Literature DB >> 24425722

Cutting-edge statistical methods for a life-course approach.

Kristen L Bub1, Larissa K Ferretti.   

Abstract

Advances in research methods, data collection and record keeping, and statistical software have substantially increased our ability to conduct rigorous research across the lifespan. In this article, we review a set of cutting-edge statistical methods that life-course researchers can use to rigorously address their research questions. For each technique, we describe the method, highlight the benefits and unique attributes of the strategy, offer a step-by-step guide on how to conduct the analysis, and illustrate the technique using data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development. In addition, we recommend a set of technical and empirical readings for each technique. Our goal was not to address a substantive question of interest but instead to provide life-course researchers with a useful reference guide to cutting-edge statistical methods.

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Year:  2014        PMID: 24425722      PMCID: PMC3884099          DOI: 10.3945/an.113.004739

Source DB:  PubMed          Journal:  Adv Nutr        ISSN: 2161-8313            Impact factor:   8.701


  10 in total

1.  False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant.

Authors:  Joseph P Simmons; Leif D Nelson; Uri Simonsohn
Journal:  Psychol Sci       Date:  2011-10-17

2.  Best practices in quantitative methods for developmentalists.

Authors:  Kathleen McCartney; Margaret R Burchinal; Kristen L Bub
Journal:  Monogr Soc Res Child Dev       Date:  2006

Review 3.  Latent variable path analysis in clinical research: a beginner's tour guide.

Authors:  R B Kline
Journal:  J Clin Psychol       Date:  1991-07

4.  Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.

Authors:  R B D'Agostino
Journal:  Stat Med       Date:  1998-10-15       Impact factor: 2.373

Review 5.  Estimating causal effects from large data sets using propensity scores.

Authors:  D B Rubin
Journal:  Ann Intern Med       Date:  1997-10-15       Impact factor: 25.391

6.  Comparative fit indexes in structural models.

Authors:  P M Bentler
Journal:  Psychol Bull       Date:  1990-03       Impact factor: 17.737

7.  Investigating onset, cessation, relapse, and recovery: why you should, and how you can, use discrete-time survival analysis to examine event occurrence.

Authors:  J B Willett; J D Singer
Journal:  J Consult Clin Psychol       Date:  1993-12

8.  The effects of universal pre-K on cognitive development.

Authors:  William T Gormley; Ted Gayer; Deborah Phillips; Brittany Dawson
Journal:  Dev Psychol       Date:  2005-11

9.  Maternal employment and child development: a fresh look using newer methods.

Authors:  Jennifer L Hill; Jane Waldfogel; Jeanne Brooks-Gunn; Wen-Jui Han
Journal:  Dev Psychol       Date:  2005-11

10.  Some controls control too much.

Authors:  Nora S Newcombe
Journal:  Child Dev       Date:  2003 Jul-Aug
  10 in total
  2 in total

1.  Using Nurse Ratings of Physician Communication in the ICU To Identify Potential Targets for Interventions To Improve End-of-Life Care.

Authors:  Kathleen J Ramos; Lois Downey; Elizabeth L Nielsen; Patsy D Treece; Sarah E Shannon; J Randall Curtis; Ruth A Engelberg
Journal:  J Palliat Med       Date:  2015-12-18       Impact factor: 2.947

2.  Cohort Profile: The Swedish Longitudinal Occupational Survey of Health (SLOSH).

Authors:  Linda L Magnusson Hanson; Constanze Leineweber; Viktor Persson; Martin Hyde; Töres Theorell; Hugo Westerlund
Journal:  Int J Epidemiol       Date:  2018-06-01       Impact factor: 7.196

  2 in total

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