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Experiment Basics: Variables

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

2 Journal Summary 1 due in labs this week
Announcements

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

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

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

6 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

7 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

8 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

9 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

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

11 Selecting every nth person
Systematic sampling

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

13 Use the participants who are easy to get
Convenience sampling

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

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

16 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

17 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

18 Blue Green Red Purple Yellow List 1

19 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?

20 Blue Green Red Purple Yellow List 2

21 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

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

23 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

24 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 Majors, class level, seating in classroom,… These are not confounds, because they don’t co-vary with the IV Blue Green Red Purple Yellow Blue Green Red Purple Yellow

25 Experimental Control Our goal:
To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV. Control is used to: Minimize excessive variability To reduce the potential of confounds (systematic variability not part of the research design) Experimental Control

26 Experimental Control Our goal:
To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV. T = NRexp + NRother + R Nonrandom (NR) Variability NRexp: Manipulated independent variables (IV) Our hypothesis: the IV will result in changes in the DV NRother: extraneous variables (EV) which covary with IV Condfounds Random (R) Variability Imprecision in measurement (DV) Randomly varying extraneous variables (EV) Experimental Control

27 Experimental Control: Weight analogy
Variability in a simple experiment: T = NRexp + NRother + R Absence of the treatment (NRexp = 0) Treatment group Control group “perfect experiment” - no confounds (NRother = 0) R NR exp other R NR other Experimental Control: Weight analogy

28 Experimental Control: Weight analogy
Variability in a simple experiment: T = NRexp + NRother + R Control group Treatment group NR exp R R Difference Detector Our experiment is a “difference detector” Experimental Control: Weight analogy

29 Experimental Control: Weight analogy
If there is an effect of the treatment then NRexp will ≠ 0 Control group Treatment group R NR exp R Difference Detector Our experiment can detect the effect of the treatment Experimental Control: Weight analogy

30 Things making detection difficult
Potential Problems Confounding Excessive random variability Difference Detector Things making detection difficult

31 Potential Problems Confound
If an EV co-varies with IV, then NRother component of data will be present, and may lead to misattribution of effect to IV IV DV Co-vary together EV Potential Problems

32 Confounding Confound R NR R
Hard to detect the effect of NRexp because the effect looks like it could be from NRexp but could be due to the NRother R NR R other NR exp Difference Detector Experiment can detect an effect, but can’t tell where it is from Confounding

33 Confound Hard to detect the effect of NRexp because the effect looks like it could be from NRexp but could be due to the NRother These two situations look the same R NR exp other Difference Detector R R NR other Difference Detector There is an effect of the IV There is not an effect of the IV Confounding

34 Potential Problems Excessive random variability
If experimental control procedures are not applied Then R component of data will be excessively large, and may make NRexp undetectable Potential Problems

35 Excessive random variability
If R is large relative to NRexp then detecting a difference may be difficult R R NR exp Difference Detector Experiment can’t detect the effect of the treatment Excessive random variability

36 Reduced random variability
But if we reduce the size of NRother and R relative to NRexp then detecting gets easier So try to minimize this by using good measures of DV, good manipulations of IV, etc. R NR exp R Difference Detector Our experiment can detect the effect of the treatment Reduced random variability

37 Controlling Variability
How do we introduce control? Methods of Experimental Control Constancy/Randomization Comparison Production Controlling Variability

38 Methods of Controlling Variability
Constancy/Randomization If there is a variable that may be related to the DV that you can’t (or don’t want to) manipulate Control variable: hold it constant Random variable: let it vary randomly across all of the experimental conditions Methods of Controlling Variability

39 Methods of Controlling Variability
Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment Training group No training (Control) group Without control groups if is harder to see what is really happening in the experiment It is easier to be swayed by plausibility or inappropriate comparisons Useful for eliminating potential confounds Methods of Controlling Variability

40 Methods of Controlling Variability
Comparison An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups This is often the absence of the treatment Sometimes there are a range of values of the IV 1 week of Training group 2 weeks of Training group 3 weeks of Training group Methods of Controlling Variability

41 Methods of Controlling Variability
Production The experimenter selects the specific values of the Independent Variables 1 week of Training group 2 weeks of Training group 3 weeks of Training group Need to do this carefully Suppose that you don’t find a difference in the DV across your different groups Is this because the IV and DV aren’t related? Or is it because your levels of IV weren’t different enough Methods of Controlling Variability

