadvantages of descriptive research

National census

The research carried out in a national census is descriptive, since it is only interested in data such as the number of population, the salary they receive, or what class the household is, without making any kind of analogy between these. .

Carrying out a descriptive investigation that collects data about the political party that people will choose in the next elections, it is possible to predict, with a margin of error , the result that will be obtained in them.

Supermarket

Using observation, qualitative data can be collected on the habits of supermarket customers regarding the purchases they make in it. Advantages and disadvantages of descriptive research

Kids playtime

Through the resource of the survey , it is possible to carry out a descriptive investigation that yields information about the number of hours per day that children in a particular population play. Being able to make a forecast of the weather that a particular child of that city plays.

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Descriptive Research: 3 Advantages and Disadvantages

When it comes to research, the more descriptive the better. Descriptive research is a technique that can be used to get more information about what you want to know about without actually having to conduct an experiment or a survey.

Find out the positive and negative aspects of this process and how descriptive research can help solve some of your concerns in this article!

What is Descriptive Research?

The advantages of descriptive research, the disadvantages of descriptive research.

Descriptive research is a type of research that focuses on describing the characteristics of a population or sample. This type of research can be used to study a variety of topics, including marketing, health, and social sciences.

One advantage of descriptive research is that it can be relatively quick and easy to carry out. This type of research doesn’t require any special equipment or access to complex databases. In addition, descriptive research can help to understand the data more clearly.

However, descriptive research has some limitations. One problem is that it can be difficult to generalize your findings from the data. Another limitation is that it doesn’t allow you to test hypotheses.

It can take a lot of time to collect the data for descriptive research. If you don’t have enough patience, you might find that your limited resources will be used up before you can complete your study. However, this type of research is an excellent way to learn about your customers and how they use the product or service that they are trying to sell.

Descriptive research has many advantages over other research methods. These advantages include the ability to collect data from a large number of participants, the ability to explore different aspects of a topic, and the ability to understand complex topics.

• One of the main advantages of descriptive research is that it is able to collect data from a large number of participants. This can be helpful in understanding complex topics. This makes it an important tool for researchers who want to understand complicated issues.
• Another advantage of descriptive research is that it can explore different aspects of a topic. This can be helpful in understanding how people use products or how people feel about a particular situation. For example, descriptive research could explore how people use a product in different ways or how people feel about a situation after hearing about it.
• Finally, descriptive research is that it is usually cheaper, easier and quicker to carry out than other methods of research. It also increases the reliability of results, as participants are less likely to lie or select inappropriate answers.

The strengths of descriptive research are that it can ask a large number of participants about a topic. The researcher only needs to contact people who might be interested in the topic and ask them questions about it. This means especially that qualitative researchers can gather information from a large number of participants, if they have an interest in the topic.

Though, descriptive research has quite great advantages, however, it is often criticized for its disadvantages.

Here, are some of the disadvantages of Descriptive research:

• One of the main disadvantages is that it can be difficult to design a study that is relevant to the researcher’s specific interests. This can make it difficult to get accurate information from participants.
• Another disadvantage of descriptive research is that it can be difficult to generalize the findings from the study. The results of the study may not apply to all people or situations. Additionally, descriptive research does not allow for any conclusions to be drawn about cause and effect.
• The main weakness of descriptive research is that there is no control group. It cannot tell us whether one treatment works better than another. Instead, it must rely on the subjective opinions of the participant or participants in the study, which can make it more difficult for researchers to draw conclusions about why certain treatments work better than others.

In Descriptive research, studies that focus on people’s perceptions about a particular topic may not provide useful information on how the general population feels about it. This can be especially important for policy makers who must decide which views should be taken into consideration when making decisions about certain subjects or issues.

Descriptive research is a type of research that is used to describe and understand phenomena. It is often used to gather information about a particular topic, such as how people use products or how people feel about a certain issue.

There are several advantages to conducting descriptive research. First, it is relatively inexpensive to do. Second, it can help you gather a lot of data quickly. Third, it can provide you with a detailed understanding of the phenomena being studied.

However, there are also some disadvantages to conducting descriptive research. First, it can be difficult to generalize the findings from the study. Second, it may not provide you with accurate information about the target population.

