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The power of the placebo effect - Emma Bryce

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Psychological Research: Crash Course Psychology #2

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Types of Hypotheses in Psychology Explained #Alevel #Revision

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The Purpose of Psychological Research: PSYCHademia, AP Psychology

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How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave. This process not only allows scientists to investigate and understand different psychological phenomena, but it also provides researchers and others a way to share and discuss the results of their studies.

What Is the Scientific Method?

What is the scientific method and how is it used in psychology? The scientific method is essentially a step-by-step process that researchers can follow to determine if there is some type of relationship between two or more variables.

Psychologists and other social scientists regularly propose explanations for human behavior. On a more informal level, people make judgments about the intentions, motivations, and actions of others on a daily basis.

While the everyday judgments we make about human behavior are subjective and anecdotal, researchers use the scientific method to study psychology in an objective and systematic way. The results of these studies are often reported in popular media, which leads many to wonder just how or why researchers arrived at the conclusions they did.

In order to truly understand how psychologists and other researchers reach these conclusions, you need to know more about the research process that is used to study psychology and the basic steps that are utilized when conducting any type of psychological research. By knowing the steps of the scientific method, you can better understand the process researchers go through to arrive at conclusions about human behavior.

Reasons to Use the Steps of the Scientific Method

The goals of psychological studies are to describe, explain, predict and perhaps influence mental processes or behaviors. In order to do this, psychologists utilize the scientific method to conduct psychological research. The scientific method is a set of principles and procedures that are used by researchers to develop questions, collect data, and reach conclusions.

What are the goals of scientific research in psychology? Researchers seek not only to describe behaviors and explain why these behaviors occur; they also strive to create research that can be used to predict and even change human behavior.

Key Terms to Know

Before you begin exploring the scientific method steps, there are some key terms and definitions that you should be familiar with.

Hypothesis: An educated guess about the possible relationship between two or more variables.

Variable: A factor or element that can change in observable and measurable ways.

Operational Definition: A full description of exactly how variables are defined, how they will be manipulated, and how they will be measured.

The Steps of the Scientific Method

While research studies can vary, these are the basic steps that psychologists and scientists use when investigating human behavior.

Step 1. Make an Observation

Before a researcher can begin, they must choose a topic to study. Once an area of interest has been chosen, the researchers must then conduct a thorough review of the existing literature on the subject. This review will provide valuable information about what has already been learned about the topic and what questions remain to be answered.

A literature review might involve looking at a considerable amount of written material from both books and academic journals dating back decades. The relevant information collected by the researcher will be presented in the introduction section of the final published study results. This background material will also help the researcher with the first major step in conducting a psychology study - formulating a hypothesis.

Step 2. Ask a Question

Once a researcher has observed something and gained some background information on the topic, the next step is to ask a question. The researcher will form a hypothesis, which is an educated guess about the relationship between two or more variables

For example, a researcher might ask a question about the relationship between sleep and academic performance. Do students who get more sleep perform better on tests at school?

In order to formulate a good hypothesis, it is important to think about different questions you might have about a particular topic. You should also consider how you could investigate the causes. Falsifiability is an important part of any valid hypothesis. In other words, if a hypothesis was false, there needs to be a way for scientists to demonstrate that it is false.

Step 3. Test Your Hypothesis and Collect Data

Once you have a solid hypothesis, the next step of the scientific method is to put this hunch to the test by collecting data. The exact methods used to investigate a hypothesis depend on exactly what is being studied. There are two basic forms of research that a psychologist might utilize - descriptive research or experimental research.

Descriptive research is typically used when it would be difficult or even impossible to manipulate the variables in question. Examples of descriptive research include case studies, naturalistic observation, and correlation studies. Phone surveys that are often used by marketers are one example of descriptive research.

Correlational studies are quite common in psychology research. While they do not allow researchers to determine cause-and-effect, they do make it possible to spot relationships between different variables and to measure the strength of those relationships.

Experimental research is used to explore cause-and-effect relationships between two or more variables. This type of research involves systematically manipulating an independent variable and then measuring the effect that it has on a defined dependent variable. One of the major advantages of this method is that it allows researchers to actually determine if changes in one variable actually cause changes in another.

While psychology experiments are often quite complex, a simple experiment is fairly basic but does allow researchers to determine cause-and-effect relationships between variables. Most simple experiments use a control group (those who do not receive the treatment) and an experimental group (those who do receive the treatment).

Step 4. Examine the Results and Draw Conclusions

Once a researcher has designed the study and collected the data, it is time to examine this information and draw conclusions about what has been found. Using statistics, researchers can summarize the data, analyze the results, and draw conclusions based on this evidence.

So how does a researcher decide what the results of a study mean? Not only can statistical analysis support (or refute) the researcher's hypothesis; it can also be used to determine if the findings are statistically significant.

When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.

Based on these observations, researchers must then determine what the results mean. In some cases, an experiment will support a hypothesis, but in other cases, it will fail to support the hypothesis.

So what happens if the results of a psychology experiment do not support the researcher's hypothesis? Does this mean that the study was worthless? Just because the findings fail to support the hypothesis does not mean that the research is not useful or informative. In fact, such research plays an important role in helping scientists develop new questions and hypotheses to explore in the future.

After conclusions have been drawn, the next step is to share the results with the rest of the scientific community. This is an important part of the process because it contributes to the overall knowledge base and can help other scientists find new research avenues to explore.

Step 5. Report the Results

The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as Psychological Bulletin, the Journal of Social Psychology, Developmental Psychology, and many others.

The structure of a journal article follows a specified format that has been outlined by the American Psychological Association (APA). In these articles, researchers:

Provide a brief history and background on previous research

Present their hypothesis

Identify who participated in the study and how they were selected

Provide operational definitions for each variable

Describe the measures and procedures that were used to collect data

Explain how the information collected was analyzed

Discuss what the results mean

Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound.

Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic.

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Following the Steps of a Scientific Method for Research

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How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave. This process not only allows scientists to investigate and understand different psychological phenomena, but it also provides researchers and others a way to share and discuss the results of their studies.

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The Hidden Biases in WEIRD Psychology Research

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The Replication Crisis in Psychology - Philosophical Questions

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Is there a reproducibility crisis in science? - Matt Anticole

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This usually involves proposing a possible relationship between two variables: the independent variable (what the researcher changes) and the dependent variable (what the research measures).

In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

A fundamental requirement of a hypothesis is that is can be tested against reality, and can then be supported or rejected.

To test a hypothesis the researcher first assumes that there is no difference between populations from which they are taken. This is known as the null hypothesis. The research hypothesis is often called the alternative hypothesis.

Types of research hypotheses

Falsifiability

The Falsification Principle, proposed by Karl Popper, is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific it must be able to be tested and conceivably proven false.

However many confirming instances there are for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that "all swans are white," can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory, rather than attempt to continually support theoretical hypotheses.

Can a hypothesis be proven?

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct.

We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to write a hypothesis

1. To write the alternative and null hypotheses for an investigation, you need to identify the key variables in the study.The independent variable is manipulated by the researcher and the dependent variable is the outcome which is measured.

