SPSS Guide For MMI 409 by John Wong March 2012
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.
S P S S G u i d e F o r M M I 4 0 9 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... 4 2 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... 10 ANOVA (One Way ANOVA)... 10 ANOVA... 12 ANOVA with Eta... 12 ANOVA... 13 Repeated Measures ANOVA... 13 Correlation Analysis... 15 Scatter Diagram... 15 Correlation Analysis... 16 Pearson r correlation coefficient... 16 Correlation Analysis... 17 Linear Regression... 17 Non-parametric Test... 18 2 Dependent Samples... 18 Sign Test (Legacy Dialog)... 18 Non-Parametric Test... 19 2 Dependent Samples... 19 Wilcoxon Signed Rank (Legacy Dialog)... 19
S P S S G u i d e F o r M M I 4 0 9 P a g e ii Non-parametric Test... 20 2 Dependent Samples... 20 Wilcoxon Signed Rank (New Dialog)... 20 Non-Parametric Test... 21 2 Independent Samples... 21 Wilcoxon Rank Sum (Mann Whitney U)... 21 Non-Parametric Test... 22 k Independent Samples... 22 Kruskal-Wallis Test (Legacy Dialog)... 22 Non Parametric Test... 23 k Independent Samples... 23 Kruskal-Wallis Test (New Dialog)... 23 Non-Parametric Test... 24 Spearman Correlation (Correlation Between Variables)... 24
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 P a g e 2 Descriptives TRT Statistic Std. Error HDL 0 Mean 58.60 9.338 95% Confidence Interval for Mean Lower Bound 37.48 Upper Bound 79.72 5% Trimmed Mean 58.78 Median 64.00 Variance 872.044 Std. Deviation 29.530 Minimum 19 Maximum 95 Range 76 Interquartile Range 64 Skewness -.373.687 Kurtosis -1.587 1.334 1 Mean 55.20 7.422 95% Confidence Interval for Mean Lower Bound 38.41 Upper Bound 71.99 5% Trimmed Mean 55.44 Median 56.00 Variance 550.844 Std. Deviation 23.470 Minimum 18 Maximum 88 Range 70 Interquartile Range 47 Skewness -.220.687 Kurtosis -1.121 1.334 Percentiles Percentiles TRT 5 10 25 50 75 90 95 Weighted HDL 0 19.00 19.10 21.50 64.00 85.50 94.20 Average(Definition 1) 1 18.00 19.00 31.75 56.00 78.25 87.10 Tukey's Hinges HDL 0 22.00 64.00 85.00 1 33.00 56.00 78.00
S P S S G u i d e F o r M M I 4 0 9 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 20 48.85 9.466 2.117 One-Sample Test Test Value = 50 95% Confidence Interval of the Difference t df Sig. (2-tailed) Mean Difference Lower Upper AGE -.543 19.593-1.150-5.58 3.28
S P S S G u i d e F o r M M I 4 0 9 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 0 10 283.90 108.189 34.212 1 10 227.10 70.245 22.213 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 3.651.072 1.392 18.181 56.800 40.791-28.899 142.499 1.392 15.443.184 56.800 40.791-29.927 143.527
S P S S G u i d e F o r M M I 4 0 9 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 20.313.180 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 -8.050 24.752 5.535-19.634 3.534-1.454 19.162
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 P a g e 7 Chi-Square Test Frequencies Topic Issue Observed N Expected N Residual Drugs 52 48.0 4.0 Sex 38 30.0 8.0 Stress 21 30.0-9.0 Education 9 12.0-3.0 Total 120 Test Statistics Topic Issue Chi-Square 5.917 a df 3 Asymp. Sig..116 a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 12.0.
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 P a g e 9 Crosstabs Site * Treatment Crosstabulation Treatment Oral Diet and Exercise Hypoglycemics Insulin Total Site HMO Count 294 827 579 1700 Expected Count 292.0 774.3 633.8 1700.0 UTH Count 132 288 352 772 Expected Count 132.6 351.6 287.8 772.0 IPA Count 189 516 404 1109 Expected Count 190.5 505.1 413.4 1109.0 Total Count 615 1631 1335 3581 Expected Count 615.0 1631.0 1335.0 3581.0 Chi-Square Tests Value df Asymp. Sig. (2- sided) Pearson Chi-Square 34.629 a 4.000 Likelihood Ratio 34.498 4.000 Linear-by-Linear Association 1.744 1.187 N of Valid Cases 3581 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 132.58.
