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· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9781597181853
· 쪽수 : 546쪽
· 출판일 : 2016-04-19
목차
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