2. Operationalized the variables being investigated.Operationalisation of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression you might count the number of punches given by participants

3. Decide on a direction for your prediction. If there is evidence in the literature to support a specific effect on the independent variable on the dependent variable, write a directional (one-tailed) hypothesis.If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.

4. Write your hypothesis. A good hypothesis is short (i.e. concise) and comprises clear and simple language.

What are examples of a hypothesis?

Let's consider a hypothesis that many teachers might subscribe to: that students work better on Monday morning than they do on a Friday afternoon (IV=Day, DV=Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and on a Friday afternoon and then measuring their immediate recall on the material covered in each session we would end up with the following:

The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.

The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

The null hypothesis is, therefore, the opposite of the alternative hypothesis in that it states that there will be no change in behavior.

At this point, you might be asking why we seem so interested in the null hypothesis. Surely the alternative (or experimental) hypothesis is more important?

Well, yes it is. However, we can never 100% prove the alternative hypothesis. What we do instead is see if we can disprove, or reject, the null hypothesis.

If we reject the null hypothesis, this doesn't really mean that our alternative hypothesis is correct - but it does provide support for the alternative / experimental hypothesis.

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Research Hypothesis: Definition, Types, & Examples

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A hypothesis (plural hypotheses) is a precise, testable statement of what the researcher(s) predict will be the outcome of the study. It is stated at the

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As a student, you may have heard buzz about the replication crisis in psychology. Some of the more sensational headlines paint a bleak picture of modern research and depending on to whom you speak, individuals can be optimistic while others are demoralized. So what is going on and is it really a crisis?

The dialogue around replication ignited in 2015 when Brian Nosek's lab reported that after replicating 100 studies from three psychology journals, researchers were unable to reproduce a large portion of findings. This report was controversial because it called into question the validity of research shared in academic journals. Publication in high profile journals requires the research to be subjected to a rigorous peer-review process. At this point, it is assumed the conclusions shared are trustworthy and others can now replicate or build upon the work. Following the Nosek study, more labs began to conduct replications and a disturbing trend emerged: a large portion of studies across multiple disciplines in science failed the replication test.

Replication is vital to psychology because studying human behavior is messy. There are numerous extraneous variables that can result in bias if researchers are not vigilant. Replication helps verify that the presence of a behavior at one point in time is not due to chance. The report that the Open Science Collaboration (2015) put forth did not undermine the peer-review process per se; rather, it highlighted a problem within the research culture. Journals were more likely to publish innovative studies over replication studies. Following the trends of the journals, researchers who require publications in order to advance their careers are unlikely to conduct a replication. As a result, without continued investigation, the exploratory studies can be treated as established lines of research rather than fledgling inquiries.

In response to the replication crisis, more individuals have been embracing the movement of transparency in research. The Open Science Foundation (OSF) and the Society for Improving Psychological Science (SIPS) have created opportunities for researchers to brainstorm means of strengthening research practices and provide avenues to share replication results. Based on these changes, I would argue the issue of replication was not a crisis, but an awakening for researchers who had become complacent to the consequences of the toxic elements of the research culture. Highlighting the issue resulted in dialogue and change. It is a perfect example of the dynamic nature of science and captures the essence of how a career in research can be intellectually stimulating, rewarding and sometimes frustrating.

From a student perspective, engaging in replication research is a useful tool to develop your own research skills. I have found that many students have misconceptions about how to conduct research. Some common behaviors include:

Assuming their idea is unique, but not conducting a thorough literature search to determine what is established.

Proposing studies that are too complicated or have design faults.

Lack of awareness of ethics or the approval process needed to conduct experiments.

Lack of experience in regard to data entry or statistical analysis.

Desire to change practices mid-study to increase participant compliance.

The replication movement has presented a unique opportunity for undergraduate researchers to provide meaningful contributions to science by bolstering evidence needed to substantiate exploratory findings. I teach a research seminar at Central Oregon Community College that requires teams to complete a replication study provided by the Collaborative Replication and Education Project (associated with OSF). Reviewers give feedback before and after data collection identifying problematic areas and insuring the study is an appropriate replication. Completed projects are shared on the website and exemplary studies are eligible for research badges. The process of replication requires students to slow down and analyze strategies. Over the course of the term, the student understanding of the process matures as groups question the choices of the original researchers. It is a high impact, low-risk educational environment because students learn valuable lessons whether or not the replication is successful.

Replication studies may not offer rewards for professionals, but there are direct incentives for students. Former seminar students have been able to add research experience to resumes, which, in turn, has allowed them to secure competitive positions in labs upon transfer to four-year institutions. My students have also reported feeling more prepared for upper division courses and more confidence in their abilities to conduct individual research.

If you would like to learn more about the replication movement or how you can begin a replication study, I suggest beginning with the reference below and visiting some of the websites of the organizations listed in this article.

Resources
Website for the Open Science Foundation Website for the Society for the Improvement of Psychological Science
References

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. DOI: 10.1126/science.aac4716

About the author

Andria Woodell, PhD, is a professor at Central Oregon Community College in Bend, Oregon.

She teaches a variety of courses ranging from introductory psychology, development, social, positive and psychology of violence. She provides trainings to local and regional groups on psychological issues that arise in the workplace. She co-developed the COCC Teaching and Learning Center and is the current coordinator of the COCC Teaching Externship.

Beyond the classroom, her primary focus is preparing community college students for a successful transition into the university system. She co-advises the COCC collaborative learning program, which develops students' professional skills and provides research/leadership opportunities beyond the classroom. In 2017, she earned the COCC Faculty Achievement award which is given to a faculty member who has demonstrated significant achievement in classroom teaching, leadership and professional excellence.

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Leaning into the replication crisis: Why you should consider conducting replication research

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As a student, you may have heard buzz about the replication crisis in psychology. Some of the more sensational headlines paint a bleak picture of modern research and depending on to whom you speak, individuals can be optimistic while others are demoralized. So what is going on and is it really a crisis?

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More widespread recognition of psychology as a science is one of the points of emphasis in the APA guidelines, Hill said.

A Scientific Discipline

Psychology's status as a science is grounded in the use of the scientific method, said Dominello. Psychologists base their professional practice in knowledge that is obtained through verifiable evidence of human behavior and mental processes. Psychological studies are designed very much like studies in other scientific fields. It is through these studies that psychologists contribute to the body of research in their field."Professionals in the field who 'do psychology' (e.g. research, teaching, psychotherapy) understand that psychology is a scientific discipline," said Dr. Nickolas H. Dominello, lead faculty for SNHU's undergraduate psychology program.

Learning to design these studies and interpret the findings is a significant part of psychology education. Undergraduate students learn to develop a research question and select a data collection method, and have the opportunity to design and refine a hypothetical research investigation, said Dominello.

Psychology is always growing and always building on itself, he said. "The subject of psychological science, behavior and mental processes is vast and complex," said Dominello. "Therefore, establishing conclusive evidence is challenging. Psychological research is cyclical, and published research findings often spawn additional inquiries. Each 'brick' of knowledge contributes to the overall structure of knowledge for a particular phenomenon."

