Statistical power analysis with missing data: a structural equation modeling approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Routledge
2010
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIII, 369 S. graph. Darst. |
| ISBN: | 9780805863697 9780805863703 |
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| 245 | 1 | 0 | |a Statistical power analysis with missing data |b a structural equation modeling approach |c Adam Davey ; Jyoti Savla |
| 264 | 1 | |a New York [u.a.] |b Routledge |c 2010 | |
| 300 | |a XIII, 369 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
1
Introduction
...............................................................................................1
Overview and Aims
...................................................................................1
Statistical Power
..........................................................................................5
Testing Hypotheses
...............................................................................6
Choosing an Alternative Hypothesis...
...............................................7
Central and
Noncentral
Distributions
................................................ 7
Factors Important for Power
.................................................................9
Effect Sizes
............................................................................................10
Determining an Effect Size
.................................................................12
Point Estimates and Confidence Intervals
........................................14
Reasons to Estimate Statistical Power
...............................................17
Conclusions
................................................................................................17
Further Readings
......................................................................................18
Section I Fundamentals
2
The LISREL Model
.................................................................................21
Matrices and the LISREL Model
.............................................................22
Latent and Manifest Variables
................................................................24
Regression Coefficient Matrices
.........................................................25
Variance-Covariance Matrices
...........................................................25
Vectors of Means and Intercepts
........................................................26
Modelparameters
.....................................................................................27
Models and Matrices
...........................................................................30
Structure of a LISREL Program
.........................................................34
Reading and Interpreting LISREL Output
.......................................38
Evaluating Model Fit
...........................................................................41
Measures of Population Discrepancy
...............................................42
Incremental Fit Indices
...................................................................42
Absolute Fit Indices
.........................................................................43
Conclusions
................................................................................................43
Further Readings
......................................................................................43
3
Missing Data: An Overview....
.............................................................47
Why Worry About Missing Data?
.......................................................... 47
Types of Missing Data
..............................................................................48
vi
Contents
Missing
Completely at Random
.........................................................48
Missing at Random
..............................................................................49
Missing Not at Random
......................................................................49
Strategies for Dealing With Missing Data
.............................................51
Complete Case Methods
.....................................................................51
List-Wise Deletion
................................................................................51
List-Wise Deletion With Weighting
...................................................51
Available Case Methods
......................................................................52
Pair-Wise Deletion
...............................................................................52
Expectation Maximization Algorithm
..............................................52
Full Information Maximum Likelihood
...........................................53
Imputation Methods
.................................................................................54
Single Imputation
.................................................................................54
Multiple Imputation
.............................................................................55
Estimating Structural Equation Models With Incomplete Data
........56
Conclusions
................................................................................................64
Further Readings
......................................................................................65
Estimating Statistical Power With Complete Data
..........................67
Statistical Power in Structural Equation Modeling
.............................67
Power for Testing a Single Alternative Hypothesis
.............................68
Tests of Exact, Close, and Not Close Fit
............................................72
Tests of Exact, Close, and Not Close Fit Between Two Models
.....75
An Alternative Approach to Estimate Statistical Power
.....................76
Estimating Required Sample Size for Given Power
............................78
Conclusions
................................................................................................80
Further Readings
......................................................................................80
Section II Applications
5
Effects of Selection on Means, Variances, and Covariances
..........89
Defining the Population Model
..............................................................90
Defining the Selection Process
...........................................................92
An Example of the Effects of Selection
.............................................93
Selecting Data Into More Than Two Groups
........................................99
Conclusions
..............................................................................................101
Further Readings
....................................................................................102
6
Testing Covariances and Mean Differences With Missing
Data
..........................................................................................................103
Step
1:
Specifying the Population Model
............................................104
Step
2:
Specifying the Alternative Model
............................................105
Contents
vii
Step
3:
Generate Data Structure Implied by the Population
Model
....................................................................................................106
Step
4:
Decide on the Incomplete Data Model
