Multilevel modeling of categorical outcomes using IBM SPSS [electronic resource] / Ronald H. Heck, Scott L. Thomas, Lynn N. Tabata.
"Preface Multilevel modeling has become a mainstream data analysis tool over the past decade, now figuring prominently in a range of social and behavioral science disciplines. Where it originally required specialized software, mainstream statistics packages such as IBM SPSS, SAS, and Stata all...
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| Format: | Electronic eBook |
| Language: | English |
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New York :
Routledge,
2012.
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| Series: | Quantitative methodology series.
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MARC
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| 100 | 1 | |a Heck, Ronald H. |0 http://id.loc.gov/authorities/names/n97122321 |1 http://isni.org/isni/0000000035185915. | |
| 245 | 1 | 0 | |a Multilevel modeling of categorical outcomes using IBM SPSS |h [electronic resource] / |c Ronald H. Heck, Scott L. Thomas, Lynn N. Tabata. |
| 260 | |a New York : |b Routledge, |c 2012. | ||
| 300 | |a 1 online resource (xvi, 439 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 490 | 1 | |a Quantitative methodology series. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Ch. 1 Introduction to Multilevel Models With Categorical Outcomes -- Introduction -- Our Intent -- Analysis of Multilevel Data Structures -- Scales of Measurement -- Methods of Categorical Data Analysis -- Sampling Distributions -- Link Functions -- Developing a General Multilevel Modeling Strategy -- Determining the Probability Distribution and Link Function -- Developing a Null (or No Predictors) Model -- Selecting the Covariance Structure -- Analyzing a Level-1 Model With Fixed Predictors -- Adding the Level-2 Explanatory Variables -- Examining Whether a Particular Slope Coefficient Varies Between Groups -- Covariance Structures -- Adding Cross-Level Interactions to Explain Variation in the Slope -- Selecting Level-1 and Level-2 Covariance Structures -- Model Estimation and Other Typical Multilevel Modeling Issues -- Determining How Well the Model Fits -- Syntax Versus IBM SPSS Menu Command Formulation -- Sample Size -- Power -- Missing Data. | |
| 505 | 8 | |a Design Effects, Sample Weights, and the Complex Samples Routine in IBM SPSS -- An Example -- Differences Between Multilevel Software Programs -- Summary -- ch. 2 Preparing and Examining the Data for Multilevel Analyses -- Introduction -- Data Requirements -- File Layout -- Getting Familiar With Basic IBM SPSS Data Commands -- RECODE: Creating a New Variable Through Recoding -- COMPUTE: Creating a New Variable That Is a Function of Some Other Variable -- MATCH FILES: Combining Data From Separate IBM SPSS Files -- AGGREGATE: Collapsing Data Within Level-2 Units -- VARSTOCASES: Vertical Versus Horizontal Data Structures -- Using "Rank" to Recode the Level-1 or Level-2 Data for Nested Models -- Creating an Identifier Variable -- Creating an Individual-Level Identifier Using COMPUTE -- Creating a Group-Level Identifier Using Rank Cases -- Creating a Within-Group-Level Identifier Using Rank Cases -- Centering -- Grand-Mean Centering -- Group-Mean Centering. | |
| 505 | 8 | |a Checking the Data -- A Note About Model Building -- Summary -- ch. 3 Specification of Generalized Linear Models -- Introduction -- Describing Outcomes -- Some Differences in Describing a Continuous or Categorical Outcome -- Measurement Properties of Outcome Variables -- Explanatory Models for Categorical Outcomes -- Components for Generalized Linear Model -- Outcome Probability Distributions and Link Functions -- Continuous Scale Outcome -- Positive Scale Outcome -- Dichotomous Outcome or Proportion -- Nominal Outcome -- Ordinal Outcome -- Count Outcome -- Negative Binomial Distribution for Count Data -- Events-in-Trial Outcome -- Other Types of Outcomes -- Estimating Categorical Models With GENLIN -- GENLIN Model-Building Features -- Type of Model Command Tab -- Distribution and Log Link Function -- Custom Distribution and Link Function -- The Response Command Tab -- Dependent Variable -- Reference Category. | |
| 505 | 8 | |a Number of Events Occurring in a Set of Trials -- The Predictors Command Tab -- Predictors -- Offset -- The Model Command Tab -- Main Effects -- Interactions -- The Estimation Command Tab -- Parameter Estimation -- The Statistics Command Tab -- Model Effects -- Additional GENLIN Command Tabs -- Estimated Marginal (EM) Means -- Save -- Export -- Building a Single-Level Model -- Research Questions -- The Data -- Specifying the Model -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.1 -- Adding Gender to the Model -- Defining Model 1.2 With IBM SPSS Menu Commands -- Obtaining Predicted Probabilities for Males and Females -- Adding Additional Background Predictors -- Defining Model 1.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.3 -- Testing an Interaction -- Limitations of Single-Level Analysis -- Summary -- Note -- ch. 4 Multilevel Models With Dichotomous Outcomes -- Introduction. | |