So, if psychologists agree that psychology is a science, where does the confusion come from? What prompts some people to think of psychology as a soft science?

"I feel that in part, this misrepresentation of psychology stems from the diversity within the field (i.e. the various subfields) and the fact that psychological science findings often lead to more questions and avenues of future research. This contrasts with some of the more traditional sciences that only search for concrete, definitive answers," said Dominello.

Research Methods in Psychology

Psychology also utilizes a wider array of qualitative methods than some traditional sciences.

"Although qualitative research provides a different route to understanding than traditional quantitative methods, I feel that is also 'scientific,' just grounded in different philosophical underpinnings," said Dominello.

Research methods can be categorized as either quantitative or qualitative. Quantitative research results in numerical data that can be analyzed. Qualitative research employs methods like questionnaires, interviews and observations. Qualitative research can be analyzed by grouping responses into broad themes. This melding of quantitative and qualitative methods is essential to understand the human factor inherent in psychology.

"Psychology as a science embraces this broader exploratory perspective in order to better understand human phenomena. When merged, qualitative data can breathe life into quantitative data," Dominello said.

"Psychology is unique in that it adds breadth and depth of knowledge in conjunction with so many other disciplines, because we are all curious about understanding human behavior to some extent, whether it's one's own behavior or the behavior of others," said Hill.

This rich combination of qualitative and quantitative skills makes psychology a good undergraduate degree that can prepare students for a wide array of careers. Individuals with a bachelor's degree in psychology can pursue careers in social services, education, human resources and medical fields, using their education and skills as a foundation for understanding and working with others, Dominello said.

Where the Science of Psychology Can Be Applied

Hill said that a psychology undergraduate program's focus on effective communication, information literacy and understanding human behavior can lend itself to many areas outside psychology, including sales, marketing and many others.

Those who wish to practice as psychologists or work in academic research must pursue additional education beyond a bachelor's degree, including a master's degree in psychology and often a PhD. This advanced education in psychology often involves a strengthening of research skills, and an increased focus on the scientific method and the design of research studies, according to Dominello.

Is psychology a science? The short answer is yes, but the long answer is much more expansive and flexible. Psychology begins with the scientific method, and researchers employ many of the same methods as their colleagues in the natural and physical sciences, but psychology also calls for a deep understanding of human behavior that goes beyond science alone.
Pete Davies is a marketing and communications director in higher education. Follow him on Twitter @daviespete or connect on LinkedIn.

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More widespread recognition of psychology as a science is one of the points of emphasis in the APA guidelines, Hill said. Psychology's status as a science is grounded in the use of the scientific method, said Dominello. Psychologists base their professional practice in knowledge that is obtained through verifiable evidence of human behavior and mental processes.

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Reliability vs. Validity recorded mini-lecture

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Reliability vs. Validity recorded mini-lecture

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Revise Psychology: Reliability and Validity

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Psychology Revision series for A-level and AP Psychology teachers and students. Reliability and Validity looks at the key aspects of these important methodological concepts - from simple definitions, through an understanding of different types to examples of how they can be applied to different types of exam question.

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Imagine you are bowling one night, and every time you take a turn, you miss all of the pins. While you do not score a single point the whole night, you still have a reliable outcome due to your consistent losses. This is just one example of how something can be reliable but not valid. If you knocked down all the pins every time you went up, this would display both reliability and validity because you constantly achieved the goal intended for the game.

In the scientific method of psychological research, both reliability and validity are essential when utilizing any tools of measurement or tests. This text will define both terms, indicate their differences, and explore common issues in the scientific investigation regarding reliability and validity.

What are reliability and validity?

What are issues with reliability and validity?

How are reliability and validity used in research?

What are examples of reliability and validity?

Meaning of Reliability and Validity

At first glance, you may think these terms have very basic definitions; however, each of their meanings can be increasingly intricate and significant in terms of psychological research. Both concepts are fundamental to understand when learning about experiments and the scientific method.

In terms of scientific investigation, the definition of reliability is the presence of a stable and constant outcome after repeated measurement (Jackson, 2014). To put it into perspective, think of any form of psychological research using tests to measure specific outcomes. A test that is considered reliable will show similar outcomes each time it is administered. This c onsistency and dependability add value to the tests being used in research.

Validity is the term used to describe the indication that a test or tool of measurement is true and accurate. In other words, a valid test or tool is measuring the exact unit that it states to measure. There are examples of validity in day-to-day life. Think of a driver's license and how it is only valid if all the information about the driver is true and accurate. In psychology research, a test can only be considered valid if the outcome is accurate to what the test claims to measure.

Issues with Reliability and Validity

Within the domain of psychological research methods, any errors in the reliability and validity of a test or experiment are very detrimental to the value of the research. Before any scientific article, journal, or experiment can be posted, the findings must first meet standards of both reliability and validity.Unfortunately, instances in which these standards are not met may lead to unethical research and false or misleading claims.

Thalidomide Tragedy

During the 1950s and 60s, Thalidomide was thought to be a cure for nausea in pregnant women; however, it caused critical congenital disabilities in infants (Kim, 2011).

This is just one devastating example of what can happen once certain study standards are compromised. These significant moments in the history of scientific research put an emphasis on the importance of reliability and validity in the realm of scientific investigation.

Errors in Reliability

There are common errors made in psychological research methods that may impact the reliability of a study. These types of issues include:

Method Error

A method error can occur due to the experimenter's actions or the testing atmosphere.

Questions asked about method error include:

Trait Error

In trait errors, issues of reliability stem from the actual subjects of the experiments.

Questions asked about trait error include:

Imagine a test is administered to measure athleticism in various sports teams; however, one of the tested teams had food poisoning the same day. This could interfere with the reliability of the results.

Errors in Validity

Similar to the issues within reliability, certain types of errors in research may also jeopardize the experiment's validity. A few of these errors are known:

Maturation

Maturation may affect the validity of an outcome of long studies. Could the passage of time interfere with the initial performance of the test? How might a participant or test be affected during this time allotted?

Biases

Biases that may occur in the selection of participants may negatively impact the validityof the study. When the selection of the participants happens under bias, the ability for the study's outcomes to be generalized amongst a population becomes disabled.

Interaction Effects

Interaction effects can impact the validity in cases where there are pretests or multiple tests involved in one study. The application of a pretest can interfere with another measurement or test that follows.

Consider a test that aims to measure reading comprehension. The test taker is asked to read five articles in one session. Each piece is ten pages long. The validity of results regarding their comprehension may be affected due to factors caused by the application of multiple lengthy articles.

As you can see, many issues can influence the value and credibility of any scientific investigation or study. Analyzing the errors that may decrease the reliability and validity of research is one of the highest priorities in the scientific method.

Reliability and Validity in Research

The scientific method is applied in all facets of scientific research and investigation. This process employs rigorous empirical methods to get a reliable and valid outcome. There are several examples of reliability and validity in psychology research methods. Assessing these examples will help you better understand the type of reliability and validity for each situation in psychology research.

Examples of Reliability and Validity

There are four types of reliability in psychology research, all of which indicate levels of consistency in various situations. The three types of validity measure the truthfulness and accuracy of tests in many different ways.