Overall, descriptive research is useful for understanding the characteristics of a population or sample. It has limitations, but these limitations are mostly minor.

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Psychological Research

Descriptive research, learning objectives.

• Differentiate between descriptive, experimental, and correlational research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not. Table 1 displays a quick overview of the characteristics of each research design.

Table 1. Characteristics of Descriptive, Experimental, and Correlational Research

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

Figure 1 . Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

Figure 2 . (a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

Figure 3 . Surveys can be administered in a number of ways, including electronically administered research, like the survey shown here. (credit: Robert Nyman)

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

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Pros & cons of descriptive research.

In academic and professional environments, researchers often take on research projects before making definitive statements or taking action involving the subject matter. Descriptive research is a form of research that relies on a variety of different techniques to organize information. It is different from exploratory research in that it is typically more focused on a certain unknown or unproven aspect of the project.

Descriptive research does not rely on only one type of data; it relies on both quantitative and qualitative research . The research can utilize methods such as surveys, observation, field experiments, interviews and number analysis. Because it is so varied in method and usage, researchers favor its use in many different fields including marketing, medical health, science and psychology.

Perhaps the strongest argument for descriptive research is the fact that it offers well-rounded support for a thesis. Because it relies on such a range of different methods, it is considered a holistic approach to a subject. Qualitative research, such as surveys and interviews, make information relatable to the reader. For example, raw numbers may show that people are more prone to a specific behavior, but an interview will explain why this behavior is occurring.

Another argument in favor of descriptive research is that once information is assembled, there are many ways to apply it. For example, if you are a representative for an ad agency, you may conduct research to find out consumer opinion on a certain product. This research could include the information on the raw numbers of people who buy the product, the demographics of the end user and the focus groups that ask for consumer feedback on the product. Once you have this descriptive research, you can use it to focus your next ad campaign to the right clientele.

Potentially Subjective

On the negative side, descriptive research can sometimes be skewed to fit the needs of the researcher. For example, if you are assembling a questionnaire, you might load the questions to direct the reader to answer a certain way. If you are doing a comparison between two products, you might give one product an unfair advantage to get the desired results. While qualitative research may be hard to argue against because it involves numbers and tests, questions of the researcher’s motive and method often arise.

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Liza Hollis has been writing for print and online publications since 2003. Her work has appeared on various digital properties, including USAToday.com. Hollis earned a degree in English Literature from the University of Florida.

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Descriptive Research

What is descriptive research and how it’s done effortlessly.

Descriptive research is a systematic search for a specific topic to come up with new and useful information. It is mainly classified into two types: basic or fundamental research and applied research.

Applied research is one type that is used to answer a specific question with direct application.

On the other hand, basic research, also known as fundamental or pure research, is a type of research that mainly driven by curiosity or the purpose of increasing your knowledge about a specific topic.

Applied research is known to be theoretical, It is utilized to develop and solve problems of a certain topic while basic research is practical in nature and is meant to add a better understanding to a piece of existing knowledge.

In this article, we’ll primarily focus on one type of basic research: descriptive research .

What is descriptive research?

Descriptive research is a type of research method under basic research that aims to accurately describe a certain topic being studied – may it be a population, situation, or phenomenon.

This focuses more on the question “what” rather than “why”. In fact, we apply this in our everyday life – from answering school works to finding the new fashion trends.

“What will be the purpose of the research?”

“What will it be used for?”

“What is the specific topics and your goals for this research?”

For example, you are planning to set up a food business. You may think about doing a demographic survey about the place where you want to put up your business. Some of the possible questions you want to ask are the following:

• What is the age group with the most number of population?
• What are the foods that they like the most?
• When are the peak hours?
• How am I going to reach out to customers?
• Where will I put up my business?

Characteristics of Descriptive Research

Foundation of the research

In order for you to go further with the “why”, you have to start answering the questions what, when, where, and how. If your case study is about the best ice cream in town, you begin with simple questions like, “What flavors are the best seller?”, “When does the store open?”, and “How do they come up with the pricing?” before you jump into, “Why do people like the chocolate flavored ones?”, “How cheap are the ingredients of each flavor?”, and “Why do most people visit the ice cream store at night?”.

