A Gentle Introduction to Stata, Fifth Edition / Edition 5

by Alan C. Acock

Overview

Alan C. Acock's A Gentle Introduction to Stata, Fifth Edition,
is aimed at new Stata users who want to become proficient in Stata.
After reading this introductory text, new users will be able not only
to use Stata well but also to learn new aspects of Stata.

Acock assumes that the user is not familiar with any statistical
software. This assumption of a blank slate is central to the structure
and contents of the book. Acock starts with the basics; for example,
the portion of the book that deals with data management begins with a
careful and detailed example of turning survey data on paper into a
Stata-ready dataset on the computer. When explaining how to go about
basic exploratory statistical procedures, Acock includes notes that
will help the reader develop good work habits. This mixture of
explaining good Stata habits and good statistical habits continues
throughout the book.

Acock is quite careful to teach the reader all aspects of using Stata.
He covers data management, good work habits (including the use of
basic do-files), basic exploratory statistics (including graphical
displays), and analyses using the standard array of basic statistical
tools (correlation, linear and logistic regression, and parametric and
nonparametric tests of location and dispersion). He also successfully
introduces some more advanced topics such as multiple imputation and
structural equation modeling in a very approachable manner. Acock
teaches Stata commands by using the menus and dialog boxes while still
stressing the value of do-files. In this way, he ensures that all
types of users can build good work habits. Each chapter has exercises
that the motivated reader can use to reinforce the material.

The tone of the book is friendly and conversational without ever being
glib or condescending. Important asides and notes about terminology
are set off in boxes, which makes the text easy to read without any
convoluted twists or forward-referencing. Rather than splitting topics
by their Stata implementation, Acock arranges the topics as they would
appear in a basic statistics textbook; graphics and postestimation are
woven into the material in a natural fashion. Real datasets, such as
the General Social Surveys from 2002 and 2006, are used
throughout the book.

The focus of the book is especially helpful for those in the
behavioral and social sciences because the presentation of basic
statistical modeling is supplemented with discussions of effect sizes
and standardized coefficients. Various selection criteria, such as
semipartial correlations, are discussed for model selection. Acock
also covers a variety of commands available for evaluating reliability
and validity of measurements.

The fifth edition of the book includes two new chapters that cover
multilevel modeling and item response theory (IRT) models. The
multilevel modeling chapter demonstrates how to fit linear multilevel
models using the mixed command. Acock discusses models with
both random intercepts and random coefficients, and he provides a
variety of examples that apply these models to longitudinal data. The
IRT chapter introduces the use of IRT models for evaluating a set of
items designed to measure a specific trait such as an attitude, value,
or a belief. Acock shows how to use the irt suite of commands,
which are new in Stata 14, to fit IRT models and to graph the results.
In addition, he presents a measure of reliability that can be computed
when using IRT.

Product Details

ISBN-13:	9781597181853
Publisher:	Stata Press
Publication date:	04/19/2016
Edition description:	Revised
Pages:	546
Product dimensions:	7.30(w) x 9.30(h) x 1.40(d)

Table of Contents

Getting started

Conventions

Introduction

The Stata screen

Using an existing dataset

An example of a short Stata session

Video aids to learning Stata

Summary

Exercises

Entering data

Creating a dataset

An example questionnaire

Developing a coding system

Entering data using the Data Editor

Value labels

The Variables Manager

The Data Editor (Browse) view

Saving your dataset

Checking the data

Summary

Exercises

Preparing data for analysis

Introduction

Planning your work

Creating value labels

Reverse-code variables

Creating and modifying variables

Creating scales

Save some of your data

Summary

Exercises

Working with commands, do-files, and results

Introduction

How Stata commands are constructed

Creating a do-file

Copying your results to a word processor

Logging your command file

Summary

Exercises

Descriptive statistics and graphs for one variable

Descriptive statistics and graphs

Where is the center of a distribution?

How dispersed is the distribution?

