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Chapter 4 Notes

by: Alisha orr

Chapter 4 Notes Psych 302-50

Alisha orr
U of L
GPA 3.2

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About this Document

Notes on the complete chapter 4
Experimental Psychology
Lora Haynes
Class Notes
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This 10 page Class Notes was uploaded by Alisha orr on Saturday July 16, 2016. The Class Notes belongs to Psych 302-50 at University of Louisville taught by Lora Haynes in Summer 2016. Since its upload, it has received 22 views. For similar materials see Experimental Psychology in Psychlogy at University of Louisville.


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Date Created: 07/16/16
Chapter 4 notes Scientific studies tend to focus on one or the other of two major activities: 1) Exploratory data and analysis, which is aimed at classifying behaviors within a given area of research, identifying potentially important variables, and identifying relationships between those variables and the behaviors. Such exploration is typically in the early stages of research. 2) Hypothesis testing, consist of evaluating potential explanations for the observed relationships. Hypothesis testing usually begins after you have collected enough information about the behavior to begin developing supportable explanations. Casual vs. Correlational Relational Relationships In a causal relationship, one variable directly or indirectly influences another. In other words, changes in the value of one variable directly or indirectly cause changes in the value of the second. Causal relationships can be either unidirectional or bidirectional. In unidirectional variable A will influence variable B, but not vice versa, in bidirectional each variable influences the other. In a correlational relationship, changes in one variable accompany changes in another, but the proper test have not been conducted to show that either variable actually causes changes in the other. All that is known is that a relationship between them exist. Correlational Research In correlational research your main interest is to determine whether two (or more) variables covary and if so, to establish the directions, magnitudes, and forms of the observed relationships. (Covary – when changes in one variable tend to be accompanied by specific changes in another, the two variables are said to be covary.) When you use correlational relationships for prediction, the variable used to predict is called the predictor variable. The variable whose value is being predicted is called the criterion variable. The possibility that correlational relationships may result from the action of an observed “third variable” is called the third variable problem. This unobserved variable may influence both of the observed variables, causing them to vary together even though no direct relationship exist between them. To solve the third variable problem, you must examine the effects of each potential third variable to determine whether it does, in fact, account for the observed relationship. A second reason why it is hazardous to draw casual inferences from correlational data is that, even when a direct causal relationship exists, the direction of causality is sometimes difficult to determine. This difficulty is known as the directionality problem. Gathering data in early stages of research The correlational approach’s ability to identify potential causal relationships can provide a rich source of hypotheses that later may be tested experimentally. Experimental Research Unlike correlational research, experimental research incorporates a high degree of control over the variables of your study. If used properly will allow you to establish causal relationships among your variables. Experimental research has two defining characteristics manipulation of one or more independent variables and control over extraneous variables. Manipulation of Independent Variables An independent variable is a variable who values are chosen and set by the experimenter. We call these set values the levels of the independent variable. To manipulate your independent variable, you must expose your participants to at least two levels of that variable. The specific conditions associated with each level are called the treatments of the experiment. The variable whose value you observe and measure in experimental designs is called the dependent variable. Another way to think about the dependent variable is that its value depends on the behavior of the participant, rather than being set by the experimenter. In the most basic of experimental designs, the group receiving the treatment is called the experimental group and the other group is called the control group (control group is treated the same as the experimental group but is not exposed to the treatment) The second characteristics of experimental research is control over extraneous variables. Extraneous variables are those that may affect behavior that you wish to investigate but are not of interest for the present experiment. You have two ways to control the effects of extraneous variables. 1) Hold extraneous variables constant. If these variables do not vary over the course of your experiment, they cannot cause uncontrolled variation in your dependent variable. 2) Randomize their effects across treatment. This technique deals with the effects of extraneous variables that cannot be held constant or, for reasons that will be explained later, should not be held constant. For statistical reasons, one of the better ways to accomplish this goal is to use random assignment of subjects for treatment. With random assignment, you assign participants to treatments randomly by picking names out of a hat. Strengths and Limitations of the Experimental Approach The great strength of the experimental approach is its ability to identify and describe causal relationships. This ability is not shared by the correlational approach Despite its power to identify causal relationships, the experimental approach has limitations that restrict its use under certain conditions. 1) You cannot use the experimental method if you cannot manipulate your hypothesized casual variables. 2) A second limitation of the experimental approach entails the tight control over extraneous factors required to clearly reveal the effects of the independent variable. Such control tends to reduce your ability to apply your findings to situations that differ from the conditions of your original experiment. Experiments vs. Demonstrations Demonstration – lacks an independent variable and exposes a group of subjects to one (and only one) treatment condition.  Remember, a true experiment requires exposing subjects to at least two treatments Internal and External Validity Internal validity – the ability of your research design to adequately test your hypotheses. In an experiment this means showing that variation in the independent variable, and only the independent variable, caused the observed variation in the dependent variable. In a correlational study it means showing that changes in the value in your criterion variable relate solely to changes in the value of your predictor variable and not changes in other, extraneous variables that may have varied along with your predictor variable. Whenever two or more variables combine in such a way that their effects cannot be separated, a confounding of those variables has occurred. Threats to internal validity Confounding variables occur in both experimental and correlational designs, but they are far more likely to be a problem in the latter. History - may confound studies in which multiple observations are taken over time. Specific events may occur between observations that affect the results. Maturation - refers to the effect of age or fatigue. Performance changes observed over time due to these factors may confound those due to the variables being studied. Testing – testing prior to the treatment changes how subjects respond in posttreatment testing Instrumentation – unobserved changes in observer criteria or instrument calibration confound the effect of the treatment Statistical regression – subjects selected for treatment on the basis of their extreme scores tend to move closer to the mean on retesting Biased selection of subjects – groups of subjects exposed to different treatments are not equivalent prior to treatment Experimental morality – differential loss of subjects from the groups of a study results in nonequivalent groups ***The time to be concerned with internal validity is during the design phase of your study. - During this phase you should carefully plan which variables will be manipulated or observed and recorded. Identify any plausible viral hypotheses not eliminated in you initial design, and redesign so as to eliminate those that threaten internal validity. External Validity A study has external validity to the degree that its results can be extended beyond the limited research setting and sample in which they were obtained. Factors affecting external validity: 1) Using highly controlled lab settings 2) Reactive testing – occurs when a pretest affects participants reactions to an experimental variable 3) Interactions between participant selection biases and the independent variable – effects observed may apply to the participants included in the study 4) Reactive effects of experimental arrangements – refers to the effects of highly artificial experimental situations used in some research 5) Multiple treatment interference – occurs when participants are exposed to multiple experimental treatments in which early treatments affects responses to later treatments Internal vs. External validity The setting in which you conduct your research strongly influences the internal and external validity of your results. For example a tightly controlled lab experiment affords you a relatively high degree of internal validity. Research settings A laboratory setting is any research setting that is artificial relative to the setting in which the behavior naturally occurs. A field experiment is an experiment conducted in a participants natural environment


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