....................................106
Step
5:
Apply the Incomplete Data Model to Population Data
........106
Step
6:
Estimate Population and Alternative Models
With Missing Data
..................................................................................109
Step
7:
Using the Results to Estimate Power or Required
Sample Size
...............................................................................................110
Conclusions
...............................................................................................117
Further Readings
.....................................................................................117
7
Testing Group Differences in Longitudinal Change
....................119
The Application
........................................................................................119
The Steps
..................................................................................................122
Step
1:
Selecting a Population Model
.............................................. 123
Step
2:
Selecting an Alternative Model
...........................................124
Step
3:
Generating Data According to the Population Model
.....125
Step
4:
Selecting a Missing Data Model
..........................................126
Step
5:
Applying the Missing Data Model to Population Data
... 127
Step
6:
Estimating Population and Alternative Models
With Incomplete Data
........................................................................128
Step
7:
Using the Results to Calculate Power or Required
Sample Size
.........................................................................................136
Conclusions
..............................................................................................140
Further Readings
.....................................................................................141
8
Effects of Following Up via Different Patterns When Data
Are Randomly or Systematically Missing
.......................................143
Background
..............................................................................................143
The Model
................................................................................................145
Design
..................................................................................................146
Procedures
...........................................................................................148
Evaluating Missing Data Patterns
...................................................152
Extensions to MAR Data
........................................................................158
Conclusions
..............................................................................................164
Further Readings
....................................................................................164
9
Using Monte Carlo Simulation Approaches to Study
Statistical Power With Missing Data....
............................................ 165
Planning and Implementing a Monte Carlo Study
............................165
Simulating Raw Data Under a Population Model
.............................. 170
Generating Normally Distributed Univariate Data...........
........... 171
Generating Nonnormally Distributed Univariate Data..
............. 172
viii Contents
Generating Normally Distributed Multivariate Data
....................174
Generating Nonnormally Distributed Multivariate Data
............177
Evaluating Convergence Rates for a Given Model
.............................178
Step
1:
Developing a Research Question
........................................180
Step
2:
Creating a Valid Model
.........................................................180
Step
3:
Selecting Experimental Conditions
....................................180
Step
4:
Selecting Values of Population Parameters
.......................181
Step
5:
Selecting an Appropriate Software Package
.....................182
Step
6:
Conducting the Simulations
................................................182
Step
7:
File Storage
..............................................................................182
Step
8:
Troubleshooting and Verification
........................................183
Step
9:
Summarizing the Results
.....................................................184
Complex Missing Data Patterns
...........................................................186
Conclusions
..............................................................................................190
Further Readings
....................................................................................191
Section III Extensions
10
Additional Issues With Missing Data in Structural
Equation Models
...................................................................................207
Effects of Missing Data on Model Fit
...................................................207
Using the NCP to Estimate Power for a Given Index
.........................211
Moderators of Loss of Statistical Power With Missing Data
.............211
Reliability
.............................................................................................211
Auxiliary Variables
............................................................................215
Conclusions
..............................................................................................218
Further Readings
....................................................................................219
11
Summary and Conclusions
.................................................................231
Wrapping Up
...........................................................................................231
Future Directions
....................................................................................232
Conclusions
..............................................................................................233
Further Readings
....................................................................................233
References
......................................................................................................235
Appendices
.....................................................................................................243
Index
................................................................................................................359
STATISTICS
There is very little in the field about the effect of missing data on statistical power. This is an important
area that needs to be addressed.
..
The writing style is...easy to read and engaging.
..
This book will.
..
be
used as a supplement in power analysis and
SEM
classes...and by...individuals that are currently
calculating power for research studies.
..
this book fills an important gap in the published literature.
—
Jay Maddock, University of Hawaii at Manoa
This text fills an enormous hole in the literature, and is sorely needed...the clear writing, examples.,
and syntax for a variety of programs are major strengths.
..
It will make a major and lasting contribution to
the field.
..
everything that I would want in a text for doctoral students is here.
— Jim Deal, North Dakota State University
...a valuable contribution to researchers conducting structural equation modeling research as well
as to researchers in general in helping to inform on basic issues of missing data...reader friendly and
accessible for all.