Quantitative

Descriptive research usually involves numbers, as this type of research attempts to collect quantifiable information to come up with a statistical analysis . This is why a demographic survey is often applied especially on market research.

Cross-sectional

Descriptive research is known to be a cross-sectional study that is observational in nature. In medical research, for example, when a case of dengue fever in an area is being studied, they study other sections such as the lifestyle of each patient instead of focusing on the area or the place of the study.

Descriptive Research Methods

The three main types of descriptive research methods: observational, survey, and case study.

Observational Method

This type of descriptive research is usually being done to observe plants, animals, or humans. It may be done through naturalistic or laboratory observation. Naturalistic observation has its greatest advantage, especially when checking for ecological validity. While laboratory observation is less time-consuming compared to naturalistic observation, this type of descriptive research method requires you to do a replica of the natural environment of your subject.

Observational method sometimes needs two or more specimen to be able to come up with comparisons and a more accurate answer. For example, for the basic study about how a plant grows, you have to observe at least three plants: one with proper sunlight and water, one with proper sunlight but without water, and another one without proper sunlight but with water.

Case Study Method

A case study is another type of descriptive research that involves a deeper investigation about an individual, a group, or a specific event. It is done with testable hypotheses but is limited and does not determine the cause and effect. Case studies help create new ideas to be tested by other research methods. Basically, data ate gathered by other sources and research methods.

Survey Method

In the survey method, a number of participants are being asked to answer questions through interviews or a set of questionnaires.

Questionnaires are generally done by answering using paper and pencil. However, with the advancement of technology, questionnaires can now be created using excel, word document, and other platforms such as Google and Facebook that are also used for descriptive writing .

Interview surveys, on the other hand, is done by asking respondents face-to-face rather than filling out questionnaires. It sometimes requires other instruments such as a video camera or a recorder to better review and analyze the findings.

Here are some tips to build effective surveys:

• Make sure that each question is valid and necessary.
• Keep each question short and understandable.
• Avoid using jargons especially for questionnaires.
• Ask questions one by one.
• Provide a set of choices as much as possible

Pros and Cons of Descriptive Research

• The collection of data is quick and easy to conduct, especially when doing surveys.
• It allows you to collect data in a natural environment that gives high-quality and honest data collection.
• It is easier to make decisions and go further for data analysis
• A better understanding of the research topic due to qualitative and quantitative data collection.
• When it comes to confidentiality, some respondents won’t give answers to questionnaires and interviews.
• It doesn’t answer the question “why”, which makes it limited and won’t come up with the cause and effect of a research topic.
• It is tough to validate an accurate and complete representation of a study.

The purpose of descriptive research, aside from describing a certain population or situation, is to know the basic knowledge of a study. Descriptive research is always the beginning of every answer to solving a problem and development of a certain idea so it is always a must before jumping into conclusions.

Image by  Dariusz Sankowski

17 Advantages and Disadvantages of Experimental Research Method in Psychology

There are numerous research methods used to determine if theories, ideas, or even products have validity in a market or community. One of the most common options utilized today is experimental research. Its popularity is due to the fact that it becomes possible to take complete control over a single variable while conducting the research efforts. This process makes it possible to manipulate the other variables involved to determine the validity of an idea or the value of what is being proposed.

Outcomes through experimental research come through a process of administration and monitoring. This structure makes it possible for researchers to determine the genuine impact of what is under observation. It is a process which creates outcomes with a high degree of accuracy in almost any field.

The conclusion can then offer a final value potential to consider, making it possible to know if a continued pursuit of the information is profitable in some way.

The pros and cons of experimental research show that this process is highly efficient, creating data points for evaluation with speed and regularity. It is also an option that can be manipulated easily when researchers want their work to draw specific conclusions.

List of the Pros of Experimental Research

1. Experimental research offers the highest levels of control. The procedures involved with experimental research make it possible to isolate specific variables within virtually any topic. This advantage makes it possible to determine if outcomes are viable. Variables are controllable on their own or in combination with others to determine what can happen when each scenario is brought to a conclusion. It is a benefit which applies to ideas, theories, and products, offering a significant advantage when accurate results or metrics are necessary for progress.

