rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Applied survey data analysis
  name:
    name part: Heeringa, Steven
    name part: 1953-
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: West, Brady T.
    role:
      role term: author.
  name:
    name part: Berglund, Patricia A.
    role:
      role term: author.
  name:
    name part: EBSCOhost
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2017
    edition: Second edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  table of contents: Note continued: 9.4.1. Survey Count Variables and Regression Modeling Alternatives -- 9.4.2. Generalized Linear Models for Count Variables -- 9.4.2.1. Poisson Regression Model -- 9.4.2.2. Negative Binomial Regression Model -- 9.4.2.3. Two- -- Part Models: Zero-Inflated Poisson and Negative Binomial Regression Models -- 9.4.3. Regression Models for Count Data: Specification Stage -- 9.4.4. Regression Models for Count Data: Estimation Stage -- 9.4.5. Regression Models for Count Data: Evaluation Stage -- 9.4.6. Regression Models for Count Data: Interpretation Stage -- 9.4.7. Example: Fitting Poisson and Negative Binomial Regression Models to Complex Sample Survey Data -- 10.1. Introduction -- 10.2. Basic Theory of Survival Analysis -- 10.2.1. Survey Measurement of Event History Data -- 10.2.2. Data for Event History Models -- 10.2.3. Important Notation and Definitions -- 10.2.4. Models for Survival Analysis -- 10.3. (Nonparametric) K-M Estimation of the Survivor Function -- 10.3.1. K-M Model Specification and Estimation -- 10.3.2. K-M Estimator: Evaluation and Interpretation -- 10.3.3. K-M Survival Analysis Example -- 10.4. The Cox Proportional Hazards (CPH) Model -- 10.4.1. CPH Model: Specification -- 10.4.2. CPH Model: Estimation Stage -- 10.4.3. CPH Model: Evaluation and Diagnostics -- 10.4.4. CPH Model: Interpretation and Presentation of Results -- 10.4.5. Example: Fitting a CPH Model to Complex Sample Survey Data -- 10.5. Discrete Time Survival Models -- 10.5.1. Discrete Time Logistic Model -- 10.5.2. Data Preparation for Discrete Time Survival Models -- 10.5.3. Discrete Time Models: Estimation Stage -- 10.5.4. Discrete Time Models: Evaluation and Interpretation -- 10.5.5. Fitting a Discrete Time Model to Complex Sample Survey Data -- 11.1. Introduction -- 11.2. Alternative Analytic Objectives with Longitudinal Survey Data -- 11.2.1. Objective 1: Descriptive Estimation at a Single Time Point -- 11.2.2. Objective 2: Estimation of Change across Two Waves -- 11.2.3. …
  table of contents: Machine generated contents note: 1.1. Introduction -- 1.2. A Brief History of Applied Survey Data Analysis -- 1.2.1. Key Theoretical Developments -- 1.2.2. Key Software Developments -- 1.3. Example Data Sets and Exercises -- 1.4. Steps in Applied Survey Data Analysis -- 2.1. Introduction -- 2.1.1. Technical Documentation and Supplemental Literature Review -- 2.2. Classification of Sample Designs -- 2.2.1. Sampling Plans -- 2.2.2. Other Types of Study Designs Involving Probability Sampling -- 2.2.3. Inference from Survey Data -- 2.3. Target Populations and Survey Populations -- 2.4. Simple Random Sampling: A Simple Model for Design- Based Inference -- 2.4.1. Relevance of SRS to Complex Sample Survey Data Analysis -- 2.4.2. SRS Fundamentals: A Framework for Design-Based Inference -- 2.4.3. Example of Design-Based Inference under SRS -- 2.5. Complex Sample Design Effects -- 2.5.1. Design Effect Ratio -- 2.5.2. Generalized Design Effects and Effective Sample Sizes -- 2.6. Complex Samples: Cluster Sampling and Stratification -- 2.6.1. Cluster Sampling Plans -- 2.6.2. Stratification -- 2.6.3. Joint Effects of Sample Stratification and Cluster Sampling -- 2.7. Weighting in Analysis of Survey Data -- 2.7.1. Introduction to Weighted Analysis of Survey Data -- 2.7.2. Weighting for Probabilities of Selection (wsel) -- 2.7.3. Nonresponse Adjustment Weights (wnr) -- 2.7.3.1. Weighting Class Approach (wnr, wc) -- 2.7.3.2. Propensity Cell Adjustment Approach (wnrprop) -- 2.7.4. Poststratification Weight Factors (wps) -- 2.7.5. Design Effects Due to Weighted Analysis -- 2.8. Multistage Area Probability Sample Designs -- 2.8.1. Primary Stage Sampling -- 2.8.2. Secondary Stage Sampling -- 2.8.3. Third-and Fourth-Stage Sampling of HUs and Eligible Respondents -- 2.9. Special Types of Sampling Plans Encountered in Surveys -- 3.1. Introduction -- 3.2. Finite Populations and Superpopulation Models -- 3.3. CIs for Population Parameters -- 3.4. Weighted Estimation of Population Parameters …
  table of contents: Inference -- 8.7. Analysis Application -- 8.7.1. Stage 1: Model Specification -- 8.7.2. Stage 2: Model Estimation -- 8.7.3. Stage 3: Model Evaluation -- 8.7.4. Stage 4: Model Interpretation/Inference -- 8.8. Comparing the Logistic, Probit, and C-L-L GLMs for Binary Dependent Variables -- 9.1. Introduction -- 9.2. Analyzing Survey Data Using Multinomial Logit Regression Models -- 9.2.1. Multinomial Logit Regression Model -- 9.2.2. Multinomial Logit Regression Model: Specification Stage -- 9.2.3. Multinomial Logit Regression Model: Estimation Stage -- 9.2.4. Multinomial Logit Regression Model: Evaluation Stage -- 9.2.5. Multinomial Logit Regression Model: Interpretation Stage -- 9.2.6. Example: Fitting a Multinomial Logit Regression Model to Complex Sample Survey Data -- 9.3. Logistic Regression Models for Ordinal Survey Data -- 9.3.1. Cumulative Logit Regression Model -- 9.3.2. Cumulative Logit Regression Model: Specification Stage -- 9.3.3. Cumulative Logit Regression Model: Estimation Stage -- 9.3.4. Cumulative Logit Regression Model: Evaluation Stage -- 9.3.5. Cumulative Logit Regression Model: Interpretation Stage -- 9.3.6. Example: Fitting a Cumulative Logit Regression Model to Complex Sample Survey Data -- 9.4. Regression Models for Count Outcomes.
  note: Steven G. Heeringa, Brady T. West, and Patricia A. Berglund.
  note: Electronic resource.
  note: Includes bibliographical references (pages 501-518) and index.
  subject:
    topic: Social sciences
    genre: Statistics
  subject:
    topic: Social surveys
    topic: Statistical methods
  subject:
    topic: Social Sciences
    topic: statistics & numerical data
  subject:
    topic: Statistics as Topic
    topic: methods
  classification: HA29 .H428 2017
  classification: 001.4/22
  classification: HA 29
  related item:
    identifier: (DLC) 2016050459
  related item:
    title info:
      title: Statistics in the social and behavioral sciences series
  identifier: 9781351649308 (electronic bk.)
  identifier: 1351649302 (electronic bk.)
  identifier: 9781498761604
  identifier: 1498761607
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 200603
    record change date: 20260609153456.2
    record identifier: in00004169798