Test/Retest Reliability

This type of reliability in research tests the consistency of results over time by administering the same test more than once.

Alternate-Forms Reliability

By using multiple forms of similar tests, a researcher can indicate whether the measurement is reliable depending on the consistency of an outcome. This is why the method is named alternate-forms.

Split-Half Reliability

This is when a study splits the test into two parts and measures the stability between measurement items in both test halves. While this does not account for the consistency over time, it does measure the reliability of the content within the test itself.

Interrater Reliability

This refers to measuring reliability by assessing the consistency of observations across raters/judges.

You can distinguish the differences between the types of reliability through their names! (i.e Inter rater = reliability measured in-between raters)

Content Validity

A test with content validity aims to measure the relevance across all content/ items within the given test, not just in one area.

Criterion Validity

The analysis of the accuracy of a test in predicting the abilities or outcomes of participants.

Construct Validity

One of the most important forms when measuring validity is construct validity. This is because it is one of the most utilized in psychology as it analyzes the extent to which a test measures the construct it claims to measure.

Within qualitative research methods, validity and reliability can be determined through the consistency and objectives of the data outcomes, participants, types of tests, and researcher observations.

Reliability and Validity - Key takeaways

Reliability is the presence of a stable and constant outcome after repeated measurement and v alidity is used to describe the indication that a test or tool of measurement is true and accurate.

Common issues in reliability include measurement errors like trait errors and method errors.

Issues in validity are maturation, biases, and interaction effects.

Four types of reliability are test/retest, alternate-forms, split-half, and interrater reliability.

Construct validity is very prominent in the field of psychology research. It analyzes the extent to which a test measures the construct it claims to measure.

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Imagine you are bowling one night, and every time you take a turn, you miss all of the pins. While you do not score a single point the whole night, you still have a reliable outcome due to your consistent losses. This is just one example of how something can be reliable but not valid.

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Case Study Research (Psychology Research Methods Explained) #Alevel

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Case Studies and the Survey Method (Intro Psych Tutorial #10)

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TYPES OF OBSERVATION METHODS IN PSYCHOLOGY

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Observation Method in Psychology: Naturalistic, Participant and Controlled

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Observation (watching what people do) would seem to be an obvious method of carrying out research in psychology. However, there are different types of observational methods, and distinctions need to be made between: 1. Controlled

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In psychology, the population is also called the target population. The population is the group of people in which a researcher is interested.

The size of the population can vary widely. Some studies target the entire human population, while others are interested only in a smaller group, for example, people from specific age groups or certain professions, and so on.

Usually, researchers can't recruit the entire population to participate in their studies. Therefore, researchers instead select a small group within the population called the sample. This method in psychology is called sampling.

The sample drawn should represent the population in which the researchers are interested to make generalisations about the population. Ideally, researchers would like to select a sample with the greatest representativeness and minimal bias. Researchers can then generalise the results to the target population with greater confidence.

Sampling the population, Katarina Gadže, StudySmarter Originals (Made with Canva images)

The following section is about the different sampling methods you need to know in A-level psychology. This section is about explaining, implementing, and evaluating each sampling technique.

An opportunity sample is a sample in which individuals are selected who are most available.

We can obtain an opportunity sample by asking members of the population if they are interested and willing to participate in the study.

Evaluation is the quickest and most convenient way to obtain a sample because participants are recruited based on availability.

However, there may be unavoidable bias since the sample is essentially self-selected.

Opportunity sampling has been criticised for its lack of representativeness and poor generalisability of results to the target population.

A voluntary sample is a sample recruited by self-selection. In other words, participants self-select and contact the researcher.

We obtain voluntary samples through word of mouth or advertising.

Volunteer sampling is a quick and inexpensive way to obtain many potential participants.

However, volunteer sampling also runs the risk of bias similar to opportunity sampling because the sample is essentially self-selected.

Bias can occur because certain types of people who agree to participate in the study may differ systematically from those who do not.

Volunteer sampling, Canva

A random sample is one in which everyone in the target population has an equal chance of being selected.

One possible method for selecting a random sample is the lottery method. Researchers assign a number to each potential participant and then create a list of random numbers to select participants for the random sample.

Random sampling is arguably the best sampling method because it will likely provide the most representative sample and generalisable results.

In addition, random samples are free from bias on the researcher's part, as the researcher does not influence the selection of participants.

Nevertheless, even a random sample is biased if selected individuals refuse to participate.

Moreover, a random sample is difficult to achieve because the recruitment process takes a lot of time and resources.

A systematic sample refers to selecting participants according to a set of patterns (also known as a sampling frame).

To draw a systematic sample, it is possible to list all the population members and then determine the desired sample size. If you divide the number of people in the target population by the desired sample size, you will get a number we call n. If you select every nth name, you will get a systematic sample of the size you want. For example, if you're going to draw a sample of 100 students from a university with 1,000 students, n = 1000/100 = 10, you can take every tenth name.

Systematic sampling is an objective method that can greatly reduce researcher bias.

However, as with random sampling, systematic sampling runs the risk of bias if selected individuals refuse to participate.

In addition, systematic sampling requires a complete list of the population, which is difficult to obtain and time-consuming.

A stratified sample is one in which researchers select participants according to their frequency in the target population. The researcher identifies the different groups that make up the target population and calculates the proportions needed to make the sample representative.

The identified groups are called strata (or subgroups), such as gender or age groups. Participants are randomly selected from each stratum in proportion to their occurrence in the population. Therefore, the sample should reflect the relative percentages of subgroups in the population.

Unlike opportunity sampling, this method is more likely to represent the underlying population, but it is constrained by our understanding of which subgroups are essential.

However, complete representation is impossible because stratification is not perfect. Researchers may not have perfect information about the composition of their target population.

Stratified sampling is time-consuming. As a result, we rarely see a stratified sampling in psychological research.

Sampling Psychology - Key takeaways

The population is the group of people in which a researcher is interested.

For practical reasons, researchers select a small group of participants from the population to participate in the study, called a sample.

Ideally, researchers aim for a sample with the greatest possible representativeness and minimal bias to generalise the results with confidence to the target population.

Each sampling method has its advantages and disadvantages. Researchers choose their preferred sampling method after weighing the representativeness of the sample and the economic cost.

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In psychology, the population is also called the target population. The population is the group of people in which a researcher is interested. The size of the population can vary widely. Some studies target the entire human population, while others are interested only in a smaller group, for example, people from specific age groups or certain professions, and so on.

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Sampling Methods March 7, 2021 - Paper 2 Psychology in Context | Research Methods Back to Paper 2 - Research Methods In Psychology, a sampling method is the way in which the researcher obtains the participants who will take part in their research. A sample is always drawn from the researcher's 'target population.'

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Understanding Target Population in Research

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Understanding Target Population in Research

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From this list, it is clear that having access to the total population of all the theoretical individuals in each of the above categories is virtually impossible regardless of which methodology you deploy. Therefore you need to evaluate the assets available to you for each of the target population groups.