| 505 | 8 | |a Components for Generalized Linear Mixed Models -- Specifying a Two-Level Model -- Specifying a Three-Level Model -- Model Estimation -- Building Multilevel Models With GENLIN MIXED -- Data Structure Command Tab -- Fields and Effects Command Tab -- Target Main Screen -- Fixed Effects Main Screen -- Random Effects Main Screen -- Weight and Offset Main Screen -- Build Options Command Tab -- Selecting the Sort Order -- Stopping Rules -- Confidence Intervals -- Degrees of Freedom -- Tests of Fixed Effects -- Tests of Variance Components -- Model Options Command Tab -- Estimating Means and Contrasts -- Save Fields -- Examining Variables That Explain Student Proficiency in Reading -- Research Questions -- The Data -- The Unconditional (Null) Model -- Defining Model 1.1 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.2 -- Defining the Within-School Variables -- Defining Model 1.2 With IBM SPSS Menu Commands. | |
| 505 | 8 | |a Interpreting the Output of Model 1.2 -- Examining Whether a Level-1 Slope Varies Between Schools -- Defining Model 1.3 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.3 -- Adding Level-2 Predictors to Explain Variability in Intercepts -- Defining Model 1.4 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.4 -- Adding Level-2 Variables to Explain Variation in Level-1 Slopes (Cross-Level Interaction) -- Defining Model 1.5 with IBM SPSS Menu Commands -- Interpreting the Output of Model 1.5 -- Estimating Means -- Saving Output -- Probit Link Function -- Defining Model 1.6 with IBM SPSS Menu Commands -- Interpreting Probit Coefficients -- Interpreting the Output of Model 1.6 -- Examining the Effects of Predictors on Probability of Being Proficient -- Extending the Two-Level Model to Three Levels -- The Unconditional Model -- Defining Model 2.1 with IBM SPSS Menu Commands -- Interpreting the Output of Model 2.1. | |
| 505 | 8 | |a Defining the Three-Level Model -- Defining Model 2.2 with IBM SPSS Menu Commands -- Interpreting the Output of Model 2.2 -- Summary -- ch. 5 Multilevel Models With a Categorical Repeated Measures Outcome -- Introduction -- Generalized Estimating Equations -- GEE Model Estimation -- An Example Study -- Research Questions -- The Data -- Defining the Model -- Model Specifying the Intercept and Time -- Correlation and Covariance Matrices -- Standard Errors -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.1 -- Alternative Coding of the Time Variable -- Defining Model 1.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.2 -- Defining Model 1.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.3 -- Adding a Predictor -- Defining Model 1.4 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.4 -- Adding an Interaction Between Female and the Time Parameter. | |
| 505 | 8 | |a Adding an Interaction to Model 1.5 -- Interpreting the Output of Model 1.5 -- Categorical Longitudinal Models Using GENLIN MIXED -- Specifying a GEE Model Within GENLIN MIXED -- Defining Model 2.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.1 -- Examining a Random Intercept at the Between-Student Level -- Defining Model 2.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.2 -- What Variables Affect Differences in Proficiency Across Individuals? -- Defining Model 2.3 With IBM SPSS Menu Commands -- Adding Two Interactions to Model 2.3 -- Interpreting the Output of Model 2.3 -- Building a Three-Level Model in GENLIN MIXED -- The Beginning Model -- Defining Model 3.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 3.1 -- Adding Student and School Predictors -- Defining Model 3.2 With IBM SPSS Menu Commands -- Adding Two Interactions to Model 3.2 -- Adding Two More Interactions to Model 3.2. | |
| 505 | 8 | |a Interpreting the Output of Model 3.2 -- An Example Experimental Design -- Defining Model 4.1 With IBM SPSS Menu Commands -- Summary -- ch. 6 Two-Level Models With Multinomial and Ordinal Outcomes -- Introduction -- Building a Model to Examine a Multinomial Outcome -- Research Questions -- The Data -- Defining the Multinomial Model -- Defining a Preliminary Single-Level Model -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 1.1 -- Developing a Multilevel Multinomial Model -- Unconditional Two-Level Model -- Defining Model 2.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.1 -- Computing Predicted Probabilities -- Level-1 Model -- Defining Model 2.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.2 -- Adding School-Level Predictors -- Defining Model 2.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.3 -- Investigating a Random Slope. | |
| 505 | 8 | |a Defining Model 2.4 With IBM SPSS Menu Commands -- Interpreting the Output of Model 2.4 Model Results -- Developing a Model With an Ordinal Outcome -- The Data -- Developing a Single-Level Model -- Preliminary Analyses -- Defining Model 3.1 with IBM SPSS Menu Commands -- Interpreting the Output of Model 3.1 -- Adding Student Background Predictors -- Defining Model 3.2 with IBM SPSS Menu Commands -- Interpreting the Output of Model 3.2 -- Testing an Interaction -- Defining Model 3.3 With IBM SPSS Menu Commands -- Adding Interactions to Model 3.3 -- Interpreting the Output of Model 3.3 -- Following Up With a Smaller Random Sample -- Developing a Multilevel Ordinal Model -- Level-1 Model -- Unconditional Model -- Defining Model 4.1 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.1 -- Within-School Predictor -- Defining Model 4.2 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.2 -- Adding the School-Level Predictors. | |
