An Introduction to Codes and Coding Chapter Summary Purposes of the Manual What Is a Code? Codifying and Categorizing What Gets Coded? The Mechanics of Coding The Numbers of Codes Manual and CAQDAS Coding Solo and Team Coding Necessary Personal Attributes for Coding On Method Writing Analytic Memos Chapter Summary The Purposes of Analytic Memo-Writing What Is an Analytic Memo? Examples of Analytic Memos Coding and Categorizing Analytic Memos Grounded Theory and Its Coding Canon Analytic Memos on Visual Data First-Cycle Coding Methods Chapter Summary The Coding Cycles Selecting the Appropriate Coding Method(s) Overview of First-Cycle Coding Methods The Coding Methods Profiles Grammatical Methods Elemental Methods Affective Methods Literary and Language Methods Exploratory Methods Forms for Additional First-Cycle Coding Methods Theming the Data Procedural Methods After First-Cycle Coding Chapter Summary Post-Coding Transitions Eclectic Coding Code Mapping and Landscaping Operational Model Diagramming Additional Transition Methods Transitioning to Second-Cycle Coding Methods Second-Cycle Coding Methods Chapter Summary The Goals of Second-Cycle Methods Overview of Second-Cycle Coding Methods Second-Cycle Coding Methods Forms for Additional Second-Cycle Coding Methods After Second-Cycle Coding Chapter Summary Post-Coding and Pre-Writing Transitions Focusing Strategies From Coding to Theorizing Formatting Matters Writing about Coding Ordering and Re-Ordering Assistance from Others Closure Appendix A: A Glossary of Coding Methods Appendix B: A Glossary of Analytic Recommendations Appendix C: Field Note, Interview Transcript and Document Samples for Coding Appendix D: Exercises and Activities for Coding and Qualitative Data Analytic Skill Development References Index

The Coding Manual for Qualitative Researchers

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Using Multivariate Statistics

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

This chapter suggests that delinquency conceals two distinct categories of individuals, each with a unique natural history and etiology: A small group engages in antisocial behavior of one sort or another at every life stage, whereas a larger group is antisocial only during adolescence. According to the theory of life-course-persistent antisocial behavior, children's neuropsychological problems interact cumulatively with their criminogenic environments across development, culminating m a pathological personality. According to the theory of adolescence-limited antisocial behavior, a contemporary maturity gap encourages teens to mimic antisocial behavior in ways that are normative and adjustive. There are marked individual differences in the stability of antisocial behavior. The chapter reviews the mysterious relationship between age and antisocial behavior. Some youths who refrain from antisocial behavior may, for some reason, not sense the maturity gap and therefore lack the hypothesized motivation for experimenting with crime.

/pdf/adolescence-limited-and-life-course-persistent-antisocial-52vil00qlw.pdf

Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy.

Summary

The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models.


The presentation of ‘variance explained’ (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest.


One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation).


Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems.


This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210x.2012.00261.x

A general and simple method for obtaining R2 from generalized linear mixed-effects models

Series Editor's Introduction Author's Introduction to the Second Edition 1. Linear Regression and Logistic Regression Model 2. Summary Statistics for Evaluating the Logistic Regression Model 3. Interpreting the Logistic Regression Coefficients 4. An Introduction to Logistic Regression Diagnosis Ch 5. Polytomous Logistic Regression and Alternatives to Logistic Regression 6. Notes Appendix A References Tables Figures

Applied Logistic Regression Analysis

1 Introduction.- 2 The Demographic Distribution of Delinquency and ADM Problems.- 3 Prevalence and General Offending/Use Patterns: The Joint Occurrence of Delinquent Behavior and ADM Problems.- 4 Age, Period, and Cohort Effects.- 5 Developmental Patterns.- 6 The Etiology of Delinquency and ADM Problems.- 7 Prediction of Delinquent and ADM Behavior from Other Delinquent and ADM Behavior.- 8 Summary and Implications.- References.- Appendix A Frequency of Alcohol Use.- Appendix B Mental Health Measures.- Appendix C Prevalence and Offending/Use Rates for Multiple Problem Types.- Appendix D Annual Transition Matrices for Problem Behavior Types.- Author Index.

Multiple Problem Youth: Delinquency, Substance Use, and Mental Health Problems

Multiple Problem Youth: Delinquency, Substance Use, and Mental Health Problems.

Coefficients of determination for continuous predicted values (R 2 analogs) in logistic regression are examined for their conceptual and mathematical similarity to the familiar R 2 statistic from ordinary least squares regression, and compared to coefficients of determination for discrete predicted values (indexes of predictive efficiency). An example motivated by substantive concerns and using empirical data from a national household probability sample is presented to illustrate the behavior of the different coefficients of determination in the evaluation of models including dependent variables with different base rates—that is, different proportions of cases or observations with “positive” outcomes. One R 2 analog appears to be preferable to the others both in terms of conceptual similarity to the ordinary least squares coefficient of determination, and in terms of its relative independence from the base rate. In addition, base rate should also be considered when selecting an index of predictiv...

Scott Menard

Papers

Applied Logistic Regression Analysis

Applied Logistic Regression Analysis

Multiple Problem Youth: Delinquency, Substance Use, and Mental Health Problems

Multiple Problem Youth: Delinquency, Substance Use, and Mental Health Problems.

Coefficients of Determination for Multiple Logistic Regression Analysis