Vernon Gayle - Quantitative Longitudinal Data Analysis

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Quantitative Longitudinal Data Analysis

Bloomsbury Research Methods

Edited by Graham Crow and Mark Elliot

The Bloomsbury Research Methods series provides authoritative introductions to a range of research methods which are at the forefront of developments in a range of disciplines.

Each volume sets out the key elements of the particular method and features examples of its application, drawing on a consistent structure across the whole series. Written in an accessible style by leading experts in the field, this series is an innovative pedagogical and research resource.

Also available in the series

Community Studies , Graham Crow

Diary Method , Ruth Bartlett and Christine Milligan

GIS , Nick Bearman

Inclusive Research , Melanie Nind

Qualitative Longitudinal Research , Bren Neale

Rhythmanalysis , Dawn Lyon

Forthcoming in the series

Embodied Inquiry , Jennifer Leigh and Nicole Brown

Statistical Modelling in R , Kevin Ralston, Vernon Gayle, Roxanne Connelly and Chris Playford

Quantitative Longitudinal Data Analysis

Research Methods

Vernon Gayle and Paul Lambert

Contents

Stata output: Regression model (OLS) General Certificate of Secondary Education (GCSE) points score School Year 11 Youth Cohort Study of England and Wales

Stata output: Regression model (OLS) General Certificate of Secondary Education (GCSE) points score School Year 11 Youth Cohort Study of England and Wales (cohort and gender interactions)

Selected examples of large-scale cross-nationally harmonized repeated cross-sectional survey projects

A diagram of a hypothetical study of unemployment

Stata output: Compact codebook for the college skills program dataset

Stata output: stdes command for the college skills program data

Stata output: Kaplan-Meier plot of time to passing the test by teaching methods (college skills program data)

Stata output: Log-rank test for equality of survivor functions

Stata output: Cox regression model time to passing the test (college skills program data)

Stata output: Test of the effects of previous education in Cox regression model of time to passing the test (college skills program data)

Stata output: Hazard ratios Cox regression model time to passing the test (college skills program data)

Stata output: Time to passing the test survival functions comparing women aged 30 with 5 previous jobs by teaching methods (college skills program data)

Snapshot of long format panel dataset

Snapshot of a collapsed dataset of mean values of variables (in Figure 13 )

A summary of the features of the fixed effects panel model

Graph of a simple set of panel data

Graph of a simple set of panel data with constant slopes but individual intercepts

A summary of the features of the random effects panel model

Simple notation for a pooled cross-sectional model, a fixed effects panel model and a random effects panel model

Summary of panel dataset measures

Stata output: Pooled regression model (OLS)

Stata output: Between effects regression model

Stata output: Regression model on individual means (manual version of the between effects regression model)

Stata output: Fixed effects panel model

Stata output: Random effects panel model

Stata output: Coefficients (b) and their standard errors (se) from the between effects model, the fixed effects panel model and the random effects panel model

Stata output: Coefficients F tests for the random effects panel model

Stata compact codebook for the BHPS extract of male full-time workers aged 2535 waves 110

Stata summary for the BHPS extract of male full-time workers aged 2535 waves 110

Stata output: Pooled cross-sectional model: usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110

Stata output: Variance inflation factors for the explanatory variables in the pooled cross-sectional model: usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110

Stata output: Pooled cross-sectional models of usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110 summary of coefficients (b) and their standard errors (se)

Stata output: xtdes male full-time workers aged 2535 waves 110

Stata output: Summary of panel information (xtsum) for usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110

Stata output: Between effects model: usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110

Stata output: Fixed effects panel model: usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110

Stata output: Random effects panel model: usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110

Stata output: Breusch and Pagan Lagrangian multiplier test random effects panel model: usual net pay () per month (adjusted for inflation) male full-time workers aged 2535 waves 110

Stata output: Coefficients (b) and their standard errors (se) from the pooled regression model, the between effects model, the fixed effects panel model and the random effects panel model usual net pay () per month (adjusted for inflation) male full-time workers aged 2535

Stata output: Compact codebook for the womens employment dataset

Stata output: Pooled logistic regression model wife employed

Stata output: Margins (adjusted predictions at means) for the pooled logistic regression model wife employed

Stata output: Pooled logistic regression model wife employed with robust standard errors

Stata output: Random effects logit panel model wife employed

Stata output: Fixed effects logit panel model wife employed

Stata output: Random effects probit panel model

Stata output: Margins (adjusted predictions at means) for the random effects probit model wife employed presented in Figure 46

An example of variable by case matrix

An example of a wide format dataset

An example of a long format dataset

Sample Stata syntax illustrating the construction of long and wide format datasets using the BHPS (and images of the data formats that are generated)

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