As the SAS[copyright] programming language evolves, this edition follows suit with coverage of this combination statistical package, database management system, and high-level programming language. It is suitable for both students and applied researchers. It also uses examples from business, medicine, education, psychology, and other disciplines.

Note: All chapters open with an Introduction. Chapter 1: A SAS TutorialComputing With SAS: An Illustrative Example. Enhancing the Program. SAS Procedures. Overview of the SAS DATA Step. Syntax of SAS Procedures. Comment Statements. References. Chapter 2: Describing DataDescribing Data. More Descriptive Statistics. Histograms, QQ Plots, and Probability Plots. Descriptive Statistics Broken Down by Subgroups. Frequency Distributions. Bar Graphs. Plotting Data. Chapter 3: Analyzing Categorical DataQuestionnaire Design and Analysis. Adding Variable Labels. Adding "Value Labels" (Formats). Recoding Data. Using a Format to Recode a Variable. Two-way Frequency Tables. A Short-cut Way to Request Multiple Tables. Computing Chi-square from Frequency Counts. A Useful Program for Multiple Chi-square Tables. A Useful Macro for Computing Chi-square from Frequency Counts. McNemar's Test for Paired Data. Computing the Kappa Statistics (Coefficient of Agreement). Odds Ratios. Relative Risk. Chi-square Test for Trend. Mantel-Haenszel Chi-square for Stratified Tables and Meta Analysis. "Check All That Apply" Questions. Chapter 4: Working with Date and Longitudinal DataProcessing Date Variables. Working with Two-digit Year Values (The Y2K Problem. Longitudinal Data.Selecting the First or Last Visit per Patient. Computing Differences between Observations in a Longitudinal Data Set. Computing the Difference between the First and Last Observation for each Subject. Computing Frequencies on Longitudinal Data Sets. Creating Summary Data Sets with PROC MEANS or PROC SUMMARY. Outputting Statistics Other Than Means. Chapter 5: Correlation and Simple RegressionCorrelation. Significance of a Correlation Coefficient. How to Interpret a Correlation Coefficient. Partial Correlations. Linear Regression. Partitioning the Total Sum of Squares. Producing a Scatter Plot and the Regression Line. Adding a Quadratic Term to the Regression Equation. Transforming Data. Chapter 6: T-tests and Nonparametric ComparisonsT-test: Testing Differences between Two Means. Random Assignment of Subjects. Two Independent Samples: Distribution Free Tests. One-tailed versus Two-tailed Tests. Paired T-tests (Related Samples). Chapter 7: Analysis of VarianceOne-way Analysis of Variance. Computing Contrasts. Analysis of Variance: Two Independent Variables. Interpreting Significant Interactions. N-way Factorial Designs. Unbalanced Designs: PROC GLM. Analysis of Covariance. Chapter 8: Repeated Measures DesignsOne-factor Experiments. Using the REPEATED Statement of PROC ANOVA. Using PROC MIXED to Compute a Mixed (random effects) Model. Two-factor Experiments with a Repeated Measure on One Factor. Two-factor Experiments with Repeated Measures on Both Factors. Three-factor Experiments with a Repeated Measure on the Last Factor. Three-factor Experiments with Repeated Measures on Two Factors. Chapter 9: Multiple Regression AnalysisDesigned Regression. Nonexperimental Regression. Stepwise and Other Variable Selection Methods. Creating and Using Dummy Variables. Using the Variance Inflation Factor to Look for Multicollinearity. Logistic Regression. Automatic Creation of Dummy Variables with PROC LOGISTIC. Chapter 10: Factor AnalysisTypes of Factor Analysis. Principal Components Analysis. Oblique Rotations. Using Communalities Other Than One. How to Reverse Item Scores. Chapter 11: Psychometrics Using SAS Software to Score a Test. Generalizing the Program for a Variable Number of Questions. Creating a Better Looking Table Using PROC TABULATE. A Complete Test Scoring and Item Analysis Program. Test