S P S S G u i d e F o r M M I 4 0 9 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 423.333 2 211.667 10.160.003 Within Groups 250.000 12 20.833 Total 673.333 14 Post Hoc Tests
S P S S G u i d e F o r M M I 4 0 9 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 1 2 7.0000 2.8868.076 -.701 14.701 3 13.0000 * 2.8868.002 5.299 20.701 2 1-7.0000 2.8868.076-14.701.701 3 6.0000 2.8868.136-1.701 13.701 3 1-13.0000 * 2.8868.002-20.701-5.299 2-6.0000 2.8868.136-13.701 1.701 Scheffe 1 2 7.0000 2.8868.091-1.047 15.047 3 13.0000 * 2.8868.003 4.953 21.047 2 1-7.0000 2.8868.091-15.047 1.047 3 6.0000 2.8868.158-2.047 14.047 3 1-13.0000 * 2.8868.003-21.047-4.953 2-6.0000 2.8868.158-14.047 2.047 *. 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 3 5 20.000 2 5 26.000 26.000 1 5 33.000 Sig..136.076 Scheffe a 3 5 20.000 2 5 26.000 26.000 1 5 33.000 Sig..158.091 Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = 5.000.
S P S S G u i d e F o r M M I 4 0 9 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 423.333 2 211.667 10.160.003 Within Groups 250.000 12 20.833 Total 673.333 14 Measures of Association Eta Eta Squared Time to Relief in Minutes * Drug Type.793.629
S P S S G u i d e F o r M M I 4 0 9 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 476.467 2 238.233 15.601.000 Greenhouse-Geisser 476.467 1.270 375.146 15.601.001 Huynh-Feldt 476.467 1.384 344.184 15.601.001
S P S S G u i d e F o r M M I 4 0 9 P a g e 14 (Treatment) Lower-bound 476.467 1.000 476.467 15.601.003 Error(Course) Within Course (Treatment) Sphericity Assumed 274.867 18 15.270 Greenhouse-Geisser 274.867 11.431 24.046 Huynh-Feldt 274.867 12.459 22.062 Lower-bound 274.867 9.000 30.541 Measure:Time Source Course Tests of Within-Subjects Contrasts Type III Sum of Squares df Mean Square F Sig. Course Linear 470.450 1 470.450 43.628.000 Quadratic 6.017 1 6.017.305.594 Error(Course) Linear 97.050 9 10.783 Quadratic 177.817 9 19.757 Measure:Time Transformed Variable:Average Tests of Between-Subjects Effects Source Type III Sum of Squares df Mean Square F Sig. Intercept 624386.133 1 624386.133 2526.137.000 Error Between Subjects 2224.533 9 247.170
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 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 1.860 ** Sig. (2-tailed).001 Sum of Squares and Cross-products 286.669 1036.950 Covariance Var(X)=31.852 Cov(X,Y)=115.217 N 10 10 Systolic Blood Pressure Pearson Correlation.860 ** 1 Sig. (2-tailed).001 Sum of Squares and Cross-products 1036.950 5072.500 Covariance 115.217 Var(Y)=563.611 N 10 10 **. Correlation is significant at the 0.01 level (2-tailed).
S P S S G u i d e F o r M M I 4 0 9 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 1.860 a.739.707 12.853 a. Predictors: (Constant), Body Mass Index Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 40.786 21.112 1.932.089 Body Mass Index 3.617.759.860 4.765.001 a. Dependent Variable: Systolic Blood Pressure
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 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 5.25 10.50 Positive Ranks 6 b 4.25 25.50 Ties 0 c Total 8 a. Postprogram < Baseline b. Postprogram > Baseline c. Postprogram = Baseline Test Statistics b Postprogram - Baseline Z -1.053 a Asymp. Sig. (2-tailed).292 a. Based on negative ranks. b. Wilcoxon Signed Ranks Test
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 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 0 5 9.70 15 5 17.40 40 5 11.20 50 5 3.70 Total 20 Test Statistics a,b Time Chi-Square 13.692 df 3 Asymp. Sig..003 a. Kruskal Wallis Test b. Grouping Variable: Treatment
S P S S G u i d e F o r M M I 4 0 9 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
S P S S G u i d e F o r M M I 4 0 9 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 1.000 -.454 Sig. (2-tailed)..139 N 12 12 Correlation Coefficient -.454 1.000 Sig. (2-tailed).139. N 12 12