Next, choose a sampling technique that will allow you to build a sample frame that will yield the sample size you require with the slightest sampling error and non-response bias from the data. It is also essential to avoid ambiguity when defining your target population. Take this political polling example:
'Individuals who are registered to vote in a primary or an election contest held in the state of South Carolina'
If this were the general election, this would be a perfectly appropriate definition. If this were the democratic primary, it would still be valid. South Carolina is an open primary state where members of any party affiliation can vote for the other party's candidate.

Since many states don't hold open primaries, you must specify registered: Democrats, Independents, and so on.

Here is a partial list of example ambiguous target population definitions:

Russians who have left the country

People who have recently become mothers

Frequent travelers

Individuals who are "gifted."

The list above is meant to highlight different cases where ambiguity can cloud the study design. In the first example:
'Russians who have left the country.'
It is unknown where the Russians live, and it is not defined here from which country they have departed. So we might assume it's Russia. But we could be mistaken. In the case of ' Recent mothers,' it is difficult to determine whether they have achieved this status through pregnancies for the first time or pregnancies, adoptions, foster programs, through marriage, or any number of other modern social scenarios.

Look at ' Frequent travelers'; you stop and ask, is this business travel or leisure? Is this overseas, or do trips to the next town count? Lastly, when it comes to people in the ' Gifted' category this day and age, in our estimation, doesn't everybody get a gold star for just getting through life? Absolutely!

The point is this, be extra cautious and thorough when defining the target population and when beginning to map out your study, and it will guide you in setting up your sampling design. Otherwise, you may end up with ambiguities in your data that will be hard to resolve or impossible to conduct any meaningful analysis.

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Understanding Target Population in Research | OvationMR

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From this list, it is clear that having access to the total population of all the theoretical individuals in each of the above categories is virtually impossible regardless of which methodology you deploy. Therefore you need to evaluate the assets available to you for each of the target population groups.

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Case study in psychology refers to the use of a descriptive research approach to obtain an in-depth analysis of a person, group, or phenomenon. A variety of techniques may be employed including personal interviews, direct-observation, psychometric tests, and archival records. In psychology case studies are most often used in clinical research to describe rare events and conditions, which contradict well established principles in the field of psychology. Case studies are generally a single-case design, but can also be a multiple-case design, where replication instead of sampling is the criterion for inclusion. Like other research methodologies within psychology, the case study must produce valid and reliable results in order to be useful for the development of future research. Distinct advantages and disadvantages are associated with the case study in psychology. The case study is sometimes mistaken for the case method, but the two are not the same.

One major advantage of the case study in psychology is the potential for the development of novel hypotheses for later testing. Second, the case study can provide detailed descriptions of specific and rare cases

Case studies cannot be used to determine causation.

^ Schultz & Schultz, Duane (2010). Psychology and work today. New York: Prentice Hall. pp. 201-202. ISBN 0-205-68358-4.

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Case study in psychology refers to the use of a descriptive research approach to obtain an in-depth analysis of a person, group, or phenomenon. A variety of techniques may be employed including personal interviews, direct-observation, psychometric tests, and archival records.

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Researcher Bias In Psychology [AP Psychology Unit 1 Topic 4] (1.4)

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Research bias is one of the dominant reasons for the poor validity of research outcomes. There are no hard and fast rules when it comes to research bias and this simply means that it can happen at any time; if you do not pay adequate attention.

The spontaneity of research bias means you must take care to understand what it is, be able to identify its feature, and ultimately avoid or reduce its occurrence to the barest minimum. In this article, we will show you how to handle bias in research and how to create unbiased research surveys with Formplus.

What is Research Bias?

Research bias happens when the researcher skews the entire process towards a specific research outcome by introducing a systematic error into the sample data. In other words, it is a process where the researcher influences the systematic investigation to arrive at certain outcomes.

When any form of bias is introduced in research, it takes the investigation off-course and deviates it from its true outcomes. Research bias can also happen when the personal choices and preferences of the researcher have undue influence on the study.

For instance, let's say a religious conservative researcher is conducting a study on the effects of alcohol. If the researcher's conservative beliefs prompt him or her to create a biased survey or have sampling bias, then this is a case of research bias.

Types of Research Bias

In many instances, poor research design or a pack of synergy between the different contributing variables in your systematic investigation can infuse bias into your research process. Research bias also happens when the personal experiences of the researcher influence the choice of the research question and methodology.

Example of Design Bias

A researcher who is involved in the manufacturing process of a new drug may design a survey with questions that only emphasize the strengths and value of the drug in question.

Selection bias happens when the research criteria and study inclusion method automatically exclude some part of your population from the research process. When you choose research participants that exhibit similar characteristics, you're more likely to arrive at study outcomes that are uni-dimensional.
Examples of Selection Bias

1. Administering your survey online; thereby limiting it to internet savvy individuals and excluding members of your population without internet access.

2. Collecting data about parenting from a mother's group. The findings in this type of research will be biased towards mothers while excluding the experiences of the fathers.

Peer-reviewed journals and other published academic papers, in many cases, have some degree of bias. This bias is often imposed on them by the publication criteria for research papers in a particular field. Researchers work their papers to meet these criteria and may ignore information or methods that are not in line with them.

This is a type of research bias that creeps in during data processing. Many times, when sorting and analyzing data, the researcher may focus on data samples that confirm his or her thoughts, expectations, or personal experiences; that is, data that favors the research hypothesis.

This means that the researcher, albeit deliberately or unintentionally, ignores data samples that are inconsistent and suggest research outcomes that differ from the hypothesis. Analysis bias can be far-reaching because it alters the research outcomes significantly and provides a false presentation of what is obtainable in the research environment.
Example of Analysis Bias
While researching cannabis, a researcher pays attention to data samples that reinforce the negative effects of cannabis while ignoring data that suggests positives.

Data collection bias is also known as measurement bias and it happens when the researcher's personal preferences or beliefs affect how data samples are gathered in the systematic investigation. Data collection bias happens in both q ualitative and quantitative research methods.

In qualitative research, data collection bias happens when you ask bad survey questions during a semi-structured or unstructured interview. Bad survey questions are questions that nudge the interviewee towards implied assumptions. Leading and loaded questions are common examples of bad survey questions.

Procedural is a type of research bias that happens when the participants in a study are not given enough time to complete surveys. The result is that respondents end up providing half-thoughts and incomplete information that does not provide a true representation of their thoughts.

There are different ways to subject respondents to procedural respondents. For instance, asking respondents to complete a survey quickly to access an incentive, may force them to fill in false information to simply get things over with.
Example of Procedural Bias

Asking employees to complete an employee feedback survey during break time. This timeframe puts respondents under undue pressure and can affect the validity of their responses.

Bias in Quantitative Research

In quantitative research, the researcher often tries to deny the existence of any bias, by eliminating any type of bias in the systematic investigation. Sampling bias is one of the most types of quantitative research biases and it is concerned with the samples you omit and/or include in your study.

Types of Quantitative Research Bias

Design bias occurs in quantitative research when the research methods or processes alter the outcomes or findings of a systematic investigation. It can occur when the experiment is being conducted or during the analysis of the data to arrive at a valid conclusion.

