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SPSS Instructions for Introduction to Biostatistics

SPSS Instructions for Introduction to Biostatistics. Larry Winner Department of Statistics University of Florida. SPSS Windows. Data View Used to display data Columns represent variables Rows represent individual units or groups of units that share common values of variables Variable View

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SPSS Instructions for Introduction to Biostatistics

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  1. SPSS Instructions for Introduction to Biostatistics Larry Winner Department of Statistics University of Florida

  2. SPSS Windows • Data View • Used to display data • Columns represent variables • Rows represent individual units or groups of units that share common values of variables • Variable View • Used to display information on variables in dataset • TYPE: Allows for various styles of displaying • LABEL: Allows for longer description of variable name • VALUES: Allows for longer description of variable levels • MEASURE: Allows choice of measurement scale • Output View • Displays Results of analyses/graphs

  3. Data Entry Tips I • For variables that are not identifiers (such as name, county, school, etc), use numeric values for levels and use the VALUES option in VARIABLE VIEW to give their levels. Some procedures require numeric labels for levels. SPSS will print the VALUES on output • For large datasets, use a spreadsheet such as EXCEL which is more flexible for data entry, and import the file into SPSS • Give descriptive LABEL to variable names in the VARIABLE VIEW • Keep in mind that Columns are Variables, you don’t want multiple columns with the same variable

  4. Data Entry/Analysis Tips II • When re-analyzing previously published data, it is often possible to have only a few outcomes (especially with categorical data), with many individuals sharing the same outcomes (as in contingency tables) • For ease of data entry: • Create one line for each combination of factor levels • Create a new variable representing a COUNT of the number of individuals sharing this “outcome” • When analyzing data Click on: • DATA  WEIGHT CASES  WEIGHT CASES BY • Click on the variable representing COUNT • All subsequent analyses treat that outcome as if it occurred COUNT times

  5. Example 1.3 - Grapefruit Juice Study To import an EXCEL file, click on: FILE  OPEN  DATA then change FILES OF TYPE to EXCEL (.xls) To import a TEXT or DATA file, click on: FILE  OPEN  DATA then change FILES OF TYPE to TEXT (.txt) or DATA (.dat) You will be prompted through a series of dialog boxes to import dataset

  6. Descriptive Statistics-Numeric Data • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS DESCRIPTIVES • Choose any variables to be analyzed and place them in box on right • Options include:

  7. Example 1.3 - Grapefruit Juice Study

  8. Descriptive Statistics-General Data • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS FREQUENCIES • Choose any variables to be analyzed and place them in box on right • Options include (For Categorical Variables): • Frequency Tables • Pie Charts, Bar Charts • Options include (For Numeric Variables) • Frequency Tables (Useful for discrete data) • Measures of Central Tendency, Dispersion, Percentiles • Pie Charts, Histograms

  9. Example 1.4 - Smoking Status

  10. Vertical Bar Charts and Pie Charts • After Importing your dataset, and providing names to variables, click on: • GRAPHS  BAR…  SIMPLE (Summaries for Groups of Cases)  DEFINE • Bars Represent N of Cases (or % of Cases) • Put the variable of interest as the CATEGORY AXIS • GRAPHS  PIE… (Summaries for Groups of Cases)  DEFINE • Slices Represent N of Cases (or % of Cases) • Put the variable of interest as the DEFINE SLICES BY

  11. Example 1.5 - Antibiotic Study

  12. Histograms • After Importing your dataset, and providing names to variables, click on: • GRAPHS  HISTOGRAM • Select Variable to be plotted • Click on DISPLAY NORMAL CURVE if you want a normal curve superimposed (see Chapter 3).

  13. Example 1.6 - Drug Approval Times

  14. Side-by-Side Bar Charts • After Importing your dataset, and providing names to variables, click on: • GRAPHS  BAR…  Clustered (Summaries for Groups of Cases)  DEFINE • Bars Represent N of Cases (or % of Cases) • CATEGORY AXIS: Variable that represents groups to be compared (independent variable) • DEFINE CLUSTERS BY: Variable that represents outcomes of interest (dependent variable)

  15. Example 1.7 - Streptomycin Study

  16. Scatterplots • After Importing your dataset, and providing names to variables, click on: • GRAPHS  SCATTER  SIMPLE  DEFINE • For Y-AXIS, choose the Dependent (Response) Variable • For X-AXIS, choose the Independent (Explanatory) Variable

  17. Example 1.8 - Theophylline Clearance

  18. Scatterplots with 2 Independent Variables • After Importing your dataset, and providing names to variables, click on: • GRAPHS  SCATTER  SIMPLE  DEFINE • For Y-AXIS, choose the Dependent Variable • For X-AXIS, choose the Independent Variable with the most levels • For SET MARKERS BY, choose the Independent Variable with the fewest levels

  19. Example 1.8 - Theophylline Clearance

  20. Contingency Tables for Conditional Probabilities • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, select the variable you are conditioning on (Independent Variable) • For COLUMNS, select the variable you are finding the conditional probability of (Dependent Variable) • Click on CELLS • Click on ROW Percentages

  21. Example 1.10 - Alcohol & Mortality

  22. Independent Sample t-Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  COMPARE MEANS  INDEPENDENT SAMPLES T-TEST • For TEST VARIABLE, Select the dependent (response) variable(s) • For GROUPING VARIABLE, Select the independent variable. Then define the names of the 2 levels to be compared (this can be used even when the full dataset has more than 2 levels for independent variable).