2. Experimental research is useful in every industry and subject. Since experimental research offers higher levels of control than other methods which are available, it offers results which provide higher levels of relevance and specificity. The outcomes that are possible come with superior consistency as well. It is useful in a variety of situations which can help everyone involved to see the value of their work before they must implement a series of events.

3. Experimental research replicates natural settings with significant speed benefits. This form of research makes it possible to replicate specific environmental settings within the controls of a laboratory setting. This structure makes it possible for the experiments to replicate variables that would require a significant time investment otherwise. It is a process which gives the researchers involved an opportunity to seize significant control over the extraneous variables which may occur, creating limits on the unpredictability of elements that are unknown or unexpected when driving toward results.

4. Experimental research offers results which can occur repetitively. The reason that experimental research is such an effective tool is that it produces a specific set of results from documented steps that anyone can follow. Researchers can duplicate the variables used during the work, then control the variables in the same way to create an exact outcome that duplicates the first one. This process makes it possible to validate scientific discoveries, understand the effectiveness of a program, or provide evidence that products address consumer pain points in beneficial ways.

5. Experimental research offers conclusions which are specific. Thanks to the high levels of control which are available through experimental research, the results which occur through this process are usually relevant and specific. Researchers an determine failure, success, or some other specific outcome because of the data points which become available from their work. That is why it is easier to take an idea of any type to the next level with the information that becomes available through this process. There is always a need to bring an outcome to its natural conclusion during variable manipulation to collect the desired data.

6. Experimental research works with other methods too. You can use experimental research with other methods to ensure that the data received from this process is as accurate as possible. The results that researchers obtain must be able to stand on their own for verification to have findings which are valid. This combination of factors makes it possible to become ultra-specific with the information being received through these studies while offering new ideas to other research formats simultaneously.

7. Experimental research allows for the determination of cause-and-effect. Because researchers can manipulate variables when performing experimental research, it becomes possible to look for the different cause-and-effect relationships which may exist when pursuing a new thought. This process allows the parties involved to dig deeply into the possibilities which are present, demonstrating whatever specific benefits are possible when outcomes are reached. It is a structure which seeks to understand the specific details of each situation as a way to create results.

List of the Cons of Experimental Research

1. Experimental research suffers from the potential of human errors. Experimental research requires those involved to maintain specific levels of variable control to create meaningful results. This process comes with a high risk of experiencing an error at some stage of the process when compared to other options that may be available. When this issue goes unnoticed as the results become transferable, the data it creates will reflect a misunderstanding of the issue under observation. It is a disadvantage which could eliminate the value of any information that develops from this process.

2. Experimental research is a time-consuming process to endure. Experimental research must isolate each possible variable when a subject matter is being studied. Then it must conduct testing on each element under consideration until a resolution becomes possible, which then requires data collection to occur. This process must continue to repeat itself for any findings to be valid from the effort. Then combinations of variables must go through evaluation in the same manner. It is a field of research that sometimes costs more than the potential benefits or profits that are achievable when a favorable outcome is eventually reached.

3. Experimental research creates unrealistic situations that still receive validity. The controls which are necessary when performing experimental research increase the risks of the data becoming inaccurate or corrupted over time. It will still seem authentic to the researchers involved because they may not see that a variable is an unrealistic situation. The variables can skew in a specific direction if the information shifts in a certain direction through the efforts of the researchers involved. The research environment can also be extremely different than real-life circumstances, which can invalidate the value of the findings.

4. Experimental research struggles to measure human responses. People experience stress in uncountable ways during the average day. Personal drama, political arguments, and workplace deadlines can influence the data that researchers collect when measuring human response tendencies. What happens inside of a controlled situation is not always what happens in real-life scenarios. That is why this method is not the correct choice to use in group or individual settings where a human response requires measurement.

5. Experimental research does not always create an objective view. Objective research is necessary for it to provide effective results. When researchers have permission to manipulate variables in whatever way they choose, then the process increases the risk of a personal bias, unconscious or otherwise, influencing the results which are eventually obtained. People can shift their focus because they become uncomfortable, are aroused by the event, or want to manipulate the results for their personal agenda. Data samples are therefore only a reflection of that one group instead of offering data across an entire demographic.

