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دانلود کتاب Higher-order growth curves and mixture modeling with Mplus : a practical guide
دانلود کتاب منحنی های رشد مرتبه بالاتر و مدل سازی مخلوط با Mplus: راهنمای عملی
مشخصات کتاب
Higher-order growth curves and mixture modeling with Mplus : a practical guide
ویرایش: [2 ed.] نویسندگان: Tae Kyoung Lee, Frederick Oscar Lorenz, K. A. S. Wickrama, Catherine Walker O'Neal سری: Multivariate applications series ISBN (شابک) : 9780367711269, 0367746204 ناشر: سال نشر: 2022 تعداد صفحات: [347] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 78 Mb
قیمت کتاب (تومان) : 30,000
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توجه داشته باشید کتاب منحنی های رشد مرتبه بالاتر و مدل سازی مخلوط با Mplus: راهنمای عملی نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب منحنی های رشد مرتبه بالاتر و مدل سازی مخلوط با Mplus: راهنمای عملی
این مقدمه عملی برای مدلهای مخلوط مرتبه دوم و رشد با استفاده از Mplus، تکنیکهای ساده و پیچیده را از طریق مراحل افزایشی معرفی میکند. نویسندگان منحنیهای رشد نهفته را به منحنی رشد مرتبه دوم و مدلهای مخلوط گسترش میدهند و سپس این دو را با استفاده از دادههای نرمال و غیرعادی (به عنوان مثال، طبقهبندی) ترکیب میکنند. برای به حداکثر رساندن درک، هر مدل با معادلات ساختاری پایه، ارقام با نحو مرتبط که معنی آمار، کاربردهای Mplus و تفسیر نتایج را برجسته میکند، ارائه میشود. مثالهایی از رشتههای مختلف نشان میدهد که استفاده از مدلها و تمرینها به خوانندگان اجازه میدهد تا درک خود را از تکنیکها آزمایش کنند. مقدمه ای جامع برای تحلیل عاملی تاییدی، مدل سازی منحنی رشد نهفته، و مدل سازی مخلوط رشد ارائه شده است تا کتاب بتواند توسط خوانندگان سطوح مختلف مهارت استفاده شود. مجموعه داده های کتاب در وب موجود است. جدید در این نسخه: * دو فصل جدید یک معرفی گام به گام و راهنمای عملی برای استفاده از منحنی های رشد مرتبه دوم و مدل های مخلوط با نتایج طبقه بندی شده با استفاده از برنامه Mplus ارائه می دهد. با تمرین ها، کلیدهای پاسخ، و فایل های داده قابل دانلود کامل شود. * نمونه های مصور به روز شده با استفاده از Mplus 8.0 شامل شکل های مفهومی، نحو برنامه Mplus و تفسیر نتایج است تا به خوانندگان نشان دهد که چگونه تحلیل ها را با داده های واقعی انجام دهند. این متن برای استفاده در دورههای تحصیلات تکمیلی یا کارگاههای آموزشی معادلات ساختاری پیشرفته، مدلسازی متغیرهای چندسطحی، طولی یا پنهان، منحنی رشد نهفته و مدلسازی مخلوط، تحلیل عاملی، آمار چند متغیره، یا تکنیکهای کمی (روشها) پیشرفته در علوم اجتماعی و رفتاری ایدهآل است. .