Many times, design biases result from the failure of the researchers to take into account the likely impact of the bias in the research they conduct. This makes the researcher ignore the needs of the research context and instead, prioritize his or her preferences.

Sampling bias in quantitative research occurs when some members of the research population are systematically excluded from the data sample during research. It also means that some groups in the research population are more likely to be selected in a sample than the others.

Sampling bias in quantitative research mainly occurs in systematic and random sampling. For example, a study about breast cancer that has just male participants can be said to have sampling bias since it excludes the female group in the research population.

Bias in Qualitative Research

In qualitative research, the researcher accepts and acknowledges the bias without trying to deny its existence. This makes it easier for the researcher to clearly define the inherent biases and outline its possible implications while trying to minimize its effects.

Qualitative research defines bias in terms of how valid and reliable the research results are. Bias in qualitative research distorts the research findings and also provides skewed data that defeats the validity and reliability of the systematic investigation.

Types of Bias in Qualitative Research

The interviewer or moderator in qualitative data collection can impose several biases on the process. The moderator can introduce bias in the research based on his or her disposition, expression, tone, appearance, idiolect, or relation with the research participants.

The framing and presentation of the questions during the research process can also lead to bias. Biased questions like leading questions, double- barrelled questions, negative questions, and loaded questions, can influence the way respondents provide answers and the authenticity of the responses they present.

Biased reporting is yet another challenge in qualitative research. It happens when the research results are altered due to personal beliefs, customs, attitudes, culture, and errors among many other factors. It also means that the researcher must have analyzed the research data based on his/her beliefs rather than the views perceived by the respondents.

Bias in Psychology

Cognitive biases can affect research and outcomes in psychology. For example, during a stop-and-search exercise, law enforcement agents may profile certain appearances and physical dispositions as law-abiding. Due to this cognitive bias, individuals who do not exhibit these outlined behaviors can be wrongly profiled as criminals.

Another example of cognitive bias in psychology can be observed in the classroom. During a class assessment, an invigilator who is looking for physical signs of malpractice might mistakenly classify other behaviors as evidence of malpractice; even though this may not be the case.

Bias in Market Research

There are 5 common biases in market research - social desirability bias, habituation bias, sponsor bias, confirmation bias, and cultural bias. Let's find out more about them.

Social desirability bias happens when respondents fill in incorrect information in market research surveys because they want to be accepted or liked. It happens when respondents are seeking social approval and so, fail to communicate how they truly feel about the statement or question being considered.

A good example will be market research to find out preferred sexual enhancement methods for adults. Some persons may not want to admit that they use sexual enhancement drugs to avoid criticism or disapproval.

Habituation bias happens when respondents give similar answers to questions that are structured in the same way. Lack of variety in survey questions can make respondents lose interest, become non-responsive, and simply regurgitate answers.

For example, multiple-choice questions with the same set of answer options can cause habituation bias in your survey. What you get is that respondents just choose answer options without reflecting on how well their choices represent their thoughts, feelings, and ideas.

Sponsor bias takes place when respondents have an idea of the brand or organization that is conducting the research. In this case, their perceptions, opinions, experiences, and feelings about the sponsor may influence how they answer the questions about that particular brand.

For example, let's say Formplus is carrying out a study to find out what the market's preferred form builder is. Respondents may mention the sponsor for the survey (Formplus) as their preferred form builder out of obligation; especially when the survey has some incentives.

Confirmation bias happens when the overall research process is aimed at confirming the researcher's perception or hypothesis about the research subjects. In other words, the research process is merely a formality to reinforce the researcher's existing beliefs.

Electoral polls often fall into the confirmation bias trap. For example, civil society organizations that are in support of one candidate can create a survey that paints the opposing candidate in a bad light to reinforce beliefs about their preferred candidate.

How to Identify Bias in a Research

1. Pay attention to research design and methods.

2. Observe the data collection process. Does it lean overwhelmingly towards a particular group in the survey population?

3. Look out for bad survey questions like loaded questions and negative questions.

4. Observe the data sample you have to confirm if it is a fair representation of your research population.

How to Avoid Research Bias

1. Gather data from multiple sources: Be sure to collect data samples from the different groups in your research population.

2. Verify your data: Before going ahead with the data analysis, try to check in with other data sources, and confirm if you are on the right track.

3. If possible, ask research participants to help you review your findings: Ask the people who provided the data whether your interpretations seem to be representative of their beliefs.

4. Check for alternative explanations: Try to identify and account for alternative reasons why you may have collected data samples the way you did.

5. Ask other members of your team to review your results: Ask others to review your conclusions. This will help you see things that you missed or identify gaps in your argument that need to be addressed.

How to Create Unbiased Research Surveys with Formplus

Formplus has different features that would help you create unbiased research surveys. Follow these easy steps to start creating your Formplus research survey today:

Go to your Formplus dashboard and click on the "create new form" button. You can access the Formplus dashboard by signing into your Formplus account here.

After you click on the "create new form" button, you'd be taken to the form builder. This is where you can add different fields into your form and edit them accordingly. Formplus has over 30 form fields that you can simply drag and drop into your survey including rating fields and scales.

After adding form fields and editing them, save your form to access the builder's customization features. You can tweak the appearance of your form here by changing the form theme and adding preferred background images to it.

Copy the form link and share it with respondents.

The first step to dealing with research bias is having a clear idea of what it is and also, being able to identify it in any form. In this article, we've shared important information about research bias that would help you identify it easily and work on minimizing its effects to the barest minimum.

Formplus has many features and options that can help you deal with research bias as you create forms and questionnaires for quantitative and qualitative data collection. To take advantage of these, you can sign up for a Formplus account here.

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Research Bias: Definition, Types + Examples

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Research bias is one of the dominant reasons for the poor validity of research outcomes. There are no hard and fast rules when it comes to research bias and this simply means that it can happen at any time; if you do not pay adequate attention.

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When undertaking a clinical trial, the two major models that one can use are the single blind and double blind trials. Selecting the right trial is important since it can affect the outcome of the trial or introduce errors. The ideal model should be selected based on the type of trial and other variables. For any clinical trial, there are usually two groups of people who are experimented on. Members of one group are given a placebo, and the members of another group are given the treatment that is being studied. This is so as to compare the effectiveness of the treatment to placebo.

In a single blind study, the participants in the clinical trial do not know if they are receiving the placebo or the real treatment. This is done to reduce the risk of errors, since some participants might produce spurious results if they know that they are taking the placebo or medication. In this model, the experimenter monitoring the participants knows which individuals received the placebo and which ones got the treatment under examination.

In a double-blind study, both the participants and the experimenters do not know which group got the placebo and which got the experimental treatment. This is considered to be the superior model of clinical research since it eliminates outcomes that are produced due to placebo effect, as well as observer bias by the experimenter. The fact that the experimenter does not know which group received the placebo or the experimental drug means that the risk of conscious and unconscious observer bias is reduced, making the study more accurate.

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What Is The Difference Between Single Blind And Double Blind Clinical Trials?

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What Is The Difference Between Single Blind And Double Blind Clinical Trials? at Biopharma Institute -Online certification training courses for clinical research, laboratory, and pharmaceutical professionals.