42 So far we’ve covered a lot of the about details experiments generally
Now let’s consider some specific experimental designs. Some bad (but common) designs Some good designs 1 Factor, two levels 1 Factor, multi-levels Between & within factors Factorial (more than 1 factor) Experimental designs

43 Poorly designed experiments
Bad design example 1: Does standing close to somebody cause them to move? “hmm… that’s an empirical question. Let’s see what happens if …” So you stand closely to people and see how long before they move Problem: no control group to establish the comparison group (this design is sometimes called “one-shot case study design”) Poorly designed experiments

44 Poorly designed experiments
Bad design example 2: Testing the effectiveness of a stop smoking relaxation program The participants choose which group (relaxation or no program) to be in Poorly designed experiments

45 Poorly designed experiments
Bad design example 2: Non-equivalent control groups Self Assignment Independent Variable Dependent Variable Training group Measure participants No training (Control) group Random Assignment Measure Problem: selection bias for the two groups, need to do random assignment to groups Poorly designed experiments

46 Poorly designed experiments
Bad design example 3: Does a relaxation program decrease the urge to smoke? Pretest desire level – give relaxation program – posttest desire to smoke Poorly designed experiments

47 Poorly designed experiments
Bad design example 3: One group pretest-posttest design Dependent Variable Independent Variable Dependent Variable participants Pre-test Training group Post-test Measure Pre-test No Training group Post-test Measure Add another factor Problems include: history, maturation, testing, and more Poorly designed experiments

48 1 factor - 2 levels Good design example
How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough 1 Factor (Independent variable), two levels Basically you want to compare two treatments (conditions) The statistics are pretty easy, a t-test 1 factor - 2 levels

49 1 factor - 2 levels Good design example
How does anxiety level affect test performance? participants Low Moderate Test Random Assignment Anxiety Dependent Variable 1 factor - 2 levels

50 1 factor - 2 levels Good design example
How does anxiety level affect test performance? One factor Use a t-test to see if these points are statistically different low moderate test performance anxiety anxiety Two levels low moderate 60 80 Observed difference between conditions T-test = Difference expected by chance 1 factor - 2 levels

51 1 factor - 2 levels Advantages:
Simple, relatively easy to interpret the results Is the independent variable worth studying? If no effect, then usually don’t bother with a more complex design Sometimes two levels is all you need One theory predicts one pattern and another predicts a different pattern 1 factor - 2 levels

52 1 factor - 2 levels Interpolation Disadvantages:
“True” shape of the function is hard to see Interpolation and Extrapolation are not a good idea low moderate test performance anxiety What happens within of the ranges that you test? Interpolation 1 factor - 2 levels

53 1 factor - 2 levels Extrapolation Disadvantages:
“True” shape of the function is hard to see Interpolation and Extrapolation are not a good idea Extrapolation low moderate test performance anxiety What happens outside of the ranges that you test? high 1 factor - 2 levels

54 1 Factor - multilevel experiments
For more complex theories you will typically need more complex designs (more than two levels of one IV) 1 factor - more than two levels Basically you want to compare more than two conditions The statistics are a little more difficult, an ANOVA (Analysis of Variance) 1 Factor - multilevel experiments

55 1 Factor - multilevel experiments
Good design example (similar to earlier ex.) How does anxiety level affect test performance? Two groups take the same test Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success Grp2 (low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough Grp3 (high anxiety group): 5 min lecture on how the students must pass this test to pass the course 1 Factor - multilevel experiments

56 1 factor - 3 levels participants Low Moderate Test Random Assignment
Anxiety Dependent Variable High 1 factor - 3 levels

57 1 Factor - multilevel experiments
low mod test performance anxiety anxiety low mod high high 80 60 60 1 Factor - multilevel experiments

58 1 Factor - multilevel experiments
Advantages Gives a better picture of the relationship (function) Generally, the more levels you have, the less you have to worry about your range of the independent variable 1 Factor - multilevel experiments

59 Relationship between Anxiety and Performance
low moderate test performance anxiety 2 levels high low mod test performance anxiety 3 levels Relationship between Anxiety and Performance

60 1 Factor - multilevel experiments
Disadvantages Needs more resources (participants and/or stimuli) Requires more complex statistical analysis (analysis of variance and pair-wise comparisons) 1 Factor - multilevel experiments

61 Pair-wise comparisons
The ANOVA just tells you that not all of the groups are equal. If this is your conclusion (you get a “significant ANOVA”) then you should do further tests to see where the differences are High vs. Low High vs. Moderate Low vs. Moderate Pair-wise comparisons


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