Measuring minds across generations: a theoretical framework on psychometric validity, interpretive frameworks, and contextual norming in cross-cohort psychological research.

内容摘要

Psychological research increasingly compares mental health outcomes across generations, with studies reporting elevated anxiety and depression in Generation Z and Alpha. Current instruments, interpretive frameworks, and normative standards were developed using language and contextual assumptions from earlier cohorts developing in distinct technological, economic, and social ecosystems. This Hypothesis and Theory article proposes to (i) evaluate measurement validity and interpretive framework issues in cross-generational research, (ii) analyze how contexts shape construct meaning across cohorts, (iii) examine artificial intelligence's impact on Generation Alpha assessment, and (iv) develop methodological frameworks for generation-specific assessment. This manuscript presents a Hypothesis and Theory contribution. We conducted a broad theoretical analysis drawing on literature identified through PubMed, PsycINFO, and Web of Science using the terms "generational differences," "measurement invariance," "cohort effects," "differential item functioning," "psychological assessment," and related terms. Articles were selected based on theoretical relevance to construct validity, psychometric principles, and generational context. Selection followed expert judgment rather than a formal systematic protocol, consistent with the Hypothesis and Theory format. Current instruments appear to suggest systematic linguistic drift, with identical items potentially measuring different constructs across generations. Interpretive frameworks fail to account for context-dependent meanings, leading to identical scores representing different psychological realities. Economic transformations reshaped how achievement is defined and assessed across cohorts, with each generation calibrating success relative to the material and social conditions of its formative years. Technology integration from analog to AI-integrated cognition may require adapted assessment frameworks. Generation-specific pathological presentations may be emerging, with corresponding diagnostic categories absent. Differential Item Functioning analyses reveal items operate differently across cohorts, yet uniform interpretations persist. Clinical cut-offs may misclassify generations when responses reflect adaptive functioning. Cross-generational comparisons using standardized instruments with uniform frameworks may lack fundamental validity. These findings support calls for generation-appropriate assessment, context-sensitive interpretation, cohort-specific norms, and mandatory testing of measurement invariance before cross-generational comparisons.