6. Experimental research can experience influences from real-time events. The issue with human error in experimental research often involves the researchers conducting the work, but it can also impact the people being studied as well. Numerous outside variables can impact responses or outcomes without the knowledge of researchers. External triggers, such as the environment, political stress, or physical attraction can alter a person’s regular perspective without it being apparent. Internal triggers, such as claustrophobia or social interactions, can alter responses as well. It is challenging to know if the data collected through this process offers an element of honesty.

7. Experimental research cannot always control all of the variables. Although experimental research attempts to control every variable or combination that is possible, laboratory settings cannot reach this limitation in every circumstance. If data must be collected in a natural setting, then the risk of inaccurate information rises. Some research efforts place an emphasis on one set of variables over another because of a perceived level of importance. That is why it becomes virtually impossible in some situations to apply obtained results to the overall population. Groups are not always comparable, even if this process provides for more significant transferability than other methods of research.

8. Experimental research does not always seek to find explanations. The goal of experimental research is to answer questions that people may have when evaluating specific data points. There is no concern given to the reason why specific outcomes are achievable through this system. When you are working in a world of black-and-white where something works or it does not, there are many shades of gray in-between these two colors where additional information is waiting to be discovered. This method ignores that information, settling for whatever answers are found along the extremes instead.

9. Experimental research does not make exceptions for ethical or moral violations. One of the most significant disadvantages of experimental research is that it does not take the ethical or moral violations that some variables may create out of the situation. Some variables cannot be manipulated in ways that are safe for people, the environment, or even the society as a whole. When researchers encounter this situation, they must either transfer their data points to another method, continue on to produce incomplete results, fabricate results, or set their personal convictions aside to work on the variable anyway.

10. Experimental research may offer results which apply to only one situation. Although one of the advantages of experimental research is that it allows for duplication by others to obtain the same results, this is not always the case in every situation. There are results that this method can find which may only apply to that specific situation. If this process is used to determine highly detailed data points which require unique circumstances to obtain, then future researchers may find that result replication is challenging to obtain.

These experimental research pros and cons offer a useful system that can help determine the validity of an idea in any industry. The only way to achieve this advantage is to place tight controls over the process, and then reduce any potential for bias within the system to appear. This makes it possible to determine if a new idea of any type offers current or future value.

12 Advantages and Disadvantages of Correlational Research Studies

A correlational research study uses the non-experimental method where the measurement of two variables occurs. It is up to the individuals conducting the study to assess and understand the statistical relationship between them without having extraneous influences occur.

It’s like when a child hears the music playing from an ice cream truck. There is a direct relationship between the sound heard and how far away the vehicle is from their current location. By understanding the commonality of the data in that situation, the child knows whether to grab their money, ask their parents for some, or not to bother making an effort.

The advantages and disadvantages of a correlational research study help us to look for variables that seem to interact with each other. If you see one of those variables changing, then you have an idea of how the other is going to change.

List of the Advantages of a Correlational Research Study

1. Neither variable goes through a manipulative process. When you choose a correlational research study to review variables, then neither one goes through a manipulative process. It is the distinctive feature of this method. Researchers could observe participants in a public setting or a closed environment because it doesn’t matter where or how the variables get measured.

2. Two different data collection methods are available with correlational research. The data gathered from a correlational research study can come from either naturalistic observation or archival data. The first option is a type of field research where those responsible for the work might observe situations in real-life scenarios as unobtrusively as possible. When people know that they are under observation, then there is a significant risk that their behaviors will change. If the participants remain anonymous with the work conducted in a public setting, then it is an ethical approach.

The second option relies on the use of collected data from previous research efforts. The information is straightforward, giving researchers access to specific points that can lead to a greater understanding of the potential variables involved in each situation.

3. The results from correlational research are more applicable. Because a correlational research study occurs in real-life situations, the data that gets gathered from this work is typically more applicable to everyday encounters. You don’t need to attempt to extrapolate the findings from a laboratory setting into something that works into the routine of the average person.

Even if the researchers don’t know the individuals or situations being studied with correlational research, their findings are still applicable to the scenarios under review.

4. It offers a beneficial starting position for research. When a correlational research study begins to look at specific relationships or phenomena to see if connections are present, then the variables provide an excellent starting position to begin the review. Each variable creates a unique data set that can work in several different ways with known and unknown relationships.

