Chapter 7

1. Chapter 7 Experimental Design: Independent Groups Design

2. Independent groups designs • We can confidently conclude a cause and effect relationship between variables if (and only if) the appropriate study has been conducted. • Goal in conducting experiments  to show that an IV causes a change in the DV. • True experiment – the independent variable must be under the control of the researcher. • Quasi-experimental design – the IV is not manipulated and/or is a characteristic

3. Steps in conducting an experiment • Step 1. Formulate a hypothesis • Hypothesis – a statement about the expected relationships between variables. • Step 2. Select appropriate independent and dependent variables • Operationalize, or make measurable, the IV and DV.

4. Steps in conducting an experiment • Step 3. Limit alternative explanations for variation • Consider what other variables might be involved and find ways to control them. • Step 4. Manipulate the IVs and measure the DVs • Carry out the experiment and collect the data.

5. Steps in conducting an experiment • Step 5. Analyze the variation in the DVs • Choose the appropriate statistical technique to analyze the variance in the DV. • Step 6. Draw inferences about relationship between IVs and DVs • Use inferential statistical procedures to make statements about populations based on your sample findings.

6. Where we do experiments • Controlled experiments in the laboratory • Advantages: • Better control over the independent variable • Superior control over secondary or extraneous sources of variation • Can more precisely measure the DV  Improved internal validity

7. Where we do experiments • Controlled experiments in the laboratory • Disadvantages: • Some phenomena can’t be studied in the lab • Some research topics present ethical problems • Practical disadvantages (e.g.. costly, time consuming) •  Outcomes may not be applicable to the real world (lack external validity)

8. Where we do experiments • Experiments in the field • Improved external validity • May lack internal validity (because of lack of control)

9. How we do experiments: Independent groups designs • Important assumption in experimental design  initial equivalence of groups • Independent groups design • Participants are randomly and independently assigned to each level of the independent variable. • Also known as between participants design.

10. Independent groups designs • Completely randomized groups designs: One IV • Research participants are randomly assigned to different levels of one IV. • Simplest completely randomized design: two group design where participants are randomly assigned and independently assigned to either an experimental group or a control group (i.e.. IV has two levels).

11. Independent Groups Design - Stats • With one IV having two levels • t-test for independent groups • One-way ANOVA • Total variance is partitioned into between groups variance and within group variance. • Between groups variance is due to the treatment and to other factors (chance, individual differences, etc). • Within group variance is due to only the other factors (not to the treatment). • F test compares BGV and WGV while correcting for sample size –- F= MSB/MSW • The greater the effect of the treatment, the greater will be the numerator and the greater F will be.

12. One-Factor Between Groups Designs • Some experiments require more than two levels of the independent variable to test the hypothesis. • This is especially true when the relationship between the variables is believed to be complex. • More that two levels allows researchers a finer grained analysis

13. One Factor BG Design Stats • One-way ANOVA is the correct test • Need to conduct follow-up tests to compare groups means • In a three group design you would have • 1 vs 2, 1 vs 3 and 2 vs 3 • Tukey’s HSD is used to make these comparisons

14. Our THC and STM Study • Problem – The effects of THC Intoxication on the ability to do occupational tasks requiring STM • Research Hypothesis – THC intoxication will impair STM • IV – Three smoked THC doses • 0%, 5%, 10% • DV – Span test for words at different time intervals • 15min, 1hr and 3hrs • The average of these three times

15. SPSS Analysis • Using our file from the previous assignment • Analyze ---- Compare Means ---- One-way ANOVA • SpanT1, SpanT2, SpanT3 and SpanAvg entered into the “Dependent Varaibles” box • THC entered into the “Fixed Factor” box • Options ---- click “Descriptives” • Post –Hoc ---- click “Tukey’s”

16. Descriptives

17. ANOVA Table

18. Post-Hocs ---Tukey’s HSD

19. Statistical Conclusions • What do the post-hoc patterns tell us? • T1 • T2 • T3 • Avg

20. When There is More Than One Cause – Studies with Multiple IVs • Various causes can be investigated individually, via a series of simple experiments, each manipulating only one independent variable, OR • They can be manipulated together in a single, more complex experiment. • These designs are called FACTORIAL DESIGNS

21. Factorial Experiments • A factorial experiment is one in which there are two or more causes (independent variables) that are believed to affect the dependent variable. • Factorial experiments are more like real life, in that there can be multiple causes for the same behavior. • We can test for these multiple causes

22. Factorial Experiments • Allow researchers to determine whether the causes interact with each other • An interaction occurs when the effect of one cause depends on the level of the other cause that is present. • Single-factor experiments do not allow experimenters to detect interactions.

23. Two of More Factors in the Same Experiment • typical names for factorial designs:2 X 3 (read: “2 by 3”)3 X 62 X 2 X 3 • There is one numeral for each independent variable.2 X 3 has two independent variables 3 X 6 has two independent variables 2 X 2 X 3 has three independent variables

24. Understanding Factorial Designs • The value of each numeral indicates the number of levels of that variable. • 2 X 3: has 2 levels in one variable, 3 levels in the other • 3 X 6: has 3 levels in one variable, 6 levels in the other • 2 X 2 X 3: has 2 levels in two of the variables, and 3 levels in the third variable • The product indicates the number of separate combinations of the two variables present in the experiment.2 X 3 = 63 X 6 = 182 X 2 X 3 = 12

25. Cells in Factorial Designs • A “cell” is a particular combination of levels of the independent variables. • Each level of every independent variable is systematically combined with each level of every other independent variable. • A factorial design can be represented by a table in which the levels of one factor are represented by the rows and the levels of the other factor are represented by the columns.

27. Combining the Factors Factor A - Alcohol Level 1 - A Level 2 - P cell 1, 1 Alc + No Anx cell 1, 2 Pla + No Anx Level 1 - No Factor B Anxiety Level 2 - Yes cell 2, 2 Pla + Anx cell 2, 1 Alc + Anx

28. Assigning Participants to Groups • Participants are assigned randomly to “groups.” • Participants in each group experience only one combination of levels of each of the variables.

30. Partitioning the Variance • For Factor A: Level 1 is compared with Level 2 • For Factor B: Level 1 is compared with Level 2 • For the Interaction between Factor A and Factor B:Level 1, 1 compared with Level 1, 2, compared with Level 2, 1 compared with Level 2, 2(all cells compared with each other)

32. ANOVA Summary Table

33. Factorial ANOVA • A separate F ratio is calculated for: • each main effectFactor AFactor B • the interaction:A X B • When there are more than two levels to an IV you would then do follow-ups

34. Main Effects • For Factor A: Is the mean of Level 1 significantly different from the mean of level 2? • For Factor B: Is the mean of Level 1 significantly different from the mean of level 2?

35. Possible Outcomes • Either main effect can be significant. • Both main effects can be significant. • The interaction can be significant. • Any combination of the above outcomes can occur.

38. Interactions • Do the individual cell means differ significantly from the grand mean?