Statistics and graphs—unordered categories

Statistics and graphs—ordered categories and variables

Statistics and graphs—quantitative variables

Summary

Exercises

Statistics and graphs for two categorical variables

Relationship between categorical variables

Cross-tabulation

Chi-squared test

Degrees of freedom

Probability tables

Percentages and measures of association

Odds ratios when dependent variable has two categories

Ordered categorical variables

Interactive tables

Tables—linking categorical and quantitative variables

Power analysis when using a chi-squared test of significance

Summary

Exercises

Tests for one or two means

Introduction to tests for one or two means

Randomization

Random sampling

Hypotheses

One-sample test of a proportion

Two-sample test of a proportion

One-sample test of means

Two-sample test of group means

Testing for unequal variances

Repeated-measures t test

Power analysis

Nonparametric alternatives

Mann—Whitney two-sample rank-sum test

Nonparametric alternative: Median test

Video tutorial related to this chapter

Summary

Exercises

Bivariate correlation and regression

Introduction to bivariate correlation and regression

Scattergrams

Plotting the regression line

An alternative to producing a scattergram, binscatter

Correlation

Regression

Spearman's rho: Rank-order correlation for ordinal data

Power analysis with correlation

Summary

Exercises

Analysis of variance

The logic of one-way analysis of variance

ANOVA example

ANOVA example with nonexperimental data

Power analysis for one-way ANOVA

A nonparametric alternative to ANOVA

Analysis of covariance

Two-way ANOVA

Repeated-measures design

Intraclass correlation—measuring agreement

Power analysis with ANOVA

Power analysis for one-way ANOVA

Power analysis for two-way ANOVA

Power analysis for repeated-measures ANOVA

Summary of power analysis for ANOVA

Summary

Exercises

Multiple regression

Introduction to multiple regression

What is multiple regression?

The basic multiple regression command

Increment in R-squared: Semipartial correlations

Is the dependent variable normally distributed?

Are the residuals normally distributed?

Regression diagnostic statistics

Outliers and influential cases

Influential observations: DFbeta

Combinations of variables may cause problems

Weighted data

Categorical predictors and hierarchical regression

A shortcut for working with a categorical variable

Fundamentals of interaction

Nonlinear relations

Fitting a quadratic model

Centering when using a quadratic term

Do we need to add a quadratic component?

Power analysis in multiple regression

Summary

Exercises

Logistic regression

Introduction to logistic regression

An example

What is an odds ratio and a logit?

The odds ratio

The logit transformation

Data used in the rest of the chapter

Logistic regression

Hypothesis testing

Testing individual coefficients

Testing sets of coefficients

More on interpreting results from logistic regression

Nested logistic regressions

Power analysis when doing logistic regression

Next steps for using logistic regression and its extensions

Summary

Exercises

Measurement, reliability, and validity

Overview of reliability and validity

Constructing a scale

Generating a mean score for each person

Reliability

Stability and test-retest reliability

Equivalence

Split-half and alpha reliabilit—-internal consistency

Kuder—Richardson reliability for dichotomous items

Rater agreement—kappa (K)

Validity

Expert judgment

Criterion-related validity

Construct validity

Factor analysis

PCF analysis

Orthogonal rotation: Varimax

Oblique rotation: Promax

But we wanted one scale, not four scales

Scoring our variable

Summary

Exercises

Working with missing values—multiple imputation

The nature of the problem

Multiple imputation and its assumptions about the mechanism for missingness

What variables do we include when doing imputations?

Multiple imputation

A detailed example

Preliminary analysis

Setup and multiple-imputation stage

The analysis stage

For those who want an R and standardized βs

When impossible values are imputed

Summary

Exercises

The sem and gsem commands

Linear regression using sem

Using the SEM Builder to fit a basic regression model

A quick way to draw a regression model and a fresh start

Using sem without the SEM Builder

The gsem command for logistic regression

Fitting the model using the logit command

Fitting the model using the gsem command

Path analysis and mediation

Conclusions and what is next for the sem command

Exercises

An introduction to multilevel analysis

Questions and data for groups of individuals

Questions and data for a longitudinal multilevel application

Fixed-effects regression models

Random-effects regression models

An applied example

Research questions

Reshaping data to do multilevel analysis

A quick visualization of our data

Random-intercept model

Random intercept—linear model

Random-intercept model—quadratic term

Treating time as a categorical variable

Random-coefficients model

Including a time-invariant covariate

Summary

Exercises

Item response theory (IRT)

How are IRT measures of variables different from summated scales?

Overview of three IRT models for dichotomous items

The one-parameter logistic (PL) model

The two-parameter logistic (PL) model

The three-parameter logistic (PL) model

Fitting the PL model using Stata

The estimation

How important is each of the items?

An overall evaluation of our scale

Estimating the latent score

Fitting a PL IRT model

Fitting the PL model

The graded response model—IRT for Likert-type items

The data

Fitting our graded response model

Estimating a person's score

Reliability of the fitted IRT model

Using the Stata menu system

Extensions of IRT

Exercises

What's next?

Introduction to the appendix

Resources

Web resources

Books about Stata

Short courses

Acquiring data

Learning from the postestimation methods

Summary