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Before understanding about single and double-blind techniques, it is important that you understand the amazing power of the placebo effect. This is an interesting film about placebos and their effect.

[youtube https://www.youtube.com/watch?v=yfRVCaA5o18&w=560&h=315]

Single Blind: A single-blind design is when the participant doesn't know if they are in the "treatment group" or the "control (e.g. placebo) group".

The treatment group in an experiment is the group that experiences the factor that the researchers hypothesize will have an effect. The control does not receive this treatment, but something in its place. In many studies the control group will receive a placebo.

For example, let's say a new drug is designed to cure cancer (we wish!). The researchers want to see if the drug actually works. Because of the power of the placebo effect the researchers want to give half the participants the real drug and the other half a placebo pill. A single blind design would mean that the participants wouldn't know which group they're in. It should be easy to see why this is important in such studies that test the effects of a particular treatment; if participants know they're not getting the actual treatment then the chances it'll work are reduced.

Double Blind: In a double blind design neither the participant nor the person gathering the dependent variable data knows which group the participant is in. Of course at least one person knows, but this is not the person gathering the data. To use the cancer pill example above, they could do something like keep a secret file of who is receiving which treatment and this is not revealed to the Doctors who are monitoring the effects the pill is having on the cancer.

The double-blind technique is valuable to reduce researcher bias. It is only natural that a researcher would want to prove their own hypothesis right. When data is highly subjective there is the potential for researcher bias to affect results. Not knowing which treatment the participant is receiving is one way of reducing the chances researcher bias will influence the experiment's results.
In animal experiments obviously the animals don't have the cognitive capacity to figure out which condition they're in, or that they're even in an experiment. But the researchers will often not know. There's no need to state that it's a double-blind because it's redundant and a single-blind is inaccurate. In this cause it can simply be called a "blind study" or "blind design".

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Single and Double Blind Designs | IB Psychology

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How are single blind and double-blind techniques used in experiments? Before understanding about single and double-blind techniques, it is important that you understand the amazing power of the placebo effect. This is an interesting film about placebos and their effect.

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Double blind studies - Intro to Psychology

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Single Blind Studies and Double Blind Studies

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Learn how placebos are being used in the study and treatment of psychological disorders—and how you can learn more with a PhD in Clinical Psychology.

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How Ice Cream Kills! Correlation vs. Causation

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HOW TO CONDUCT CORRELATIONAL RESEARCH IN PSYCHOLOGY?

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A correlation refers to a relationship between two variables.﻿﻿ Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation.

What Correlation Means

There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation. Researchers can present the results using a numerical value called the correlation coefficient.﻿﻿

Positive correlations: Both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation.

Negative correlations: As the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation.

No correlation:There is no relationship between the two variables. A correlation coefficient of 0 indicates no correlation.

What Is a Correlation Coefficient?

How Correlational Studies Work

Correlational studies are a type of research often used in psychology, as well as other fields like medicine.﻿﻿ Correlational research is a preliminary way to gather information about a topic. The method is also useful if researchers are unable to perform an experiment.

Researchers use correlations to see if a relationship between two or more variables exists, but the variables themselves are not under the control of the researchers.

While correlational research can demonstrate a relationship between variables, it cannot prove that changing one variable will change another. In other words, correlational studies cannot prove cause-and-effect relationships.﻿﻿

Types of Correlational Research

There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.

Naturalistic Observation

The naturalistic observation method involves observing and recording variables of interest in a natural setting without interference or manipulation.﻿﻿

This method is well-suited to studies where researchers want to see how variables behave in their natural setting or state.﻿﻿ Inspiration can then be drawn from the observations to inform future avenues of research.

In some cases, it might be the only method available to researchers; for example, if lab experimentation would be precluded by access, resources, or ethics. It might be preferable to not being able to conduct research at all, but the method can be costly and usually takes a lot of time.﻿﻿

However, this does not mean that researchers will get reliable data from watching the variables, or that the information they gather will be free from bias.

For example, study subjects might act differently if they know that they are being watched. The researchers might not be aware that the behavior that they are observing is not necessarily the subject's natural state (i.e., how they would act if they did not know they were being watched).

Researchers also need to be aware of their biases, which can affect the observation and interpretation of a subject's behavior.﻿﻿

The Survey Method

Surveys and questionnaires are some of the most common methods used for psychological research. The survey method involves having a random sample of participants complete a survey, test, or questionnaire related to the variables of interest.﻿﻿ Random sampling is vital to the generalizability of a survey's results.

If researchers need to gather a large amount of data in a short period of time, a survey is likely to be the fastest, easiest, and cheapest option.﻿﻿

It's also a flexible method because it lets researchers create data-gathering tools that will help ensure they get the information they need (survey responses) from all the sources they want to use (a random sample of participants taking the survey).

Survey data might be cost-efficient and easy to get, but it has its downsides. For one, the data is not always reliable-particularly if the survey questions are poorly written or the overall design or delivery is weak.﻿﻿ Data is also affected by specific faults, such as unrepresented or underrepresented samples.

For example, some people might struggle to understand the questions. A person might answer a particular way to try to please the researchers or to try to control how the researchers perceive them (such as trying to make themselves "look better").

Sometimes, respondents might not even realize that their answers are incorrect or misleading because of mistaken memories.

Archival Research

Many areas of psychological research benefit from analyzing studies that were conducted long ago by other researchers, as well as reviewing historical records and case studies.﻿﻿

For example, in an experiment known as "The Irritable Heart," researchers used digitalized records containing information on American Civil War veterans to learn more about post-traumatic stress disorder (PTSD).﻿﻿

Using records, databases, and libraries that are publically accessible or accessible through their institution can help researchers who might not have a lot of money to support their research efforts.

Another potential benefit is that these sources often provide an enormous amount of data that was collected over a very long period of time, which can give researchers a way to view trends, relationships, and outcomes related to their research.

While the inability to change variables can be a disadvantage of some methods, it can be a benefit of archival research. That said, using historical records or information that was collected a long time ago also presents challenges. For one, important information might be missing or incomplete and some aspects of older studies might not be useful to researchers in a modern context.

A primary issue with archival research is reliability. When reviewing old research, little information might be available about who conducted the research, how a study was designed, who participated in the research, as well as how data was collected and interpreted.

Researchers can also be presented with ethical quandaries-for example, should modern researchers use data from studies that were conducted unethically or with questionable ethics?

Limitations of Correlational Studies

You've probably heard the phrase, "correlation does not equal causation." This means that while correlational research can suggest that there is a relationship between two variables, it cannot prove that one variable will change another.

For example, researchers might perform a correlational study that suggests there is a relationship between academic success and a person's self-esteem. However, the study cannot show that academic success changes a person's self-esteem.

To determine why the relationship exists, researchers would need to consider and experiment with other variables, such as the subject's social relationships, cognitive abilities, personality, and socioeconomic status.

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Why Correlational Studies Are Used in Psychological Research

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A correlation refers to a relationship between two variables.﻿﻿ Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation. There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation.

