As the SAS© programming language continues to evolve, this guide follows suit with timely coverage of the combination statistical package, database management system, and high-level programming language. Using current examples from business, medicine, education, and psychology, Applied Statistics and the SAS Programming Language is an invaluable resource for applied researchers, giving them the capacity to perform statistical analyses with SAS without wading through pages of technical documentation. Includes the necessary SAS statements to run programs for most of the commonly used statistics, explanations of the computer output, interpretations of results, and examples of how to construct tables and write up results for reports and journal articles. Illustrated with SAS Graph™ output. Provides readers with ample models for developing programming skills. For anyone interested in learning more about applied statistics and the SAS programming language.
|Edition description:||New Edition|
|Product dimensions:||7.00(w) x 9.20(h) x 1.40(d)|
Table of Contents
Note: All chapters open with an Introduction.
Chapter 1: A SAS Tutorial
Computing With SAS: An Illustrative Example.
Enhancing the Program. SAS Procedures. Overview of
the SAS DATA Step. Syntax of SAS Procedures. Comment
Chapter 2: Describing Data
Describing 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 Data
Questionnaire Design and Analysis. Adding Variable Labels. Adding “Value Labels” (Formats). Recoding Data. Using a Format to Recode a Variable. Two-way Frequency Tables.
Chapter 4: Working with Date and Longitudinal Data
Processing 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 Regression
Correlation. 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.
Chapter 6: T-tests and Nonparametric Comparisons
T-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 Variance
One-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 Designs
One-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 Analysis
Designed 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 Analysis
Types 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 Statement
List Input: Data values separated by spaces.
Comma-delimited Data. Using INFORMATS with List
Input. Column Input. Pointers and Informats.
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:
Data 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 Updating
Subsetting. 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 Arrays
Substituting 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
Chapter 16: Restructuring SAS Data Sets Using Arrays
Creating 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 Functions
Arithmetic 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
Chapter 18: A Review of SAS Functions
Part II. Character Functions
How Lengths of Character Variables are Set in a SAS
DATA Step. Working with Blanks. How to Remove
Characters from a String. Character Data Verification
Substring 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 String
Concatenating (Joining) Strings. Soundex Conversion.
Spelling Distance: The SPEDIS Function.
Chapter 19: Selected Programming Examples
Expressing 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.