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Understanding Experimental Design: Independent and Dependent Variables in Research

This document outlines a comprehensive method for conducting experiments, focusing on how to identify and manipulate independent and dependent variables. It details the steps involved in testing hypotheses, including the use of experimental and control groups, as well as various levels of independent variables. The document also discusses reliability and validity in measurements, providing insights into assessing the effectiveness of different variables in influencing behavior. Clear examples are used to illustrate the principles of experimental design, making it accessible for researchers.

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Understanding Experimental Design: Independent and Dependent Variables in Research

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  1. Our “more complex” method 1. Collect data about current behavior 2a. Experimental group - Change the suspected cause 2b. Control group - Don’t change the suspected cause 3. Don’t change anything else 4. Collect data about subsequent behavior 5. Compare data collected before and after the change was made

  2. Experiments • The method that has been discussed thus far is referred to as an Experiment • Variations on this method are the primary ways that researchers try to establish that one thing causes something else to happen

  3. Independent Variables • The aspects of the study that vary across time or across groups are known as Independent Variables • This will also be referred to as the manipulation in a given study • The manipulation will have, at least, two levels (sometimes called conditions)

  4. Our experimental designs • Experimental group • Pre-Test, Verbal Training w/ Feedback, Post-Test • Control group • Pre-Test, Verbal Training w/o Feedback, Post-Test

  5. Our Independent Variables • Feedback • Two conditions, i.e., with and without feedback • Session • Three levels, i.e., Pre-Test, Training, Post-Test

  6. Kinds of Independent Variables • Between-Subjects • Each participant receives only one condition of the Independent Variable • Within-Subjects • Each participant receives every level of the Independent Variable

  7. Our Independent Variables • Between-Subjects • Feedback • Two conditions, i.e., with and without feedback • Within-Subjects • Session • Three levels, i.e., Pre-Test, Training, Post-Test

  8. Kinds of Independent Variables • Manipulated • The researcher has control of these Independent Variables and can change them • Subject • The researcher does not have control over these Independent Variables, i.e., cannot change them • This lack of control prevents a research from being able to establish whether or not the Independent Variable caused something to happen • Studies with Subject variables are referred to as Quasi-Experiments

  9. Our Independent Variables • Both of our Independent Variables (Feedback and Session) are Manipulated variables

  10. How many levels (conditions) should my IV have?

  11. Experiment 1 Tipping Attentiveness

  12. Experiment 2 Tipping Attentiveness

  13. Experiment 3 Tipping Attentiveness

  14. Experiment 4 Tipping Attentiveness

  15. Summary • Two studies showed that attentiveness does not affect tipping • One study showed that attentiveness helped tipping, while a different study showed that attentiveness hurt tipping • What would you conclude?

  16. Benefits of > 2 IV Levels Tipping Attentiveness

  17. Dependent Variables • The data that you collect, as part of the logical method described earlier, is known as the Dependent Variable

  18. Our Dependent Variable • Accuracy • | (Estimated Distance - Actual Distance) | • Notes • The estimated distance refers to verbal and active estimates of distance

  19. A different way to think about IVs and DVs

  20. Identifying IVs and DVs • What behavior do the researchers measure? • That is the Dependent Variable • What do the researchers think influences (affects) the behavior being measured? • That is the Independent Variable • What two (or more) elements make up the Independent Variable? • Those are the conditions or levels of the Independent Variable

  21. Types of IVs • Is the influence something that the researcher can control? • If yes, then it is a manipulated IV • If no, then it is a subject IV • Was the influence distributed across groups? • If yes, then it is a between-subjects IV • If no, then it is a within-subjects IV • What behavior do the researchers measure? • That is the DV • What do the researchers think influences (affects) the behavior being measured? • That is the IV • What two (or more) elements make up the Independent Variable? • Those are the conditions or levels of the IV

  22. Relations Between IV Types • Manipulated • Can be between-subjects or within-subjects • Subject • Can ONLY be between-subjects

  23. How do I know that I have a good measure?

  24. Do I have a good measure? • To answer this question, researchers have to assess the reliability and the validity of their measures • Both of these are qualities that you want in a measurement

  25. Do I have a good measure? • Reliability • Do you get the same measurements if you make the measurements again?

  26. Do I have a good measure? • There are two common ways to examine the reliability of a measure • Split-half • Correlate the two halves of the measurement device • Test-retest • Give the measure • Give it again at a later date (usually after a week or two, if the thing being measured is a trait) • Correlate the two scores

  27. Do I have a good measure? • Validity • Are you measuring what you think you are measuring? • Different ways to assess • Face Validity • Criterion Validity • Construct Validity

  28. An example … • Farmer & Sundberg (1986) developed a scale of boredom proneness, which I will use as an example of how people validate a new measure • Farmer, R., & Sundberg, N. D. (1986). Boredom proneness: The development and correlates of a new scale. Journal of Personality Assessment, 50, 4-17 • They examined the Face, Criterion, and Construct Validity of their new measurement device

  29. Do I have a good measure? • Face Validity • Does the measurement device appear to be measuring what you think you are measuring? • For example, asking someone directly about their level of anxiousness has more Face Validity than inferring it from the number of times that he/she gets up out of a seat

  30. Farmer & Sundberg (1986) • There is no formal assessment technique for face validity • In this case, you look at what questions are asked, e.g., “Frequently when I am working I find myself worrying about other things”, or “I am good at waiting patiently”

  31. Do I have a good measure? • Criterion Validity • Does the measurement device give similar results to other ways of assessing the topic? • For example, does your new schizophrenia scale correlate with ratings of schizophrenia made by trained professionals?

  32. Farmer & Sundberg (1986) • Farmer and Sundberg (1986) correlated their new measure with • Self-rating of general boredom and interest • Self-ratings of boredom and interest in a specific class • A measure of boredom susceptibility • A measure of boredom at work

  33. Do I have a good measure? • Construct Validity • Is the construct being measured unique and separate from other similar constructs? • For example, is mental workload really different from stress?

  34. Farmer & Sundberg (1986) • Farmer and Sundberg (1986) correlated their new measure with measures of • Depression • Hopelessness • Perceived effort • Loneliness • Experience seeking • Life satisfaction • Course grades

  35. Summary • The logic of establishing cause-and-effect • Independent Variables • Manipulated Variables • Subject Variables • Dependent Variables • Reliability • Validity

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