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Variables are phenomena in research that can change and, in psychology, usually refer to a phenomenon or event in which an objective measure has been linked. The essence of studying anything in psychology is to observe the changes in variables in order to test the hypotheses relating to the relationship between variables.

Variables are elements within an experimental study of psychology which are pivotal to the validity and findings of the research. They are factors that are manipulated and measured, or controlled, or can have an unexpected effect on the results if not taken into consideration.

There are five primary forms of variables, one of which has four subcategories of variables. These are as follows:

Independent variable (IV)

Dependent variable (DV)

Control variable

Random variable

Extraneous/confounding variable

Situational variable

Participant/person variable

Experimenter/investigator effects

Demand characteristics

Measuring Variables

The first step in the process of research is to find a way in which to quantify variables, which involves a process known as operationalisation. Some phenomena naturally lend themselves to being quantifiable, such as age, height, time, etc. However, psychological researchers will often want to measure other things such as stress, or intelligence for example. Operationalisation involves taking abstract concepts and turning them into something that can be objectively measurable. For example, intelligence tests such as the Wechsler Adult Intelligence Test (WAIS) measures intelligence and cognitive ability in adults through a series of standardised logic and mathematical questions to create an intelligence quotient (IQ) score for each individual that takes the test.

The majority of variables used in psychological research tend to be in the form of numbers, such as height, age, time, as examples. These are known as measured variables and can be used to place individuals along a scale, as well as being more easily manipulated during data observations.

One challenge in psychology is to adapt phenomena so that they can be measured through numbers. Sometimes this is not always possible. For example, you cannot simply put a number on emotions (although some researchers may measure other characteristics associated with an emotion to make it a measured variable). Factors that cannot be represented through numbers, such as names, are known as categorical variables as each value of the variable is a qualitatively separate category. As an example, lets say we were researching the veracity of nominative determinism (whether your name influences your job) by separating people by surnames (independent variable) which would be a categorical variable (e.g. "Baker", "Smith", "Cook", etc.). We would then see whether their current or past jobs were in any way related to their surname (dependent variable), which would be another categorical variable. This is not to say that a numerical value could not be added to the results once the data had been collected, but both variables are collected as categorical variables).

Independent Variables (IV)

The independent variable is one which can be manipulated or controlled in order to see how it affects other variables. By changing the volume of any given independent variable, we are able to investigate the causal relationship between the factor being manipulated and the factor in which we are measuring.

For example, if we were to conduct an experiment on the efficacy of a new drug to treat depression, we would label the independent variable as the dosage of the new drug. Does increased dosage of the new drug alleviate symptoms of depression in comparison to participants who took a placebo drug?

Another example, we are studying the effects of sleep on driving skills. The independent variable for this study would be sleep. We can manipulate the amount of sleep and quality of sleep in participants to see how this can affect driving skills. Could poor sleep have a negative impact on driving skills and vice versa?

The choice of an independent variable can have a large impact on the study and can also change what is being studied. When choosing the independent variable within an experiment, it must be closely related to the theoretical variable, that is a variable within the initial theory. Through the operationalisation process, we can develop a plan to how the IV can be manipulated within the study. The IV must be both practical (i.e. easily manipulated) and theoretically relevant (i.e. it must be linked to the initial theory).

Experimental research studies will almost always have only one IV as more than one can skew results and make it difficult to understand which variable was impactful or more impactful on the variable being measured. This can cause an issue with internal validity.

Dependent Variables (DV)

The dependent variable is one that is being measured in research and therefore the one that is being studied. The dependent variable that is chosen, as well as the methods used in the study, will define the results and outcome of the experiment. Through operationalisation, the DV must be inherently measurable in order for us to understand whether a study has been successful or not.

Taking one of our previous examples, if we were to study the efficacy of a new drug as treatment for depression, our DV in this instance would be on depression. Through operationalisation, we might measure depression through the Beck Depression Inventory (questionnaire) as well as other means.

The way in which we operationalise the DV is extremely important and can impact the validity of our study. With a topic like depression, we must be sure that the measurements we are taking are in relation to the DV. For example, we might want to measure the level of sleep in someone who is diagnosed with depression; lack of or too much sleep are both symptoms of depression, but they could also be impacted by other variables within participants (i.e. an individual might have insomnia, or may simply not have sleep as a symptom of their depression).

Just like the IV, DV has to be both practical (i.e. measurements are practically possible) and must be related to the theoretical variable (i.e. what we are measuring is related to the initial variable of the theory).

Extraneous/Confounding Variables

Extraneous variables are those other than the independent variable which could have affect the results found in the dependent variable. All variables outside out the independent variable could have an impact on the dependent variable and so should therefore either be controlled or considered as a random variable that will not have an impact.

There are four types of extraneous variable:

1. Situational variables - These are aspects of the environment which may have an impact on the participant's behaviour. This can include noise, lighting, and temperature among others. Environmental factors should be controlled within an experiment as far as practically possible so they are as close to the same for all participants.

2. Participant/person variables - This refers to the way in which participants can vary from one another in all aspects such as mood, intelligence, concentration or stress among others. This variable can be somewhat controlled by the use of random allocation into experimental groups so as to avoid influencing the results in any way.

3. Experimenter effects - These relate to unconscious signals from experimenters which are conveyed to the participants regarding how they should behave in the experiment.

4. Demand characteristics - This type of extraneous variable relates to all the clues within an experiment which could lead to a participant either working out the purpose of the study, or thinking that they have worked out the purpose of the study, whether or not they are right or wrong. This can lead to an unnatural change in the behaviour of a participant which can skew the results of the dependent variable. Participants are influenced by all aspects of a study, including the following:

1. Their surroundings

2. The researchers' characteristics

3. The researchers' behaviour

4. Their interpretation of the situation.

As such with all of these extraneous variables, researchers should use standardised procedures when conducting their experiment and aim to control any confounding variables that may arise ahead of the experiment.

Control Variables

Control variables are those in which could have an impact on the DV and therefore the results of the entire study, and so are controlled by researchers. Taking due care and attention into variables that could have an impact on results helps protect the validity of the experiment by taking steps promote accuracy in the findings.

As an example, if we were studying the effects of the brightness of lighting on stress, there are several other variables which could impact stress which need to be taken into account. One potential control variable might be noise; making sure the laboratory does not have any unwanted internal or external noises which could cause stress in participants. Another factor might simply be what the participant was up to before they arrived at the experiment. They may have been stuck in traffic, had an argument with someone, or read the news. We could potentially control this by asking the all participants to sit for 15 minutes with a glass of water while they wait.

By limiting the number of variables which can impact the DV, we essentially help promote potential validity in the experiment and so these variables should not be overlooked.

Random Variables

Random variables are those which researchers allow to change randomly between experiments. This may be because they are either too difficult to control or that they are not relevant to the study. For example, the gender of an individual may be allowed to be a random variable if researchers believe this will not impact the results of the study.

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Variables in Psychological Research (Study Guide) | PsychBite

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Variables are phenomena in research that can change and, in psychology, usually refer to a phenomenon or event in which an objective measure has been linked. The essence of studying anything in psychology is to observe the changes in variables in order to test the hypotheses relating to the relationship between variables.