Reliability. Interrater Reliability. Chapter 12: The SAS INPUT StatementList Input: Data values separated by spaces. Reading Comma-delimited Data. Using INFORMATS with List Input. Column Input. Pointers and Informats. Reading More Than One Line per Subject. Changing the Order and Reading a Column More Than Once. Informat Lists. "Holding the Line"-Single- and Double-trailing @'s. Suppressing the Error Messages for Invalid Data.Reading "Unstructured" Data. Chapter 13: External Files: Reading and Writing Raw and System FilesData in the Program Itself. Reading Data from An External Text File (ASCII or EBCDIC). INFILE Options. Reading Data from Multiple Files (using wildcards). Writing ASCII or Raw Data to An External File. Writing CSV (comma separated variables) Files Using SAS. Creating a Permanent SAS Data Set. Reading Permanent SAS Data Sets. How to Determine the Contents of a SAS Data Set. Permanent SAS Data Sets with Formats.Working with Large Data Sets. Chapter 14: Data Set Subsetting, Concatenating, Merging, and UpdatingSubsetting. Combining Similar Data from Multiple SAS Data Sets. Combining Different Data from Multiple SAS Data Sets. "Table Look Up". Updating a Master Data Set from An Update Data Set. Chapter 15: Working with ArraysSubstituting One Value for Another for a Series of Variables. Extending Example 1 to Convert All Numeric Values of 999 to Missing. Converting the Value of N/A (Not Applicable) to a Character Missing Value. Converting Heights and Weights from English to Metric Units. Temporary Arrays. Using a Temporary Array to Score a Test. Specifying Array Bounds. Temporary Arrays and Array Bounds. Implicitly Subscripted Arrays. Chapter 16: Restructuring SAS Data Sets Using ArraysCreating a New Data Set with Several Observations per Subject from a Data Set with One Observation per Subject. Another Example of Creating Multiple Observations from a Single Observation. Going from One Observation per Subject to Many Observations per Subject Using Multi-dimensional Arrays. Creating a Data Set with One Observation per Subject from a Data Set with Multiple Observations per Subject. Creating a Data Set with One Observation per Subject from a Data Set with Multiple Observations per Subject Using a Multi-dimensional Array. Chapter 17: A Review of SAS FunctionsPart I. Functions Other Than Character FunctionsArithmetic and Mathematical Functions. Random Number Functions. Time and Date Functions. The INPUT and PUT Functions: Converting Numerics to Character, and Character to Numeric Variables. The LAG and DIF Functions. Chapter 18: A Review of SAS FunctionsPart II. Character FunctionsHow Lengths of Character Variables are Set in a SAS DATA Step. Working with Blanks. How to Remove Characters from a String. Character Data VerificationSubstring Example. Using the SUBSTR Function on the Left-Hand Side of the Equals Sign. Doing the Previous Example Another Way. Unpacking a String. Parsing a String. Locating the Position of One String Within Another String. Changing Lower Case to Upper Case and Vice Versa. Substituting One Character for Another. Substituting One Word for Another in a StringConcatenating (Joining) Strings. Soundex Conversion.Spelling Distance: The SPEDIS Function. Chapter 19: Selected Programming ExamplesExpressing Data Values as a Percentage of the Grand Mean. Expressing a Value as a Percentage of a Group Mean. Plotting Means with Error Bars. Using a Macro Variable to Save Coding Time. Computing Relative Frequencies. Computing Combined Frequencies on Different Variables. Computing a Moving Average. Sorting Within an Observation. Computing Coefficient Alpha (or KR-20) in a DATA Step. Chapter 20: Syntax Examples PROC ANOVA. PROC APPEND. PROC CHART. PROC CONTENTS. PROC CORR. PROC DATASETS. PROC FACTOR. PROC FORMAT. PROC FREQ. PROC GCHART. PROC GLM. PROC GPLOT. PROC LOGISTIC. PROC MEANS. PROC NPAR1WAY. PROC PLOT.PROC PRINT. PROC RANK. PROC REG. PROC SORT. PROC TTEST. PROC UNIVARIATE.