Revisiting Sample Size and Number of Parameter Estimates: Some Support for the N:q Hypothesis

By Dennis L. Jackson. 2003

1,159 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/81ef087303bcc581dd1bf3dda59c0972149895fd

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In Applied Psychology

In Experimental and Cognitive Psychology

In Management Science and Operations Research

Citations in the corpus

Cultural validation of the Maslach Burnout Inventory for Korean students (2011, background): “The minimum sample size in confirmatory factor analysis requires a rate of 10 cases per each free parameter ( Jackson 2003; Kline 2005).”
Use of structural equation modeling in operations management research: Looking back and forward ☆ (2006, methodology): “The lower end of both sample size and sample size to parameter estimate ratios are significantly smaller in the reviewed OM research than those studied by Jackson (2003), indicating that the OM literature may be highly susceptible to the negative outcomes reported in his study.”
A Brief Guide to Structural Equation Modeling (2006, background): “For example, Jackson (2001, 2003) found only a small effect of sample size on model fit when he tested the hypothesis that an inadequate sample would result in poor-fitting models.”
Structural Equations Modeling: Fit Indices, Sample Size, and Advanced Topics (2010, background): “There was some thinking that strong, clean measures (as defined by the number of variables loading on each factor and the factors' reliabilities) would be somewhat compensatory for sample size, but while the number of variables per factor has an effect on improving fit statistics, its effect is mode”
The problem of empirical redundancy of constructs in organizational research: An empirical investigation (2010, methodology): “Traditionally, correction for the bias is made either by applying SEM (or CFA) or by using a reliability coefficient with the disattenuation formula (DeShon, 1998; Schmidt et al., 2003).”
Energy at work: A measurement validation and linkage to unit effectiveness. (2012, background): “, repeated measures) design would allow one to address questions like “Does an upward change in productive energy lead to increased performance or do performance changes increase/decrease productive energy?” Alternatively, support may be found for reciprocal relationships. Consequently, we advocate ”
Creativity and Intelligence Revisited: A Latent Variable Analysis of Wallach and Kogan (1965) (2008, background): “Although a larger sample is always desirable, a sample of 151 suffices if the measured variables relate strongly to the latent variable and if the model has relatively few parameters (see Jackson, 2001, 2003).”
The moderating role of gender on entrepreneurial intentions: A TPB perspective (2015, neutral): “In this context, an oft-used rule is what is known as the “N:q rule” (Jackson, 2003), according to which between 10 and 20 cases are needed for each estimated parameter.”
A Note on Sample Size and Solution Propriety for Confirmatory Factor Analytic Models (2013, background): “…endorsed to varying degrees (Bollen, 1989; Herzog & Boomsma, 2009; Kim & Bentler, 2006; Kline, 2005; Marsh et al., 1988; Mueller, 1996; Nevitt & Hancock, 2004; Ullman, 1996); however, recommendations based on n/q have also been questioned (Jackson, 2003, 2007; Marsh, Hau, Balla, & Grayson, 1998).”
Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018, methodology): “On the other hand, even for latent variable models with continuous outcomes and normal distribution using ML Jackson (2003) suggested a sample-size to parameters ratio of 20:1 or at least 10:1.”
Factors Influencing Organic Food Purchase Intention in Developing Countries and the Moderating Role of Knowledge (2019, background): “independent variable that is not counted by other independent variables in the model [80].”