SPSS Guide For MMI 409

Transcription

1 SPSS Guide For MMI 409 by John Wong March 2012

2 Preface Hopefully, this document can provide some guidance to MMI 409 students on how to use SPSS to solve many of the problems covered in the D Agostino book. In order to minimize the size of this document, the images are reduced to very small sizes. Readers should view this document electronically using maximum zoom. Good Luck. John Wong LBJOHN99 at Yahoo dot com D Agostino, R.B., Sullivan, L.M., & Beiser, A.S. (2006). Introductory applied biostatistics. Belmont, CA: Brooks/Cole, Cengage Learning.

3 S P S S G u i d e F o r M M I P a g e i Table of Contents Descriptive Statistics... 1 Explore, Percentile, 95% CI, Boxplot, Mean, Median, Histogram... 1 T Test... 3 One Sample T Test... 3 T Test Independent Samples T-test... 4 T Test... 5 Paired Samples T-test... 5 Chi-Square Test... 6 Goodness of Fit (Aggregated Data)... 6 Chi-Square Test... 8 Test of Independence (Aggregated Data)... 8 ANOVA ANOVA (One Way ANOVA) ANOVA ANOVA with Eta ANOVA Repeated Measures ANOVA Correlation Analysis Scatter Diagram Correlation Analysis Pearson r correlation coefficient Correlation Analysis Linear Regression Non-parametric Test Dependent Samples Sign Test (Legacy Dialog) Non-Parametric Test Dependent Samples Wilcoxon Signed Rank (Legacy Dialog)... 19

4 S P S S G u i d e F o r M M I P a g e ii Non-parametric Test Dependent Samples Wilcoxon Signed Rank (New Dialog) Non-Parametric Test Independent Samples Wilcoxon Rank Sum (Mann Whitney U) Non-Parametric Test k Independent Samples Kruskal-Wallis Test (Legacy Dialog) Non Parametric Test k Independent Samples Kruskal-Wallis Test (New Dialog) Non-Parametric Test Spearman Correlation (Correlation Between Variables)... 24

5 S P S S G u i d e F o r M M I P a g e 1 Descriptive Statistics Explore, Percentile, 95% CI, Boxplot, Mean, Median, Histogram 1) Setup Data 2) Descriptive Statistics - > Explore 3) Select Dependent List 4) Select Factor List (Independent Variable) 5) Click Statistics 6) Check Descriptives, Outliers, Percentiles 9) Check Normality Plots with tests 7) Click Continue 8) Click Plot 10) Click Continue 11) Click OK Histogram Output 1 Histogram Output 2 Boxplot Output

6 S P S S G u i d e F o r M M I P a g e 2 Descriptives TRT Statistic Std. Error HDL 0 Mean % Confidence Interval for Mean Lower Bound Upper Bound % Trimmed Mean Median Variance Std. Deviation Minimum 19 Maximum 95 Range 76 Interquartile Range 64 Skewness Kurtosis Mean % Confidence Interval for Mean Lower Bound Upper Bound % Trimmed Mean Median Variance Std. Deviation Minimum 18 Maximum 88 Range 70 Interquartile Range 47 Skewness Kurtosis Percentiles Percentiles TRT Weighted HDL Average(Definition 1) Tukey's Hinges HDL

7 S P S S G u i d e F o r M M I P a g e 3 T Test One Sample T Test 1) Setup Data 2) Compare Means -> One Sample T Test 3) Select Test Variable 4) Enter Test Value 5) Click OK One-Sample Statistics N Mean Std. Deviation Std. Error Mean AGE One-Sample Test Test Value = 50 95% Confidence Interval of the Difference t df Sig. (2-tailed) Mean Difference Lower Upper AGE

8 S P S S G u i d e F o r M M I P a g e 4 T Test 2 Independent Samples T-test 1) Setup Data 2) Compare Means -> Independent Samples T Test 3) Select Test Variable 4) Select Grouping Variable 5) Click Define Groups 6) Enter Groups range for Analysis 7) Click Continue 8) Click OK 9) From the Levene s test, with pvalue > 0.05 (variances are equal), use the t test for equal variances. Group Statistics TRT N Mean Std. Deviation Std. Error Mean LDL Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means Std. 95% Confidence Error Interval of the Sig. (2- Mean Differe Difference F Sig. t df tailed) Difference nce Lower Upper LDL Equal variances assumed Equal variances not assumed

9 S P S S G u i d e F o r M M I P a g e 5 T Test Paired Samples T-test 1) Setup Data 2) Compare Means -> Paired Samples T Test 3) Select Variables into Variable 1 and Variable 2 Paired Samples Correlations N Correlation Sig. Pair 1 Before & After Paired Samples Test Paired Differences Std. Std. Error 95% Confidence Interval of the Difference Sig. (2- Mean Deviation Mean Lower Upper t df tailed) Pair 1 Before - After

10 S P S S G u i d e F o r M M I P a g e 6 Chi-Square Test Goodness of Fit (Aggregated Data) 1) Enter Data 2) Define Labels (Optional) 3) Weight Cases to use the aggregated data 4) Check Weight Cases by 5) Select the aggregated variable 6) Non-parametric test -> Legacy Diaglo -> Chisquare 7) Select Test Variable 8) Define Expected Variable (Order must match p1,p2,p3,p4) 9) Click OK

11 S P S S G u i d e F o r M M I P a g e 7 Chi-Square Test Frequencies Topic Issue Observed N Expected N Residual Drugs Sex Stress Education Total 120 Test Statistics Topic Issue Chi-Square a df 3 Asymp. Sig..116 a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 12.0.

