Alternative Model-Based and Design-Based Frameworks for Inference From Samples to Populations: From Polarization to Integration

A model-based framework, due originally to R. A. Fisher, and a design-based framework, due originally to J. Neyman, offer alternative mechanisms for inference from samples to populations. We show how these frameworks can utilize different types of samples (nonrandom or random vs. only random) and al...

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Bibliographic Details
Published in:Multivariate behavioral research 2009-11, Vol.44 (6), p.711-740
Main Author: Sterba, Sonya K.
Format: Article
Language:English
Subjects:
Behavioral psychology
High Schools
Population
Psychological aspects
Psychology
Sampling
Statistical Inference
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Summary:A model-based framework, due originally to R. A. Fisher, and a design-based framework, due originally to J. Neyman, offer alternative mechanisms for inference from samples to populations. We show how these frameworks can utilize different types of samples (nonrandom or random vs. only random) and allow different kinds of inference (descriptive vs. analytic) to different kinds of populations (finite vs. infinite). We describe the extent of each framework's implementation in observational psychology research. After clarifying some important limitations of each framework, we describe how these limitations are overcome by a newer hybrid model/design-based inferential framework. This hybrid framework allows both kinds of inference to both kinds of populations, given a random sample. We illustrate implementation of the hybrid framework using the High School and Beyond data set.
ISSN:0027-3171
1532-7906
DOI:10.1080/00273170903333574