| 505 | 8 | |a Defining Model 4.3 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.3 -- Using Complementary Log-Log Link -- Interpreting a Categorical Predictor -- Other Possible Analyses -- Examining a Mediating Effect at Level 1 -- Defining Model 4.4 With IBM SPSS Menu Commands -- Interpreting the Output of Model 4.4 -- Estimating the Mediated Effect -- Summary -- Note -- ch. 7 Two-Level Models With Count Data -- Introduction -- A Poisson Regression Model With Constant Exposure -- The Data -- Preliminary Single-Level Models -- Defining Model 1.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.1 -- Defining Model 1.2 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.2 -- Considering Possible Overdispersion -- Defining Model 1.3 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.3 -- Defining Model 1.4 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.4. | |
| 505 | 8 | |a Defining Model 1.5 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 1.5 -- Comparing the Fit -- Estimating Two-Level Count Data With GENLIN MIXED -- Defining Model 2.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.1 -- Building a Two-Level Model -- Defining Model 2.2 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.2 -- Within-Schools Model -- Defining Model 2.3 with IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.3 -- Examining Whether the Negative Binomial Distribution Is a Better Choice -- Defining Model 2.4 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.4 -- Does the SES-Failure Slope Vary Across Schools? -- Defining Model 2.5 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 2.5 -- Modeling Variability at Level 2 -- Defining Model 2.6 With IBM SPSS Menu Commands. | |
| 505 | 8 | |a Interpreting the Output Results of Model 2.6 -- Adding the Cross-Level Interactions -- Defining Model 2.7 With IBM SPSS Menu Commands -- Adding Two Interactions to Model 2.7 -- Interpreting the Output Results of Model 2.7 -- Developing a Two-Level Count Model With an Offset Variable -- The Data -- Research Questions -- Offset Variable -- Specifying a Single-Level Model -- Defining Model 3.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.1 -- Adding the Offset -- Defining Model 3.2 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.2 -- Defining Model 3.3 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.3 -- Defining Model 3.4 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 3.4 -- Estimating the Model With GENLIN MIXED -- Defining Model 4.1 With IBM SPSS Menu Commands -- Interpreting the Output Results of Model 4.1. | |
| 520 | |a "Preface Multilevel modeling has become a mainstream data analysis tool over the past decade, now figuring prominently in a range of social and behavioral science disciplines. Where it originally required specialized software, mainstream statistics packages such as IBM SPSS, SAS, and Stata all have included routines for multilevel modeling in their programs. Although some devotees of these statistical packages have been making good use of the relatively new multilevel modeling functionality, progress has been slower in carefully documenting these routines to facilitate meaningful access to the average user. Two years ago we developed Multilevel and Longitudinal Modeling with IBM SPSS to demonstrate how to use these techniques in IBM SPSS Version 18. Our focus was on developing a set of concepts and programming skills within the IBM SPSS environment that could be used to develop, specify, and test a variety of multilevel models with continuous outcomes, since IBM SPSS is a standard analytic tool used in many graduate programs and organizations globally. Our intent was to help readers gain facility in using the IBM SPSS linear-mixed models routine for continuous outcomes. We offered multiple examples of several different types of multilevel models, focusing on how to set up each model and how to interpret the output. At the time, mixed modeling for categorical outcomes was not available in the IBM SPSS software program. Over the past year or so, however, the generalized linear mixed model (GLMM) has been added to the mixed modeling analytic routine in IBM SPSS starting with Version 19. This addition prompted us to create this companion workbook that would focus on introducing readers to the multilevel approach to modeling with categorical outcomes"-- |c Provided by publisher. | ||
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| 700 | 1 | |a Thomas, Scott Loring. |0 http://id.loc.gov/authorities/names/n99007284. | |
| 700 | 1 | |a Tabata, Lynn Naomi. |0 http://id.loc.gov/authorities/names/no2006102833 |1 http://isni.org/isni/0000000042775163. | |
| 776 | 0 | 8 | |a Heck, Ronald H. |t Multilevel modeling of categorical outcomes using IBM SPSS. |d New York ; London : Routledge, 2012 |z 9781848729551 |w (DLC) 2012004179 |w (OCoLC)758394697. |
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