توضیحاتی درمورد کتاب به خارجی
This practical introduction to second-order and growth mixture models using Mplus introduces simple and complex techniques through incremental steps. The authors extend latent growth curves to second-order growth curve and mixture models and then combine the two using normal and non-normal (e.g., categorical) data. To maximize understanding, each model is presented with basic structural equations, figures with associated syntax that highlight what the statistics mean, Mplus applications, and an interpretation of results. Examples from a variety of disciplines demonstrate the use of the models and exercises allow readers to test their understanding of the techniques. A comprehensive introduction to confirmatory factor analysis, latent growth curve modeling, and growth mixture modeling is provided so the book can be used by readers of various skill levels. The book's datasets are available on the web. New to this edition: * Two new chapters providing a stepwise introduction and practical guide to the application of second-order growth curves and mixture models with categorical outcomes using the Mplus program. Complete with exercises, answer keys, and downloadable data files. * Updated illustrative examples using Mplus 8.0 include conceptual figures, Mplus program syntax, and an interpretation of results to show readers how to carry out the analyses with actual data. This text is ideal for use in graduate courses or workshops on advanced structural equation, multilevel, longitudinal or latent variable modeling, latent growth curve and mixture modeling, factor analysis, multivariate statistics, or advanced quantitative techniques (methods) across the social and behavioral sciences.
فهرست مطالب
Cover Endorsement Page Half Title Series Page Title Page Copyright Page Table of Contents Preface Acknowledgments Authors Part I Growth Curve Modeling Chapter 1 Introduction A Layout of Incrementally Related SEMs: An Organizing Guide Illustrative Example 1.1: Examining Alternative Growth Curve Models Adolescents’ Internalizing Symptoms (IS) Trajectories Datasets Used in Illustrations Measures References Chapter 2 Latent Growth Curves Introduction Growth Curve Modeling Conventional Latent Growth Curve Models (LGCM Linear Growth Curve Modeling Investigating Longitudinal Covariance Patterns Illustrative Example 2.1: Examining the Longitudinal Covariance Pattern of Indicators Estimating an Unconditional Linear Latent Growth Curve Model (LGCM) Using Mplus Illustrative Example 2.2: Estimating a Linear Latent Growth Curve Model (LGCM Curvilinear Growth Curve Modeling (i.e., a Quadratic Growth Curve Model Illustrative Example 2.3: Estimating a Quadratic Latent Growth Curve Model (LGCM Model Fit Indices Comparing Nested Models Illustrative Example 2.4: Nested Model Comparison between Linear and Quadratic Models Illustrative Example 2.5: Nested Model Comparison between Models with and without Correlated Errors Illustrative Example 2.6: Non-Nested Model Comparison between Linear and Piecewise Models Adding Covariates to an Unconditional Model Illustrative Example 2.7: Adding a Predictor and Outcome to a Linear LGCM Illustrative Example 2.8: Adding a Predictor and Outcome to a Quadratic LGCM Methodological Concerns in Longitudinal Analysis: Why Growth Curves The Need to Preserve the Continuity of Change The Need to Investigate Different Growth Parameters The Need to Incorporate Growth Parameters as Either Predictors or Outcomes in the Same Model The Need to Incorporate Time-Varying Predictors Limitations Beyond Latent Growth Curve Modeling Revisiting the Layout of Models: Figures 1.1, 1.2, and 1.3 First-Order Structural Equation Models Second-Order Growth Curve Modeling Growth Mixture Modeling Chapter 2 Exercises References Chapter 3 Longitudinal Confirmatory Factor Analysis and Curve-of-Factors Growth Curve Models Introduction Confirmatory Factor Analysis (CFA) (Step 1 Specification of a Simple