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Experiment Basics: Variables

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Presentation on theme: "Experiment Basics: Variables"— Presentation transcript:

1 Experiment Basics: Variables
Psych 231: Research Methods in Psychology

2 Journal Summary 1 due in labs this week
Announcements

3 Results Mean: 78.3% Median: 78% Range: 59-92 If you want to go over your exam set up a time to see me Exam 1

4 Many kinds of Variables
Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables Many kinds of Variables

5 Many kinds of Variables
Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables Many kinds of Variables

6 Measuring your dependent variables
Scales of measurement Errors in measurement Reliability & Validity Sampling error Measuring your dependent variables

7 Measuring the true score
Example: Measuring intelligence? How do we measure the construct? How good is our measure? How does it compare to other measures of the construct? Is it a self-consistent measure? Measuring the true score Mythbusters

8 Errors in measurement In search of the “true score” Reliability
Do you get the same value with multiple measurements? Validity Does your measure really measure the construct? Is there bias in our measurement? (systematic error) Errors in measurement

9 Bull’s eye = the “true score”
Dartboard analogy

10 Dartboard analogy reliable valid unreliable invalid reliable invalid
Bull’s eye = the “true score” Reliability = consistency Validity = measuring what is intended Measurement error Estimate of true score biased reliable valid unreliable invalid reliable invalid Dartboard analogy

11 Reliability True score + measurement error
A reliable measure will have a small amount of error Multiple “kinds” of reliability Reliability

12 Reliability Test-restest reliability
Test the same participants more than once Measurement from the same person at two different times Should be consistent across different administrations Reliable Unreliable Reliability

13 Reliability Internal consistency reliability
Multiple items testing the same construct Extent to which scores on the items of a measure correlate with each other Cronbach’s alpha (α) Split-half reliability Correlation of score on one half of the measure with the other half (randomly determined) Reliability

14 Reliability Inter-rater reliability At least 2 raters observe behavior
Extent to which raters agree in their observations Are the raters consistent? Requires some training in judgment 5:00 4:56 Reliability

15 Does your measure really measure what it is supposed to measure?
There are many “kinds” of validity Validity

16 Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE
CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity

17 Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE
CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity

18 At the surface level, does it look as if the measure is testing the construct?
“This guy seems smart to me, and he got a high score on my IQ measure.” Face Validity

19 Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct Construct Validity

20 Internal Validity The precision of the results
Did the change in the DV result from the changes in the IV or does it come from something else? Internal Validity

21 Threats to internal validity
Experimenter bias & reactivity History – an event happens the experiment Maturation – participants get older (and other changes) Selection – nonrandom selection may lead to biases Mortality (attrition) – participants drop out or can’t continue Regression to the mean – extreme performance is often followed by performance closer to the mean The SI cover jinx Threats to internal validity

22 Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?” External Validity

23 External Validity Variable representativeness
Relevant variables for the behavior studied along which the sample may vary Subject representativeness Characteristics of sample and target population along these relevant variables Setting representativeness Ecological validity - are the properties of the research setting similar to those outside the lab External Validity

24 Measuring your dependent variables
Scales of measurement Errors in measurement Reliability & Validity Sampling error Measuring your dependent variables

25 Sampling Errors in measurement Sampling error
Population Everybody that the research is targeted to be about The subset of the population that actually participates in the research Sample Sampling

26 Sampling Population Sampling to make data collection manageable
Inferential statistics used to generalize back Sampling to make data collection manageable Sample Allows us to quantify the Sampling error Sampling

27 Sampling Goals of “good” sampling: Key tool: Random selection
Maximize Representativeness: To what extent do the characteristics of those in the sample reflect those in the population Reduce Bias: A systematic difference between those in the sample and those in the population Key tool: Random selection Sampling

28 Sampling Methods Probability sampling Non-probability sampling
Simple random sampling Systematic sampling Stratified sampling Non-probability sampling Convenience sampling Quota sampling Have some element of random selection Susceptible to biased selection Sampling Methods

29 Simple random sampling
Every individual has a equal and independent chance of being selected from the population Simple random sampling

30 Selecting every nth person
Systematic sampling

31 Cluster sampling Step 1: Identify groups (clusters)
Step 2: randomly select from each group Cluster sampling

32 Use the participants who are easy to get
Convenience sampling

33 Quota sampling Step 1: identify the specific subgroups
Step 2: take from each group until desired number of individuals Quota sampling

34 Variables Independent variables Dependent variables
Measurement Scales of measurement Errors in measurement Extraneous variables Control variables Random variables Confound variables Variables

35 Extraneous Variables Control variables
Holding things constant - Controls for excessive random variability Random variables – may freely vary, to spread variability equally across all experimental conditions Randomization A procedure that assures that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. Confound variables Variables that haven’t been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s) Co-varys with both the dependent AND an independent variable Extraneous Variables

36 Colors and words Divide into two groups:
men women Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Women first. Men please close your eyes. Okay ready? Colors and words

37 Blue Green Red Purple Yellow List 1

38 Okay, now it is the men’s turn.
Remember the instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Okay ready?

39 Blue Green Red Purple Yellow List 2

40 So why the difference between the results for men versus women?
Is this support for a theory that proposes: “Women are good color identifiers, men are not” Why or why not? Let’s look at the two lists. Our results

41 List 2 Men List 1 Women Blue Green Red Purple Yellow Blue Green Red
Matched Mis-Matched

42 What resulted in the performance difference?
Our manipulated independent variable (men vs. women) The other variable match/mis-match? Because the two variables are perfectly correlated we can’t tell This is the problem with confounds Blue Green Red Purple Yellow Blue Green Red Purple Yellow IV DV Confound Co-vary together

43 What DIDN’T result in the performance difference?
Extraneous variables Control # of words on the list The actual words that were printed Random Age of the men and women in the groups These are not confounds, because they don’t co-vary with the IV Blue Green Red Purple Yellow Blue Green Red Purple Yellow

44 “Debugging your study”
Pilot studies A trial run through Don’t plan to publish these results, just try out the methods Manipulation checks An attempt to directly measure whether the IV variable really affects the DV. Look for correlations with other measures of the desired effects. “Debugging your study”


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