..
The quality of scholarship is high. It is evident the authors understand the material.
—
Debbie Hans-Vaughn, University of Central Florida
The book has the potential to add to the research literature...in terms of how to do statistical power
analysis with missing data.
..
I would definitely buy this book because of the programs and instructions
for power calculations for covariance structure models.
—
David P. MacKinnon, Arizona State University
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar
developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications.
This volume brings statistical power and incomplete data together under a common framework, in a way that
is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers
many practical questions such as:
•
how missing data affects the statistical power in a study
•
how much power is likely with different amounts and types of missing data
•
how to increase the power of a design in the presence of missing data, and
•
how to identify the most powerful design in the presence of missing data.
Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections
test one s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems.
Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics.
Numerous examples demonstrate the book s application to a variety of disciplines. Each issue is accompanied by
its potential strengths and shortcomings and examples using a variety of software packages
(SAS,
SPSS,
Stata,
LISREL, AMOS, and MPIus). Syntax is provided using a single software program to promote continuity but in each
case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and
links to student versions of software packages are found at www.psypress.com/davey. The worked examples
in Part
2
also provide results from a wider set of estimated models. These tables, and accompanying syntax, can
be used to estimate statistical power or required sample size for similar problems under a wide range of conditions.
Class-tested at Temple, Virginia Tech, and Miami University of Onio^ this brief text is an ideal supplement for
graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design,
structural equation modeling, power analysis, and research methods taught in departments of psychology, human
development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book s
applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications,
and Extensions are designed to take readers from first steps to mastery.
|
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| id | DE-604.BV035710622 |
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| indexdate | 2024-07-09T21:43:58Z |
| institution | BVB |
| isbn | 9780805863697 9780805863703 |
| language | English |
| lccn | 2009026347 |
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| spelling | Davey, Adam Verfasser aut Statistical power analysis with missing data a structural equation modeling approach Adam Davey ; Jyoti Savla New York [u.a.] Routledge 2010 XIII, 369 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Mathematisches Modell Sozialwissenschaften Statistik Social sciences Statistics Social sciences Statistical methods Social sciences Mathematical models Fehlende Daten (DE-588)4264715-0 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 s Fehlende Daten (DE-588)4264715-0 s b DE-604 Savla, Jyoti Sonstige oth Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017764373&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017764373&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Davey, Adam Statistical power analysis with missing data a structural equation modeling approach Mathematisches Modell Sozialwissenschaften Statistik Social sciences Statistics Social sciences Statistical methods Social sciences Mathematical models Fehlende Daten (DE-588)4264715-0 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| subject_GND | (DE-588)4264715-0 (DE-588)4116599-8 |
| title | Statistical power analysis with missing data a structural equation modeling approach |
| title_auth | Statistical power analysis with missing data a structural equation modeling approach |
| title_exact_search | Statistical power analysis with missing data a structural equation modeling approach |
| title_full | Statistical power analysis with missing data a structural equation modeling approach Adam Davey ; Jyoti Savla |
| title_fullStr | Statistical power analysis with missing data a structural equation modeling approach Adam Davey ; Jyoti Savla |
| title_full_unstemmed | Statistical power analysis with missing data a structural equation modeling approach Adam Davey ; Jyoti Savla |
| title_short | Statistical power analysis with missing data |
| title_sort | statistical power analysis with missing data a structural equation modeling approach |
| title_sub | a structural equation modeling approach |
| topic | Mathematisches Modell Sozialwissenschaften Statistik Social sciences Statistics Social sciences Statistical methods Social sciences Mathematical models Fehlende Daten (DE-588)4264715-0 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| topic_facet | Mathematisches Modell Sozialwissenschaften Statistik Social sciences Statistics Social sciences Statistical methods Social sciences Mathematical models Fehlende Daten Statistische Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017764373&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017764373&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT daveyadam statisticalpoweranalysiswithmissingdataastructuralequationmodelingapproach AT savlajyoti statisticalpoweranalysiswithmissingdataastructuralequationmodelingapproach |