This paper aims to unsettle assumptions of generalizability and representativeness in quantitative research using queer framings and positionalities. We argue that generalizability and representativeness are tools of supremacist dominance that reinforce harmful and essentialist categories of identities for the false purpose of statistical…

Students often believe that statistical significance is the only determinant of whether a quantitative result is "important." In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction,…

Many studies in mathematics education research occur with a nonrepresentative sample and are never replicated. To challenge this paradigm, I designed a large-scale study evaluating student conceptions in group theory that surveyed a national, representative sample of students. By replicating questions previously used to build theory around student…

Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…

Quantitative research methods produce factual, reliable, and generalizable data. Qualitative methods generate rich, detailed, valid process data. Ways to integrate them include (1) using qualitative methods to develop quantitative instruments; (2) using qualitative methods to explain quantitative findings; (3) using quantitative methods to…

A new method is presented for determining the minimum number of observations per subject needed to achieve a specific generalizability coefficient. The method, consisting of a branch-and-bound algorithm, enables an investigator to specify an acceptable threshold for generalizability coefficients. (SLD)

This chapter serves as a guide for quantitative researchers who seek to approach their research questions critically.

Addresses generalizability theory (GT), which offers a flexible framework for assessing dependability of measurement. GT allows for consideration of multiple sources of error, allowing investigators to assess the overall impact of measurement error. Illustrative analyses demonstrate the special advantages of GT for planning studies in which…

In education research, a polar distinction is frequently made to describe and produce different kinds of research: "quantitative" versus "qualitative." In this article, the authors argue against that polarization and the associated polarization of the "subjective" and the "objective," and they question the attribution of generalizability to only…

Two chapters of this issue consider critical multiplism as a research strategy with links to meta analysis and generalizability theory. The unifying perspective it can provide for quantitative and qualitative evaluation is discussed. The third chapter explores meta analysis as a way to improve causal inferences in nonexperimental data. (SLD)

Description of common errors found in the statistical methodologies of research carried out by librarians, focuses on sampling and generalizability. The discussion covers the need to either adapt library research to the statistical abilities of librarians or to educate librarians in the proper use of statistics. (15 references) (CLB)

Results from a performance assessment in which 68 high school students wrote essays support the use of latent variable modeling for estimating reliability, concurrent validity, and generalizability of a scoring rubric. The latent variable modeling approach overcomes the limitations of certain conventional statistical techniques in handling…