CFA CFA Model Identification Scale Setting in a CFA Longitudinal Confirmatory Factor Analysis (LCFA): Model Specification (Step 2 A Second-Order Growth Curve: A Curve-of-Factors Model (Step 3 Specification of a Curve-of-Factors Model (CFM Why Analyze a Curve-of-Factors Model? Improvements Over a Conventional LGCM Equal Contribution of Items to the Composite Measure Longitudinal Measurement Invariance (Factorial Invariance Variance Components of Indicators: Measurement Error and Time-Specific Variance Chapter 3 Exercises References Chapter 4 Estimating Curve-of-Factors Growth Curve Models Introduction Steps for Estimating a Curve-of-Factors Model (CFM Investigating the Longitudinal Correlation Patterns of Subdomain Indicators (Step 1 Illustrative Example 4.1: Examining the Longitudinal Correlation Patterns among Indicators Performing an Unconstrained Longitudinal Confirmatory Factor Analysis (LCFA) (Step 2 Illustrative Example 4.2: Longitudinal Confirmatory Factor Analysis (LCFA) Using Mplus Measurement Invariance of the LCFA Model (Step 3 Illustrative Example 4.3: Systematic Incremental Testing Sequences for Assessing Measurement Invariance Nested Model Comparison for Measurement Invariance Taking Autocorrelations among Indicators in a LCFA into Account as a Trait Factor Illustrative Example 4.4: Longitudinal Confirmatory Factor Analysis (LCFA) with “Trait” Factors (IT Model Estimating a Second-Order Growth Curve: A Curve-of-Factors Model (CFM) (Step 4 Illustrative Example 4.5: Estimating a Curve-of-Factors Model (CFM Scale Setting Approaches and Second-Order Growth Model Parameters (Curve-of- Factors Model, CFM Marker Variable Approach Illustrative Example 4.6: Using the Marker Variable Approach for CFA Scale Setting Fixed Factor Approach Illustrative Example 4.7: Using the Fixed Factor Scale Setting Approach in a CFA Effect Coding Approach Illustrative Example 4.8: Using the Effect Coding Scale Setting Approach in a CFA Adding Covariates to a Curve-of-Factors Model (CFM Time-Invariant Covariate (TIC) Model Incorporating a single indicator variable (W) as a predictor Illustrative Example 4.9: Adding a Time-Invariant Covariate (TIC) as a CFM Predictor Incorporating a Multiple-Indicator Latent Variable (P) as a Predictor Illustrative Example 4.10: Adding a Multiple-Indicator Latent Factor as a CFM Predictor Predicting Both Time-Specific Latent Factors and Second-Order Growth Parameters Illustrative Example 4.11: Predicting Both Second-Order Growth Parameters and First-Order Latent Factors Predicting Distal Outcomes (D) of Second-Order Growth Factors Illustrative Example 4.12: Predicting Distal Outcomes of Second-Order Growth Factors Time-Varying Covariate (TVC) Model Time-Varying Covariate (TVC) as a Predictor of Manifest Outcomes Illustrative Example 4.13: Incorporating a Time-Varying Covariate as a Direct Predictor of Manifest Indicators A Parallel Process Second-Order Model Using a Dyadic Model Framework as an Example Illustrative Example 4.14: Incorporating a Time-Varying Covariate as a Parallel Process Chapter 4 Exercises References Chapter 5 Extending a Parallel Process Latent Growth Curve Model (PPM) to a Factor-of-Curves Model (FCM Introduction Parallel Process Latent Growth Curve Model (PPM Estimating a Parallel Process Model (PPM Correlation of Measurement Errors in a PPM Influence of Growth Factors of One Subdomain on the Growth Factors of Other Subdomains Modeling Sequentially Contingent Processes over Time Extending a Parallel Process Latent Growth Curve Model (PPM) to a Factor-of- Curves Growth Curve Model (FCM Second-Order Growth Factors Chapter 5 Exercises References Chapter 6 Estimating a Factor-of-Curves Model (FCM) and Adding Covariates Introduction Estimating a Factor-of-Curves Model (FCM) Investigating the Longitudinal Correlation Patterns among Repeated Measures of Each Subdomain (Step 1) Illustrative Example 6.1: Investigating the Longitudinal Correlation Patterns among Repeated