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Planned Contrast: Execution (Conceptual) 1. Must predict pattern of interaction before gathering data. Predict that Democratic women will be most opposed.

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Presentation on theme: "Planned Contrast: Execution (Conceptual) 1. Must predict pattern of interaction before gathering data. Predict that Democratic women will be most opposed."— Presentation transcript:

1 Planned Contrast: Execution (Conceptual) 1. Must predict pattern of interaction before gathering data. Predict that Democratic women will be most opposed to gun instruction in school, compared to Democratic men, Republican men, and Republican women.

2 Post Hoc Tests Do female democrats differ from other groups? 1= Male/Republican5.00 2=Male/Democrat4.50 3=Female/Republican4.75 4=Female/Democrat2.75 Conduct six t tests? NO. Why not?Will capitalizes on chance. Solution: Post hoc tests of multiple comparisons. Post hoc tests consider the inflated likelihood of Type I error Kent's favorite—Tukey test of multiple comparisons, which is the most generous. NOTE: Post hoc tests can be done on any multiple set of means, not only on planned contrasts.

3 Conducting Post Hoc Tests 1. Recode data from multiple factors into single factor, as per planned contrast. 2. Run oneway ANOVA statistic 3. Select "posthoc tests" option. ONEWAY gunctrl BY genparty /CONTRAST= /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC = TUKEY ALPHA(.05). Selected post- hoc test Note: Not necessary to conduct planned contrast to conduct post-hoc test

4 Post hoc Tests, Page 1

5 Post Hoc Tests, Page 2

6 Data Management Issues Setting up data file Checking accuracy of data Disposition of data Why obsess on these details? Murphy's Law If something can go wrong, it will go wrong, and at the worst possible time. Errars Happin!

7 Creating a Coding Master 1. Get survey copy 2. Assign variable names 3. Assign variable values 4. Assign missing values 5. Proof master for accuracy 6. Make spare copy, keep in file drawer

8 Coding Master variable names variable values Note: Var. values not needed for scales

9 Cleaning Data Set 1. Exercise in delay of gratification 2. Purpose: Reduce random error 3. Improve power of inferential stats.

10 Complete Data Set Note: Are any cases missing data?

11 Are any “Minimums” too low? Are any “Maximums” too high? Do N s indicate missing data? Do SDs indicate extreme outliers? Checking Descriptives

12 Do variables correlate in the expected manner? Checking Correlations Between Variables

13 Using Cross Tabs to Check for Missing or Erroneous Data Entry Case A: Expect equal cell sizes Gender OldestYoungestOnly Child Males10 20 Females TOTAL Case B: Impossible outcome Number of Siblings OldestYoungestOnly Child None 43 6 One340 More than one34 2 TOTAL10 8

14 Storing Data Raw Data 1. Hold raw data in secure place 2. File raw data by ID # 3. Hold raw date for at least 5 years post publication, per APA Automated Data 1. One pristine source, one working file, one syntax file 2. Back up, Back up, Back up `3. Use external hard drive as back-up for PC

15 File Raw Data Records By ID Number

16 COMMENT SYNTAX FILE GUN CONTROL STUDY SPRING 2007 COMMENT DATA MANAGEMENT IF (gender = 1 & party = 1) genparty = 1. EXECUTE. IF (gender = 1 & party = 2) genparty = 2. EXECUTE. IF (gender = 2 & party = 1) genparty = 3. EXECUTE. IF (gender = 2 & party = 2) genparty = 4. EXECUTE. COMMENT ANALYSES UNIANOVA gunctrl BY gender party /METHOD = SSTYPE(3) /INTERCEPT = INCLUDE /PRINT = DESCRIPTIVE /CRITERIA = ALPHA(.05) /DESIGN = gender party gender*party. ONEWAY gunctrl BY genparty /CONTRAST= /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC = TUKEY ALPHA(.05). Save Syntax File!!!

