Tips for writing (and reading) methodological articles
Bibliographic Data
| ID | 16868375 |
|---|---|
| Authors | Scott E Maxwell (0000-0001-8067-7027, University of Notre Dame), David A Cole (0000-0002-7535-1230) |
| Year | 1995 |
| Volume | 118 |
| Issue | 2 |
| Pages | 193-198 |
| Publication date | 1995-09-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Psychological Bulletin (JOURNAL) |
| Journal identifiers | ISSN: 0033-2909 • E-ISSN: 1939-1455 |
| Publisher | American Psychological Association (APA) (PUBLISHER) |
| DOI | 10.1037/0033-2909.118.2.193 |
| OpenAlex | W2013262158 |
| Language | EN |
| Citations received | 2 |
| References cited | 11 |
One reason many methodological articles are not very intelligible to their readers is because the content is often inherently difficult. However, a contributing factor in some cases is the tacit assumption that rules of good writing cease to apply when writing about statistics. The authors of this article argue that good writing becomes even more important as the content of the article becomes more complex. Furthermore, they believe that additional rules pertain to writing methodological articles and highlight various ways that methodological article authors can make their work more accessible (and less painful) to researchers who are not methodological specialists. The authors also suggest how nonspecialists can most effectively approach the task of reading a quantitative article. For some psychologists, writing a methodological article is a fine art of obfuscating needlessly tedious and complex trivia. For others, reading a methodological article ranks right up there with a visit to the dentist's office. Many methodological articles, however, are not accessible to their intended readers, not necessarily because the material is so sophisticated but because the presentation of the material is so obtuse. Our goal in this article
Epistemology · Focus (optics) · Linguistics · Presentation (obstetrics) · Reading (process) · Task (project management) · Computer Science · Medicine · Philosophy · Psychology · Sensory Analysis and Statistical Methods · Statistics Education and Methodologies
Utility of confirmatory factor analysis in test validation research.
Optimal allocation of assessment time in randomized pretest-posttest designs
Testing the null hypothesis in meta-analysis
A comparison of methods for increasing power in randomized between-subjects designs
Using covariance structure analysis to detect correlates and predictors of individual change over time
The use of causal indicators in covariance structure models
Nonparametric estimators of effect size in meta-analysis
A more realistic look at the robustness and Type II error properties of the t test to departures from population normality
Manova method for analyzing repeated measures designs
The unicorn, the normal curve, and other improbable creatures
Application of hierarchical linear models to assessing change
| Citations per year | 0.08 |
|---|---|
| Citation span | 2000 - 2018 (19) |
| Highly cited | No |