12 S P S S G u i d e F o r M M I P a g e 8 Chi-Square Test Test of Independence (Aggregated Data) 1) Setup Data 2) Define Labels For Variable 1 (Optional) 3) Define Labels for Variable 2 4) Weight Cases to use aggregated data 5) Click Weight cases by 6) Select measurement variable 8) Select Descriptive Statistics -> Crosstab 7) Click OK 9) Select Row and Column Variables 11) Check Chi-square 14) Check Expected 10) Click Statistics 12) Click Continue 13) Click Cells

13 S P S S G u i d e F o r M M I P a g e 9 Crosstabs Site * Treatment Crosstabulation Treatment Oral Diet and Exercise Hypoglycemics Insulin Total Site HMO Count Expected Count UTH Count Expected Count IPA Count Expected Count Total Count Expected Count Chi-Square Tests Value df Asymp. Sig. (2- sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases 3581 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is

14 S P S S G u i d e F o r M M I P a g e 10 ANOVA ANOVA (One Way ANOVA) 1) Setup Data 2) Compare Means -> One Way ANOVA 3) Select Dependent List 4) Select Factor 5) Click Post Hoc 6) Select Scheffe 7) Select Tukey 8) Click Continue Time To Relief in Minutes ANOVA Sum of Squares df Mean Square F Sig. Between Groups Within Groups Total Post Hoc Tests

15 S P S S G u i d e F o r M M I P a g e 11 Dependent Variable:Time to Relief in Minutes Multiple Comparisons Mean 95% Confidence Interval (I) Drug Type (J) Drug Type Difference (I-J) Std. Error Sig. Lower Bound Upper Bound Tukey HSD * * Scheffe * * *. The mean difference is significant at the 0.05 level. Homogeneous Subsets Time to Relief in Minutes Subset for alpha = 0.05 Drug Type N 1 2 Tukey HSD a Sig Scheffe a Sig Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size =

16 S P S S G u i d e F o r M M I P a g e 12 ANOVA ANOVA with Eta 1) Setup Data 2) Select Compare Means - > Means 3) Select Dependent List 4) Select Independent List 5) Click Options 6) Check Anova Table with Eta 7) Click Continue ANOVA Table Sum of Squares df Mean Square F Sig. Time to Relief in Minutes * Drug Type Between Groups Within Groups Total Measures of Association Eta Eta Squared Time to Relief in Minutes * Drug Type

17 S P S S G u i d e F o r M M I P a g e 13 ANOVA Repeated Measures ANOVA 1) Setup Data 2) General Linear Model -> Repeated Measures 3) Enter Within Subject Name 4) Type Number of Levels 5) Enter Measure Name 6) Click Define 7) Select Variables to Within Subjects 8) Click OK Measure:Time Source Course Between Course Tests of Within-Subjects Effects Type III Sum of Squares df Mean Square F Sig. Sphericity Assumed Greenhouse-Geisser Huynh-Feldt

18 S P S S G u i d e F o r M M I P a g e 14 (Treatment) Lower-bound Error(Course) Within Course (Treatment) Sphericity Assumed Greenhouse-Geisser Huynh-Feldt Lower-bound Measure:Time Source Course Tests of Within-Subjects Contrasts Type III Sum of Squares df Mean Square F Sig. Course Linear Quadratic Error(Course) Linear Quadratic Measure:Time Transformed Variable:Average Tests of Between-Subjects Effects Source Type III Sum of Squares df Mean Square F Sig. Intercept Error Between Subjects

19 S P S S G u i d e F o r M M I P a g e 15 Correlation Analysis Scatter Diagram 1) Enter Data 2) Legacy Dialogs -> Scatter Plot 3) Select Simple Scatter 4) Click Define 5) Select Dependent Variable into Y Axis 6) Select Independent Variable into X Axus 7) Click OK Scatter Plot Output

20 S P S S G u i d e F o r M M I P a g e 16 Correlation Analysis Pearson r correlation coefficient 1) Enter Data 2) Correlate -> Bivariate 3) Select Variables for correlation 4) Click Options 5) Select Cross product Deviations and covariances Correlations Body Mass Index Systolic Blood Pressure Body Mass Index Pearson Correlation ** Sig. (2-tailed).001 Sum of Squares and Cross-products Covariance Var(X)= Cov(X,Y)= N Systolic Blood Pressure Pearson Correlation.860 ** 1 Sig. (2-tailed).001 Sum of Squares and Cross-products Covariance Var(Y)= N **. Correlation is significant at the 0.01 level (2-tailed).

