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Thesis (M Ed (Human Resources Studies)) --University of South Australia, 1994

Thesis (Ph.D)--University of Wollongong, 2003. / Typescript. Includes bibliographical references: leaf 366-409.

Measures of psychological attributes abound in the social sciences as much as measures of physical properties do in the physical sciences. However, there are crucial differences between the scientific underpinning of measurement. While measurement in the physical sciences is supported by empirical evidence that demonstrates the quantitative nature of the property assessed, measurement in the social sciences is, in large part, made possible only by a vague, discretionary definition of measurement that places hardly any restrictions on empirical data. Traditional psychometric analyses fail to address the requirements of measurement as defined more rigorously in the physical sciences. The construct definitions do not allow for testable predictions; and content validity becomes a matter of highly subjective judgment. In order to improve measurement of psychological attributes, it is suggested to, first, readopt the definition of measurement in the physical sciences; second, to devise an elaborate theory of the construct to be measured that includes the hypothesis of a quantitative attribute; and third, to test the data for the structure implied by the hypothesis of quantity as well as predictions derived from the theory of the construct. (author's abstract)

The dissertation presents evidence on the measurement properties of self-report items in contemporary organizational contexts (Podsakoff & Organ, 1986). Operationally, the dissertation adopts a construct representation approach to construct validity, defined by the response processes engaged for measurement performance in trait assessment (AERA, 2014; Embretson, 1983). For example, self-report measures are known to be affected by a variety of variables, such as semantic and referent features (Cermac & Craik, 1979; Kelly, 1955) and design factors that impact cognitive context (Stone, et al, 2000; The Science of Self-Report). In turn, the response processes impacts the external correlations (Embretson, 2007). To the extent that semantic-referent features and design factors are construct-irrelevant, reduced external correlations can be expected. This dissertation presents evidence from a qualitative review of self-report future time perspective (FTP) instruments across organizational and retirement contexts. A quantitative review compares external correlates of the two instruments. A retrospective-observational study benchmarks the psychometric properties of Carstensen's self-report instrument using modern latent-variable modeling (item-response theory [IRT]). Structural equation modeling (SEM) is further used to test for moderating effects of subjective life expectancy (SLE) on latent predictors of FTP and retirement plans. Evidence from a '3 x 2' mixed-subjects experimental design is also presented indicating the effects of subjective life expectancy (SLE) on measurement error in personality factors, FTP, and retirement plans. Discussion centers on advancing measurement paradigms in psychological and education research, as well as -more generally- adopting an integrated perspective of construct validity for advancing and evaluating substantive research.

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Construct truncation can be defined as the failure to capture variation along the entire continuum of a construct reliably. It can occur due to suboptimal person selection or due to suboptimal item selection. In this thesis, I used a series of simulation studies coupled with real data examples to characterise the consequences of construct truncation on the inferences made in empirical research. The analyses suggested that construct truncation has the potential to result in significant distortions of substantive conclusions. Based on these analyses I developed recommendations for anticipating the circumstances under which construct truncation is likely to be problematic, identifying it when it occurs, and mitigating its adverse effects on substantive conclusions drawn from affected data.