1. IS 4800 Empirical Research Methods for Information Science Class Notes March 16, 2012 Instructor: Prof. Carole Hafner, 446 WVH hafner@ccs.neu.edu Tel: 617-373-5116 Course Web site: www.ccs.neu.edu/course/is4800sp12/

2. Outline Sampling and statistics (cont.) T test for paired samples T test for independent means Analysis of Variance Two way analysis of Variance

3. 3 Relationship Between Population and Samples When a Treatment Had No Effect

4. 4 Relationship Between Population and Samples When a Treatment Had An Effect

5. Population  Mean?Variance? Sampling Sample of size N Mean values from all possible samples of size N aka “distribution of means”    Z M = ( M - 

6. Z tests and t-tests t is like Z: Z = M - μ / t = M – μ / μ = 0 for paired samples We use a stricter criterion (t) instead of Z because is based on an estimate of the population variance while is based on a known population variance. S 2 = Σ (X - M) 2 = SS N – 1 N-1 S 2 M = S 2 /N

7. Given info about population of change scores and the sample size we will be using (N) T-test with paired samples Now, given a particular sample of change scores of size N We can compute the distribution of means We compute its mean and finally determine the probability that this mean occurred by chance ?  = 0 S 2 est  2 from sample = SS/df df = N-1 S 2 M = S 2 /N

8. t test for independent samples Given two samples Estimate population variances (assume same) Estimate variances of distributions of means Estimate variance of differences between means (mean = 0) This is now your comparison distribution

9. Estimating the Population Variance S 2 is an estimate of σ 2 S 2 = SS/(N-1) for one sample (take sq root for S) For two independent samples – “pooled estimate”: S 2 = df 1 /df Total * S 1 2 + df 2 /df Total * S 2 2 df Total = df 1 + df 2 = (N1 -1) + (N2 – 1) From this calculate variance of sample means: S 2 M = S 2 /N needed to compute t statistic S 2 difference = S 2 Pooled / N1 + S 2 Pooled / N2

10. t test for independent samples, continued This is your comparison distribution NOT normal, is a ‘t’ distribution Shape changes depending on df df = (N1 – 1) + (N2 – 1) Distribution of differences between means Compute t = (M1-M2)/SDifference Determine if beyond cutoff score for test parameters (df,sig, tails) from lookup table.

11. ANOVA: When to use Categorial IV numerical DV (same as t-test) HOWEVER: –There are more than 2 levels of IV so: –(M1 – M2) / Sm won’t work

12. 12 ANOVA Assumptions Populations are normal Populations have equal variances More or less..

13. 13 Basic Logic of ANOVA Null hypothesis –Means of all groups are equal. Test: do the means differ more than expected give the null hypothesis? Terminology –Group = Condition = Cell

14. 14 Accompanying Statistics Experimental –Between-subjects Single factor, N-level (for N>2) –One-way Analysis of Variance (ANOVA) Two factor, two-level (or more!) –Factorial Analysis of Variance –AKA N-way Analysis of Variance (for N IVs) –AKA N-factor ANOVA –Within-subjects Repeated-measures ANOVA (not discussed) –AKA within-subjects ANOVA

15. 15 The Analysis of Variance is used when you have more than two groups in an experiment –The F-ratio is the statistic computed in an Analysis of Variance and is compared to critical values of F –The analysis of variance may be used with unequal sample size (weighted or unweighted means analysis) –When there are just 2 groups, ANOVA is equivalent to the t test for independent means ANOVA: Single factor, N-level (for N>2)

16. One-Way ANOVA – Assuming Null Hypothesis is True… Within-Group Estimate Of Population Variance Between-Group Estimate Of Population Variance M1 M2 M3

17. Justification for F statistic

18. Calculating F

19. Example

21. Using the F Statistic Use a table for F(BDF, WDF) –And also α BDF = between-groups degrees of freedom = number of groups -1 WDF = within-groups degrees of freedom = Σ df for all groups = N – number of groups

22. One-way ANOVA in SPSS

23. 23 Data Mean

24. 24 Analyze/Compare Means/One Way ANOVA…

25. SPSS Results… F(2,21)=9.442, p<.05

26. 26 Factorial Designs Two or more nominal independent variables, each with two or more levels, and a numeric dependent variable. Factorial ANOVA teases apart the contribution of each variable separately. For N IVs, aka “N-way” ANOVA

27. 27 Factorial Designs Adding a second independent variable to a single- factor design results in a FACTORIAL DESIGN Two components can be assessed –The MAIN EFFECT of each independent variable The separate effect of each independent variable Analogous to separate experiments involving those variables –The INTERACTION between independent variables When the effect of one independent variable changes over levels of a second Or– when the effect of one variable depends on the level of the other variable.

28. Example Wait Time Sign in Student Center vs. No Sign Satisfaction

29. Example of An Interaction - Student Center Sign – 2 Genders x 2 Sign Conditions F M No Sign

30. 30 Two-way ANOVA in SPSS

31. 31 Analyze/General Linear Model/Univariate

32. 32 Results

33. 33 Results

34. 34 Degrees of Freedom df for between-group variance estimates for main effects –Number of levels – 1 df for between-group variance estimates for interaction effect –Total num cells – df for both main effects – 1 –e.g. 2x2 => 4 – (1+1) – 1 = 1 df for within-group variance estimate –Sum of df for each cell = N – num cells Report: “F(bet-group, within-group)=F, Sig.”

35. Publication format N=24, 2x3=6 cells => df TrainingDays=2, df within-group variance=24-6=18 =>F(2,18)=7.20, p<.05

36. 36 Reporting rule IF you have a significant interaction THEN –If 2x2 study: do not report main effects, even if significant –Else: must look at patterns of means in cells to determine whether to report main effects or not.

37. Results? TrainingDays Trainer TrainingDays * Trainer Sig. 0.34 0.12 0.41 n.s.

38. Results? TrainingDays Trainer TrainingDays * Trainer Sig. 0.34 0.12 0.02 Significant interaction between TrainingDays And Trainer, F(2,22)=.584, p<.05

39. Results? TrainingDays Trainer TrainingDays * Trainer Sig. 0.34 0.02 0.41 Main effect of Trainer, F(1,22)=.001, p<.05

40. Results? TrainingDays Trainer TrainingDays * Trainer Sig. 0.04 0.12 0.01 Significant interaction between TrainingDays And Trainer, F(2,22)=.584, p<.05 Do not report TrainingDays as significant

41. Results? TrainingDays Trainer TrainingDays * Trainer Sig. 0.04 0.02 0.41 Main effects for both TrainingDays, F(2,22)=7.20, p<.05, and Trainer, F(1,22)=.001, p<.05

42. “Factorial Design” Not all cells in your design need to be tested –But if they are, it is a “full factorial design”, and you do a “full factorial ANOVA” Real-Time Retrospective Agent Text   X

43. 43 Higher-Order Factorial Designs More than two independent variables are included in a higher-order factorial design –As factors are added, the complexity of the experimental design increases The number of possible main effects and interactions increases The number of subjects required increases The volume of materials and amount of time needed to complete the experiment increases