Measures of Each Subdomain Estimating a Parallel Process Growth Curve Model (PPM) (Step 2) Illustrative Example 6.2: Estimating a Parallel Process Growth Curve Model (PPM) Estimating a Factor-of-Curves Model (FCM) (Step 3) Illustrative Example 6.3: Estimating a Factor-of-Curves Model (FCM) Illustrative Example 6.4: Comparing Two Competing Models Empirically Estimating a Conditional FCM (Step 4) Adding Time-Invariant Covariates (TICs) to a FCM Predicting Both First-Order and Second-Order Growth Factors Illustrative Example 6.5: Adding Time-Invariant Covariates (TIC) to a FCM Primary and Secondary Growth Factors of the FCM as Predictors of Latent Distal Outcomes (D) Illustrative Example 6.6: Incorporating a Latent Distal Outcome into a FCM Adding Time-Varying Covariates (TVC) to a FCM A Time-Varying Covariate (TVC) as a Direct Predictor of Indicators Illustrative Example 6.7: Incorporating a Time-Varying Covariate (TVC) as a Direct Predictor Incorporating a TVC as a Secondary Growth Curve: A Second-Order Parallel Process Dyadic Model Illustrative Example 6.8: Incorporating a Time-Varying Predictor as a Parallel Process A Multiple-Group FCM (Multi-Group Longitudinal Modeling) Illustrative Example 6.9: Estimating a FCM for Multiple Groups Multivariate FCM Illustrative Example 6.10: Estimating a Multivariate FCM Model Selection: Factor-of-Curves vs. Curve-of-Factors Illustrative Example 6.11: Empirically Comparing CFM and FCM Approaches Combining a CFM and a FCM: A Factor-of-Curves-of-Factors (FCF) Model Illustrative Example 6.12: Estimating a Factor-of-Curves-of-Factors (FCF) Model Chapter 6 Exercises References Growth Mixture Modeling Part II Growth Mixture Modeling Chapter 7 An Introduction to Growth Mixture Models (GMMs Introduction A Conventional Latent Growth Curve Model (LGCM Potential Heterogeneity in Individual Trajectories Growth Mixture Modeling (GMM Latent Class Growth Analysis (LCGA): A Simplified GMM Specifying a Growth Mixture Model (GMM Specifying Trajectory Classes: Class-Specific Equations Specifying a Latent Class Growth Analysis (LCGA Building A Growth Mixture Model (GMM) Using MSpecify a Traditional Growth Curve Model (LGCM) (Step 1 Estimating a Latent Class Growth Analysis (LCGA) (Step Two Illustrative Example 7.1: Mplus Syntax for a Latent Class Growth Analysis (LCGA Specifying a Growth Mixture Model (GMM) (Step 3 Illustrative Example 7.2: Mplus Syntax for a Growth Mixture Model (GMM Addressing Estimation Problems (Step Four Estimation Problems Related to a Non-Normal Probability Distribution Illustrative Example 7.3: A Non-Normal Distribution Estimation Problems Related to Local Maxima Estimation Problems Due to Model Non-Identification and Inappropriate Data Selecting the Optimal Class Model (Enumeration Indices) (Step 5 Information Criteria (IC) Statistics Entropy and Average Posterior Probabilities Likelihood Ratio Test (LRT): LMR-LRT and Bootstrapped LRT (BLRT Other Considerations Illustrative Example 7.4: Identifying the Optimal Model Summary of a Model-Building Strategy Chapter 7 Exercises References Chapter 8 Estimating a Conditional Growth Mixture Model (GMM Introduction Growth Mixture Models: Predictors and Distal Outcomes The One-Step Approach to Incorporating Covariates into a GMM Predictors of Latent Classes (Multinomial Regression Illustrative Example 8.1: Incorporating a Time-Invariant Predictor into a GMM Predictors of Latent Growth Factors Within Classes Illustrative Example 8.2: Adding Within-Class Effects of Predictors to a GMM Adding Distal Outcomes of Latent Classes (Categorical and Continuous Illustrative Example 8.3: Incorporating a Binary Distal Outcome into a GMM Illustrative Example 8.4: Incorporating a Continuous Distal Outcome into a GMM Uncertainty of Latent Class Membership with the Addition of Covariates The Three-Step Approach: The “Manual” Method Illustrative Example 8.5: The Three-Step Procedure for Incorporating Predictor(s Illustrative Example 