  23. Example 3.5 - Levocabastine in Renal Patients

  24. Wilcoxon Rank-Sum/Mann-Whitney Tests • After Importing your dataset, and providing names to variables, click on: • ANALYZE  NONPARAMETRIC TESTS  2 INDEPENDENT SAMPLES • For TEST VARIABLE, Select the dependent (response) variable(s) • For GROUPING VARIABLE, Select the independent variable. Then define the names of the 2 levels to be compared (this can be used even when the full dataset has more than 2 levels for independent variable). • Click on MANN-WHITNEY U

  25. Example 3.6 - Levocabastine in Renal Patients

  26. Paired t-test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  COMPARE MEANS  PAIRED SAMPLES T-TEST • For PAIRED VARIABLES, Select the two dependent (response) variables (the analysis will be based on first variable minus second variable)

  27. Example 3.7 - Cmaxin SRC&IRC Codeine

  28. Wilcoxon Signed-Rank Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  NONPARAMETRIC TESTS  2 RELATED SAMPLES • For PAIRED VARIABLES, Select the two dependent (response) variables (be careful in determining which order the differences are being obtained, it will be clear on output) • Click on WILCOXON Option

  29. Example 3.8 - t1/2SSin SRC&IRC Codeine

  30. Relative Risks and Odds Ratios • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on RISK • Under CELLS, Click on OBSERVED and ROW PERCENTAGES • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.

  31. Example 5.1 - Pamidronate Study

  32. Example 5.2 - Lip Cancer

  33. Fisher’s Exact Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on CHI-SQUARE • Under CELLS, Click on OBSERVED and ROW PERCENTAGES • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.

  34. Example 5.5 - Antiseptic Experiment

  35. McNemar’s Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, Select the outcome for condition/time 1 • For COLUMNS, Select the outcome for condition/time 2 • Under STATISTICS, Click on MCNEMAR • Under CELLS, Click on OBSERVED and TOTAL PERCENTAGES • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.

  36. Example 5.6 - Report of Implant Leak P-value

  37. Cochran Mantel-Haenszel Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • For LAYERS, Select the Strata Variable • Under STATISTICS, Click on COCHRAN’S AND MANTEL-HAENSZEL STATISTICS • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.

  38. Example 5.7 Smoking/Death by Age

  39. Chi-Square Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on CHI-SQUARE • Under CELLS, Click on OBSERVED, EXPECTED, ROW PERCENTAGES, and ADJUSTED STANDARDIZED RESIDUALS • NOTE: Large ADJUSTED STANDARDIZED RESIDUALS (in absolute value) show which cells are inconsistent with null hypothesis of independence. A common rule of thumb is seeing which if any cells have values >3 in absolute value

  40. Example 5.8 - Marital Status & Cancer

  41. Goodman & Kruskal’s g / Kendall’s tb • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on GAMMA and KENDALL’S tb

  42. Examples 5.9,10 - Nicotine Patch/Exhaustion

  43. Kruskal-Wallis Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  NONPARAMETRIC TESTS  k INDEPENDENT SAMPLES • For TEST VARIABLE, Select Dependent Variable • For GROUPING VARIABLE, Select Independent Variable, then define range of levels of variable (Minimum and Maximum) • Click on KRUSKAL-WALLIS H

  44. Example 5.11 - Antibiotic Delivery Note: This statistic makes the adjustment for ties. See Hollander and Wolfe (1973), p. 140.

  45. Cohen’s k • After Importing your dataset, and providing names to variables, click on: • ANALYZE  DESCRIPTIVE STATISTICS  CROSSTABS • For ROWS, Select Rater 1 • For COLUMNS, Select Rater 2 • Under STATISTICS, Click on KAPPA • Under CELLS, Click on TOTAL Percentages to get the observed percentages in each cell (the first number under observed count in Table 5.17).

  46. Example 5.12 - Siskel & Ebert

  47. 1-Factor ANOVA - Independent Samples (Parallel Groups) • After Importing your dataset, and providing names to variables, click on: • ANALYZE  COMPARE MEANS  ONE-WAY ANOVA • For DEPENDENT LIST, Click on the Dependent Variable • For FACTOR, Click on the Independent Variable • To obtain Pairwise Comparisons of Treatment Means: • Click on POST HOC • Then TUKEY and BONFERRONI (among many other choices)

  48. Examples 6.1,2 - HIV Clinical Trial

  49. Kruskal-Wallis Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE  NONPARAMETRIC TESTS  k INDEPENDENT SAMPLES • For TEST VARIABLE, Select Dependent Variable • For GROUPING VARIABLE, Select Independent Variable, then define range of levels of variable (Minimum and Maximum) • Click on KRUSKAL-WALLIS H

  50. Example 6.2(a) - Thalidomide and HIV-1

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