# The Reviewer's Guide to Quantitative Methods in the Social Sciences

## Gregory R. Hancock, Laura M. Stapleton, Ralph O. Mueller, Gregory R. Hancock, Laura M. Stapleton, Ralph O. Mueller

- 502 pagine
- English
- ePUB (disponibile sull'app)
- Disponibile su iOS e Android

# The Reviewer's Guide to Quantitative Methods in the Social Sciences

## Gregory R. Hancock, Laura M. Stapleton, Ralph O. Mueller, Gregory R. Hancock, Laura M. Stapleton, Ralph O. Mueller

## Informazioni sul libro

The Reviewer's Guide to Quantitative Methods in the Social Sciences provides evaluators of research manuscripts and proposals in the social and behavioral sciences with the resources they need to read, understand, and assess quantitative work. 35 uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The second edition of this valuable resource updates readers on each technique's key principles, appropriate usage, underlying assumptions and limitations, providing reviewers with the information they need to offer constructive commentary on works they evaluate. Written by methodological and applied scholars, this volume is also an indispensable author's reference for preparing sound research manuscripts and proposals.

## Domande frequenti

## Informazioni

Desideratum | Manuscript Section(s)* |

1. The dependent variable(s) under study are outlined with a discussion of their importance within the field of study. | I |

2. Each discrete-level independent variable is defined and its hypothesized relation with the dependent variable is explained. | I |

3. A rationale is provided for the simultaneous inclusion of two or more independent variables and any interaction effects are discussed in terms of their relation with the dependent variable. | I |

4. Appropriate analyses are adopted when the research hypothesis relates to the equivalence of means. | I |

5. The inclusion of any covariate is justified in terms of its purpose within the analysis. | I |

6. The research design is explained in detail, including the nature/measurement of all independent and dependent variables. | M |

7. In randomized block designs, the number, nature, and method of creation of the blocks is discussed. | M |

8. The use of a random factor is justified given the hypotheses. | M |

9. In hierarchical designs, the rationale for nesting is explained and the analysis acknowledges the dependence in the data. | M |

10. In incomplete designs, or complex variants of other designs, sufficient information and references are provided. | M |

11. A rationale is given for the number of participants, the source of the participants, and any inclusion/exclusion criteria used. | M |

12. Missing data and statistical assumptions of the model are investigated and robust methods are adopted when issues arise. | M, R |

13. The final model is discussed, including defining and justifying the chosen error term and significance level. | M, R |

14. Follow-up strategies for significant main effects or interactions are discussed. | M, R |

15. Effect size and confidence interval information are provided to supplement the results of the statistical significance tests. | R |

16. Appropriate language, relative to the meaning and generalizability of the findings, is used. | D |

## Indice dei contenuti

- Cover
- Half Title
- Praise
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- Preface
- 1 Analysis of Variance: Between-Groups Designs
- 2 Analysis of Variance: Repeated-Measures Designs
- 3 Canonical Correlation Analysis
- 4 Cluster Analysis
- 5 Correlation and Other Measures of Association
- 6 Effect Sizes and Confidence Intervals
- 7 Event History and Survival Analysis
- 8 Factor Analysis: Exploratory and Confirmatory
- 9 Generalizability Theory
- 10 Interrater Reliability and Agreement
- 11 Item Response Theory and Rasch Modeling
- 12 Latent Class Analysis
- 13 Latent Growth Curve Models
- 14 Latent Transition Analysis
- 15 Latent Variable Mixture Models
- 16 Logistic Regression and Extensions
- 17 Log-Linear Analysis
- 18 Mediation and Moderation
- 19 Meta-analysis
- 20 Monte Carlo Simulation Methods
- 21 Multidimensional Scaling
- 22 Multilevel Modeling
- 23 Multiple Regression
- 24 Multitrait–Multimethod Analysis
- 25 Multivariate Analysis of Variance
- 26 Nonparametric Statistics
- 27 Power Analysis
- 28 Propensity Scores and Matching Methods
- 29 Reliability and Validity
- 30 Research Design
- 31 Single-Subject Design and Analysis
- 32 Social Network Analysis
- 33 Structural Equation Modeling
- 34 Structural Equation Modeling: Multisample Covariance and Mean Structures
- 35 Survey Sampling, Administration, and Analysis
- List of Contributors
- Index