What teachers are saying about Study. What Is Survey Research? Are you still watching? Your next lesson will play in 10 seconds. Add to Add to Add to. Want to watch this again later? What is Developmental Research? Non-Comparative Scales in Marketing Research.
What is Historical Research? Selecting a Problem to Research. The True Experimental Research Design. Research Methods in Psychology: Research Methods in Psychology for Teachers: Information Systems and Computer Applications. Devin Kowalczyk Devin has taught psychology and has a master's degree in clinical forensic psychology. This lesson explores the different ways that a researcher can understand individuals or groups of people, both in terms of psychological research as well as general research in other fields.
Definitions Sometimes an individual wants to know something about a group of people. There are three ways a researcher can go about doing a descriptive research project, and they are: Observational , defined as a method of viewing and recording the participants Case study , defined as an in-depth study of an individual or group of individuals Survey , defined as a brief interview or discussion with an individual about a specific topic Let's look at specific ways we can use each of these.
Observational If I say, 'chimpanzees,' what do you think? Survey A survey comes in different flavors, be it interviewing people face to face or handing out questionnaires to fill out.
Try it risk-free No obligation, cancel anytime. Want to learn more? Select a subject to preview related courses: Case Study Case studies are a little more in-depth than an observation and typically a little more holistic.
Lesson Summary Descriptive research is a study designed to depict the participants in an accurate way. The three main ways to collect this information are: Observational , defined as a method of viewing and recording the participants Case study , defined as an in-depth study of an individual or group of individuals Survey , defined as a brief interview or discussion with the individuals about a specific topic Learning Outcomes After watching this video lesson, you might be able to: Realize the purpose of descriptive research Mention three ways to do descriptive research Emphasize two types of observational research Note the contrasts between case studies and observations Highlight ways of performing survey research.
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Browse Articles By Category Browse an area of study or degree level. Education and Career Roadmap. You are viewing lesson Lesson 1 in chapter 5 of the course:. Research Methods in Psychology 16 chapters lessons 12 flashcard sets. Setting Up the Research Data Collection Techniques in Qualitative Research Methods and Help and Review Educational Psychology: Tutoring Solution Introduction to Psychology: Certificate Program Research Methods in Psychology: Browse by Lessons Interpersonal Therapy: Tutoring Solution Clinical Assessment: Tutoring Solution Introduction to Anxiety Disorders: Tutoring Solution Stress Disorders: Latest Courses Computer Science Network Forensics Computer Science Latest Lessons Getting Started with Study.
How to Pass the PE Exam. Create an account to start this course today. Like this lesson Share. Browse Browse by subject. Upgrade to Premium to enroll in Psychology Part c of Figure 2. 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 2. 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. Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient r between variables that have curvilinear relationships will likely be close to zero. Adapted from Stangor, C. 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 direction of the linear relationship is indicated by the sign of the correlation coefficient. The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero its absolute value. 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.
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 2. Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.
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 behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade 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 the researcher is tempted to assume that viewing violent television causes aggressive play,.
One alternate 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:. 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:. 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. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:.
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. If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear.
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 behavior 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. 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:. Consider an experiment conducted by Anderson and Dill The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior.
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 behavior was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.
Two advantages of the experimental research design are 1 the assurance that the independent variable also known as the experimental manipulation occurs prior to the measured dependent variable, and 2 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 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 behavior, 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.
Testing and interpreting interactions. A psychological study of the strange situation. Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.
Journal of Personality and Social Psychology, 78 4 , — The return of Phineas Gage: This may be an attempt to test a theory with a typical case or it can be a specific topic that is of interest. Research should be thorough and note taking should be meticulous and systematic. The first foundation of the case study is the subject and relevance.
In a case study, you are deliberately trying to isolate a small study group, one individual case or one particular population. For example, statistical analysis may have shown that birthrates in African countries are increasing. A case study on one or two specific countries becomes a powerful and focused tool for determining the social and economic pressures driving this. In the design of a case study, it is important to plan and design how you are going to address the study and make sure that all collected data is relevant.
Unlike a scientific report, there is no strict set of rules so the most important part is making sure that the study is focused and concise; otherwise you will end up having to wade through a lot of irrelevant information. It is best if you make yourself a short list of 4 or 5 bullet points that you are going to try and address during the study. If you make sure that all research refers back to these then you will not be far wrong. With a case study, even more than a questionnaire or survey , it is important to be passive in your research.
You are much more of an observer than an experimenter and you must remember that, even in a multi-subject case, each case must be treated individually and then cross case conclusions can be drawn.
Analyzing results for a case study tends to be more opinion based than statistical methods. The usual idea is to try and collate your data into a manageable form and construct a narrative around it.
Use examples in your narrative whilst keeping things concise and interesting. It is useful to show some numerical data but remember that you are only trying to judge trends and not analyze every last piece of data. Constantly refer back to your bullet points so that you do not lose focus. It is always a good idea to assume that a person reading your research may not possess a lot of knowledge of the subject so try to write accordingly.
In addition, unlike a scientific study which deals with facts, a case study is based on opinion and is very much designed to provoke reasoned debate. There really is no right or wrong answer in a case study. Check out our quiz-page with tests about:. Martyn Shuttleworth Apr 1, Case Study Research Design.
Descriptive research is a study designed to depict the participants in an accurate way. More simply put, descriptive research is all about describing people who take part in the study. More simply put, descriptive research is all about describing people who take part in the study.
Descriptive research can be explained as a statement of affairs as they are at present with the researcher having no control over variable. Moreover, “descriptive studies may be characterised as simply the attempt to determine, describe or identify what is, while analytical research attempts to establish why it is that way or how it came to be” .
Descriptive research methods are pretty much as they sound — they describe situations. They do not make accurate predictions, and they do not determine cause and effect. There are three main types of descriptive methods: observational methods, case-study methods and survey methods. By the term descriptive research, we mean a type of conclusive research study which is concerned with describing the characteristics of a particular individual or group. It includes research related to specific predictions, features or functions of person or group, the narration of facts, etc.
Many of the benefits and limitations of the specific descriptive research methods have been alluded to in previous modules in this series. Following is a summary regarding both the advantages and the disadvantages of using descriptive research methodology in general. Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.