21 S P S S G u i d e F o r M M I P a g e 17 Correlation Analysis Linear Regression 1) Define Measure Variables as scale. Enter Data 2) Regression -> Linear 3) Select Dependent and Independent varaibles Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), Body Mass Index Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) Body Mass Index a. Dependent Variable: Systolic Blood Pressure

22 S P S S G u i d e F o r M M I P a g e 18 Non-parametric Test 2 Dependent Samples Sign Test (Legacy Dialog) 1) Define Measure Variables as Scale; Enter Data 2) Nonparametric tests -> Legacy Dialogs ->2 Related Samples 3) Select Before and After variables 4) Check Sign Sign Test Frequencies N Postprogram - Baseline Negative Differences a 2 Positive Differences b 6 Ties c 0 Total 8 a. Postprogram < Baseline b. Postprogram > Baseline c. Postprogram = Baseline Test Statistics b Exact Sig. (2-tailed) Postprogram - Baseline.289 a a. Binomial distribution used. b. Sign Test

23 S P S S G u i d e F o r M M I P a g e 19 Non-Parametric Test 2 Dependent Samples Wilcoxon Signed Rank (Legacy Dialog) 1) Define Measure Variables as Scale; Enter Data 2) Nonparametric tests -> Legacy Dialogs -> 2 Related Samples 3) Select Before and After Variables 4) Check Wilcoxon Wilcoxon Signed Ranks Test Ranks N Mean Rank Sum of Ranks Postprogram - Baseline Negative Ranks 2 a Positive Ranks 6 b Ties 0 c Total 8 a. Postprogram < Baseline b. Postprogram > Baseline c. Postprogram = Baseline Test Statistics b Postprogram - Baseline Z a Asymp. Sig. (2-tailed).292 a. Based on negative ranks. b. Wilcoxon Signed Ranks Test

24 S P S S G u i d e F o r M M I P a g e 20 Non-parametric Test 2 Dependent Samples Wilcoxon Signed Rank (New Dialog) 1) Define Measure Variables as Scale; Setup Data 2) Nonparametric tests -> Related Samples 3) Click Customize Analysis 4) Click Fields 5) Select Variables 6) Click Settings 7) Check Sign test 8) Check Wilcoxon 9) Click Run Hypothesis Output Double Click to drill down to analysis Sign Test Output Wilcoxon Test Output

25 S P S S G u i d e F o r M M I P a g e 21 Non-Parametric Test 2 Independent Samples Wilcoxon Rank Sum (Mann Whitney U) 1) Define Treatment type as Ordinal, Measure Variable as Scale, Setup Data 2) Nonparametric tests -> Independent Samples 3) Select Customize analysis 4) Select Fields tab 5) Select Measure variable in Test Fields 6) Select Treatment type variable in Groups 7) Select Settings Tab 8) Check Mann Witney U 9) Click Run Hypothesis Output (one tail test) Double Click to drill down to analysis Mann-Whitney U Output Use Exact Sig for one tail analysis Use Asymp Sig for 2 tail analysis

26 S P S S G u i d e F o r M M I P a g e 22 Non-Parametric Test k Independent Samples Kruskal-Wallis Test (Legacy Dialog) 1) Define Treatment type as Ordinal, Measure Variable as Scale, Setup Data 2) Nonparametric tests -> Legacy Dialog -> K Independent Samples 3) Select test Variable 4) Select Grouping Variable 5) Click Define range 6) Define range of grouping 7) Click Continue 8) Click OK Kruskal-Wallis Test Ranks Treatment N Mean Rank Time Total 20 Test Statistics a,b Time Chi-Square df 3 Asymp. Sig..003 a. Kruskal Wallis Test b. Grouping Variable: Treatment

27 S P S S G u i d e F o r M M I P a g e 23 Non Parametric Test k Independent Samples Kruskal-Wallis Test (New Dialog) 1) Define Treatment type as Ordinal, Measure Variable as Scale, Setup Data 2) Nonparametric tests -> Independent Samples 3) Select Customize analysis 4) Select Fields tab 5) Select Measure variable in Test Fields 6) Select Treatment type variable in Groups 7) Select Settings 8) Select Customer Tests 9) Select Kruskal Wallis 10) Click Run Hypothesis Output (one tail) Double Click to drill down to analysis At the bottom of output, click the down arrow, select pairwise comparison n) Pairwise Comparison. Use the Sig. column value for significance

28 S P S S G u i d e F o r M M I P a g e 24 Non-Parametric Test Spearman Correlation (Correlation Between Variables) 1) Define Treatment type as Ordinal, Measure Variable as Scale, Setup Data 2) Correlate -> Bivariate 3) Select Variables 4) Check Spearman Nonparametric Correlations [DataSet0] Correlations Number of Cigaretes Per Day Number of Hours of Exercise Per Day Spearman' s rho Number of Cigaretes Per Day Number of Hours of Exercise Per Day Correlation Coefficient Sig. (2-tailed)..139 N Correlation Coefficient Sig. (2-tailed).139. N 12 12