8.6: The Three-Step Procedure for Incorporating Distal Outcome(s AUXILIARY Option for the Three-Step Approach Illustrative Example 8.7: Utilizing the Auxiliary Option with the Three-Step Approach Illustrative Example 8.8: Utilizing the Auxiliary Option Chapter 8 Exercises References Chapter 9 Second-Order Growth Mixture Models (SOGMMs Introduction Estimating a Second-Order Growth Mixture Model: A Curve-of-Factors Model (SOGMM of a CFM Illustrative Example 9.1: A Second-Order Growth Mixture Model of a CFM (SOGMM-CF Illustrative Example 9.2: Avoiding Convergence Problems Estimating a Second-Order Growth Mixture Model: A Factor-of-Curves Model (SOGMM of a FCM Illustrative Example 9.3: A Second-Order Growth Mixture Model of a FCM (SOGMM-FC Comparison of Classification between a First-Order GMM with Composite Measures and Second-Order GMMs Estimating a Conditional Model (Conditional SOGMM The Three-Step Approach (Using the AUXILIARY Option) to Add Predictors of Second-Order Trajectory Classes Illustrative Example 9.4: Estimating a Conditional SOGMM with Predictors The Three-Step Approach (Using the AUXILIARY Option) to Add Outcomes of Second-Order Trajectory Classes Illustrative Example 9.5: Estimating a Conditional SOGMM with Outcomes Estimating a Multidimensional Growth Mixture Model (MGMM Illustrative Example 9.6: Estimating a Multidimensional Growth Mixture Model Conclusion Chapter 9 Exercises References Latent Growth Curves with Non-Normal Variables Part III Latent Growth Curves with Non-Normal Variables Chapter 10 Latent Growth Curve Model with Non-Normal Variables Introduction Introduction Latent Response Variable (LRV) Transformation LRV Transformation of a Binary Response Variable Using the Standard Logistic Distribution The LRV Transformation Converting Logistic Coefficients to Probabilities of Yi Being 1 Extending Logit Transformation to Latent Growth Curves with Binary Indicator Variables Illustrative Example 10.1: Estimating a Categorical LGCM with Binary Outcomes A Categorical LGCM with Time-Invariant Covariates Illustrative Example 10.2: Estimating a Conditional Categorical LGCM with Time-Invariant Covariates Applying Probit Transformation for Categorical LGCM Parameterization and Estimator Illustrative Example 10.3: Estimating a Categorical LGCM with Binary Outcomes (Using Probit Transformation Extending Probit Transformations to a Categorical LGCM with Ordinal Outcomes Latent Growth Curves with Count Variables Poisson Model (Log-link Functioning Illustrative Example 10.4: Estimating a Count LGCM Alternative Count Models (Negative Binominal and Zero-Inflated Count Models Negative Binomial (NB) Models Zero-Inflated (ZI) Models Illustrative Example 10.5: Estimating Count LGCMs (Using Negative Binomial and Zero-Inflated Model Conclusion Chapter 10 Exercises Note References Chapter 11 Growth Mixture Models with Non-Normal Variables Introduction Estimating a GMM with Binary Variables Building a GMM with Binary Variables Using Mplus Mplus Syntax for a LCGA with Binary Outcomes (Step 2 Mplus Syntax for a GMM with Binary Outcomes (Step 3 Assessing Estimation Problems (Step 4 Selecting the Optimal Class Model (Step 5 Interpreting Results from the Optimal Class Model with Binary Variables A GMM with Time-Invariant Covariates A GMM with Ordinal Variables A GMM with Count Variables Building a GMM with Count Variables Using Mplus Illustrative Example 11.1: Estimating the Two-Class Model of a Zero-Inflated LCGA Interpreting Results From the Optimal Class Model with Count Variables A Brief Introduction to a Second-Order Growth Mixture Model (SOGMM) with Categorical Variables Conclusion Chapter 11 Exercises References Answers to Chapter Exercises Chapter 2 Exercises Chapter 3 Exercises Chapter 4 Exercises Chapter 5 Exercises Chapter 6 Exercises Chapter 7 Exercises Chapter 8 Exercises Chapter 9 Exercises Chapter 10 Exercises Chapter 11 Exercises Index
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