It is not unusual for researchers to create new opportunities for future studies because of the amount of data that becomes available. These studies provide a lot more information to review than a simple experiment would offer in most situations.

5. Researchers can determine the direction and strength of each relationship. The variables that get studied with correlational research help us to find the direction and strength of each relationship. This advantage makes it possible to narrow the findings in future studies as needed to determine causation experimentally as needed. It can be an experiential process that involves direct observation or occur through data insights with an additional review.

This advantage creates the possibility of discovering new relationships existing between phenomena that don’t seem to have existing connections. That process helps us to discover more about the world and specific situations than if other research methods were used.

6. A survey method is helpful in correlational research. Some correlational research study methods can benefit from the use of surveys to collect information on a specific topic. Since the variables being studied still aren’t under the control of the researchers, then it can reveal the presence of a relationship between them. That makes it fast, easy, and affordable to start looking for potential outcomes and results when studying specific contact points.

7. The results of a correlational research study are easy to classify. A correlational research study uses what is called the “correlation coefficient” to measure the strength of the relationship between the variables. It can range from 1.00 to -1.00. These figures create three potential definition outcomes for the work being performed.

• A positive correlation shows that both variables increase or decrease simultaneously. A coefficient that approaches 1.00 indicates the strongest correlation for this result.
• A negative correlation indicates that when one variable increases, the other will decrease. When the coefficient approaches -1.00, then this is the expected result.
• If the coefficient is zero, then this result indicates that there is no correlation between the two variables.

List of the Disadvantages of a Correlational Research Study

1. Correlational research only uncovers relationships. The benefit of a correlational research study is that it can uncover relationships that may have not been previously known. What it does not provide is a conclusive reason for why that connection exists in the first place. All we can do with the information is study the connections between phenomena to see how each one influences the other. Knowing that one change can create additional alternations can be beneficial when looking for unique outcomes, but it fails to answer the question of “why” that is sometimes necessary for research.

Correlation does not equate to causation when using this study method.

2. It won’t determine what variables have the most influence. A correlational research study can help to determine the connections that variables share with a specific phenomenon. What this work cannot produce is information regarding which variable is responsible for influencing the other. You might know that households with more wealth also have higher education levels, but you can’t determine if it is the education that leads to additional wealth.

That means the correlation for a specific variable must be assumed or sent to a different research method to collect the necessary data.

3. Correlational research can be a time-consuming process. Although the benefits of a correlational research study can be tremendous, it can also be expensive and time-consuming to achieve an outcome. The only way to collect data is through direct interactions or observation of the variables in question. That means numerous scenarios must receive a thorough look before it is possible to determine an accurate coefficient. The naturalistic observation method sees this disadvantage most often, but it can apply to every effort in this category.

4. Extraneous variables might interfere with the information. There is no guarantee that additional influences will stay out of the correlational research study. It is possible for unique outcomes to exist that interfere with the work. Going back to the example of the child and the ice cream truck, the presence of heavy winds might make it seem like the vehicle is closer or further away than it actually is.

Another issue that fits into this disadvantage involves the awareness of the subjects of an observer. People act different when they know that someone is watching, so it can skew the results in either direction. This issue even impacts surveys because some people try to provide or deny data to create specific outcomes.

5. Outcomes can be adversely impacted by the quality of the work. The quality of the work performed during a correlational research study will determine the usefulness of the data gathered. If the survey questions do not provide enough of a trigger to generate information, then the time and money spent on the effort gets wasted. Even when there is some flexibility in the structure of the study, a lack of representation in the selected sample can produce inferior results that could lead researchers down an incorrect path of study.

Most correlational research studies are found in the field of psychology. It’s treated as a preliminary way to gather information about a specific topic or situation where experimentation isn’t possible for some reason. Although it typically looks at two variables to determine if a coefficient exists, it can also look at more in some relationships.

The variables themselves are not under the control of the researchers, which is why this method of study can be problematic at times. It is also the reason why it can be a popular way to look at specific data points.

Although the advantages and disadvantages of a correlational research study won’t reveal the reason why relationships exist, it can at least determine their existence. That’s why it is often considered a worthwhile investment, even when there are sometimes cheaper methods to use.