Research-Problem Validity in Primary Research: Precision and Transparency in Characterizing Past Knowledge

Abstract

Four validity types evaluate the approximate truth of inferences communicated by primary research. However, current validity frameworks ignore the truthfulness of empirical inferences that are central to research-problem statements. Problem statements contrast a review of past research with other knowledge that extends, contradicts, or calls into question specific features of past research. Authors communicate empirical inferences, or quantitative judgments, about the frequency (e.g., “few,” “most”) and variability (e.g., “on the one hand,” “on the other hand”) in their reviews of existing theories, measures, samples, or results. We code a random sample of primary research articles and show that 83% of quantitative judgments in our sample are vague and do not have a transparent origin, making it difficult to assess their validity. We review validity threats of current practices. We propose that documenting the literature search, reporting how the search was coded, and quantifying the search results facilitates more precise judgments and makes their origin transparent. This practice enables research questions that are more closely tied to the existing body of knowledge and allows for more informed evaluations of the contribution of primary research articles, their design choices, and how they advance knowledge. We discuss potential limitations of our proposed framework.

The Influence Of Competence and Compensation On The Performance Of Army Polytechnic Teaching Staff: Mediated By Work Motivation

“…Meanwhile, reliability testing is conducted using the Cronbach alpha formula. A measurement item is considered reliable if its Cronbach alpha value is greater than 0.6 (Schweinsberg, Thau, and Pillutla, 2023).…”