17 Research Project Notebook Purpose : All-in-one handy summary of research project Content: 1. Administrative (timeline, list of staff, etc.) 2. Overview of Research 3. Experiment Materials * Surveys * Consents, debriefings * Manipulations * Procedures summary/instructions 4. IRB materials * Application * Approval 5. Data * Coding forms * Syntax file * Primary outcomes

18 Correlation Class 20

19 Today's Class Covers What and why of measures of association Covariation Pearson's r correlation coefficient Partial Correlation Comparing two correlations Non-Parametric correlations

20 Do Variables Relate to One Another? Is teacher pay related to performance? Is exercise related to illness? Is CO 2 related to global warming? Is platoon cohesion related to PTSD? Is TV viewing related to shoe size? Positive Negative Positive Negative Zero

21 Exercise and Illness 1. How many times a week do you exercise? _____ 2. How many days have you missed school this term due to illness? _____ 3. How many hours of sleep do you get each night? ____

22 Interpreting Correlations [C] Sleep Hours [A] Exercise [B] Illness A --> B Exercise reduces illness B --> C Illness reduces exercise C --> (A & B) Third variable (sleep) affects exercise and illness simultaneously

23 Exercise and Illness Data (fabricated) subjectexerise.dayssleep.hourssick.days

24 Description of Data

25 Scatterplot: Exercise and Days Sick Regression Line

26 Co-variation exercise days sick days Subject Number # Days

27 Covariation Formula cov (x,y) = Σ ( X i – X ) ( Y i – Y ) N – 1 cov (exercise, sickness) = (-3.32) + (0.40) + (-0.46) …+ (-1.02) 14-1 = -23/13 = -1.77

28 Problem with Covariation "To all health and exercise researchers: Please send us your exercise and health covariations." Team 1: exercise = days per week exercise, covariation = Team 2: exercise = hours per week exercise, covariation = What if we all we have are the covariations? How do we compare them? How would we know, in this case, whether Team 1 showed a larger, smaller, or equal covariation than did Team 2?

29 Pearson Correlation Coefficient r = cov xy sxsysxsy r = Σ ( X i – X ) ( Y i – Y ) (N – 1) sxsysxsy Pearson r (“rho”): to

30 Using R 2 to Interpret Correlation R 2 = r 2 = amount of variance shared between correlated variables. Correl: exercise.hours, sick.days =.613 R 2 = =.376 “About 38% of variability in sick days is explained by variability in exercise hours.”

31 Variation in Sick Days Explained by Exercise Hours Exercise hours =.376% Number of Sick Days Last Term R 2 = =.376

32 Partial Correlation Issue: How much does Variable 1 explain Variable 2, AFTER accounting for the influence of Variable 3? Sickness and Exercise Study: How much does exercise explain days sick, AFTER accounting for the influence of nightly hours of sleep? Partial Correlation answers this question.

33 Partial Correlation Sick Days Exercise Days Sleep Hours var. explained =.376 var. explained =.277 var. explained by exercise alone (.04) var. explained by sleep alone (.17) var. explained by exercise + sleep (.21)

34 Partial Correlations in SPSS PARTIAL CORR /VARIABLES= sleep.hours sick.days by exercise.days /SIGNIFICANCE=TWOTAIL /MISSING=LISTWISE. PARTIAL CORR /VARIABLES= sleep.hours exercise.days by sick.days /SIGNIFICANCE=TWOTAIL /MISSING=LISTWISE.

35 Non-Parametric Correlations Assumptions of Correlations 1. Normally distributed data 2. Homogeneity of variance 3. Interval data (at least) What if Assumptions Not Met? Spearman's rho: Data are ordinal. Kendall's tau: Data are ordinal, but small sample, and many scores have the same ranking

36 Parametric Correlations Assumptions of Correlations 1. Normally distributed data 2. Homogeneity of variance 3. Interval data (at least) Var. AVar. B Watch TV 1 hr 2 hr 3 hr 4 hr 5 hr Eat Fast Food 1 day 2 day 3 day 4 day 5 day

37 Non-Parametric Correlations Var. AVar. B Watch TV Never Daily Weekly Monthly Yearly Eat Fast Food Never Daily Weekends Holidays Leap Years What if Assumptions Not Met? Spearman's rho: Data are ordinal. Kendall's tau: Data are ordinal, but small sample, and many scores have the same ranking.

38 Comparing Correlations Issue: How do we know if one correlation is different from another? Example: Is the nightly-sleep / sick days correl. different from the TV hours /sick days correl?

39 Difference Between Correlations Link to calculator for two ind. samples correlations Diff. Between 2 Independent correlations Diff. Between 2 dependent = correlations tdifference = (r xy - r zy ) √ (n-3) (1 + r xz ) 2 (1-r 2 xy -r 2 xz - r 2 zy + 2r xy r xz r zy ) z = zr 1 - zr 2 1 n n 2 - 3

40 Note: Assumes independent samples


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