What’s in a name? Psychology’s ever evasive construct

Slaney, Kathleen L.; Racine, Timothy P.

doi:10.1016/j.newideapsych.2011.02.003

Cited by 42 publications

(25 citation statements)

References 25 publications

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“…From a linguistic and philosophical viewpoint, it is fairly safe to dismiss that proposition (Slaney & Racine, , p. 10), but the inclusion of LSA_IS and LSA_PSYCH allows an empirical test of the question. Theoretically speaking, language does not in itself contain truth but only allows us to express propositions that may be true or not.…”

Section: Text Algorithmsmentioning

confidence: 99%

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

Arnulf

Dysvik

Larsen

2018

Human Resource Dev Quarterly

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This is a methodological presentation of the relationship between semantics and survey statistics in human resource development (HRD) research. This study starts with an introduction to the semantic theory of survey response (STSR) and proceeds by offering a guided approach to conducting such analyses. The reader is presented with two types of semantic algorithms and a brief overview of how they are calculated and how they can be accessed by interested researchers. Subsequently, we use semantic data to reanalyze a previously published study on the relationships between perceptions of a trainee program, intrinsic motivation, and work outcomes. The semantic algorithms can explain between 31 and 55% of the variation in the observed correlations. This article shows how the statistical models originally used to explore the survey data can be replicated using semantics either alone or as an identifiable source of variation in the data. All the steps are presented in detail, and the datasets as well as the statistical syntax necessary to perform the analyses are made available to the readers. Implications for methodology and the improvement of predictive validity in HRD research are discussed.

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Section: Text Algorithmsmentioning

confidence: 99%

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

Arnulf

Dysvik

Larsen

2018

Human Resource Dev Quarterly

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“…In his original work on response scales, Likert (1932) was insistent that the object of measurement should be attitude strength. If the emerging statistics are caused by other psychological operations, such as the cognitive processing of items, it would cast doubt about their nature as measurements and pose a problem for interpretation (Lamiell, 2013;Slaney & Racine, 2013). On the other hand, the possibility to explore and measure these cognitive operations might open new applications in survey research, possibly improving the predictive validity of such measurements and expanding the use of such instruments.…”

mentioning

confidence: 99%

The failing measurement of attitudes: How semantic determinants of individual survey responses come to replace measures of attitude strength

et al. 2018

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The traditional understanding of data from Likert scales is that the quantifications involved result from measures of attitude strength. Applying a recently proposed semantic theory of survey response, we claim that survey responses tap two different sources: a mixture of attitudes plus the semantic structure of the survey. Exploring the degree to which individual responses are influenced by semantics, we hypothesized that in many cases, information about attitude strength is actually filtered out as noise in the commonly used correlation matrix. We developed a procedure to separate the semantic influence from attitude strength in individual response patterns, and compared these results to, respectively, the observed sample correlation matrices and the semantic similarity structures arising from text analysis algorithms. This was done with four datasets, comprising a total of 7,787 subjects and 27,461,502 observed item pair responses. As we argued, attitude strength seemed to account for much information about the individual respondents. However, this information did not seem to carry over into the observed sample correlation matrices, which instead converged around the semantic structures offered by the survey items. This is potentially disturbing for the traditional understanding of what survey data represent. We argue that this approach contributes to a better understanding of the cognitive processes involved in survey responses. In turn, this could help us make better use of the data that such methods provide.

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“…This implies that the construct (i.e., stereotype) pre-existing manifestation of attractiveness bias operates outside of the agent's awareness insofar as subsequent attributions of positive characteristics may be seen as a post hoc rationalization. Given this implication, criticisms of causal inefficacy are ill advised because they may be leveled at the authors' own position if it is considered problematic for a causally implicated construct to pre-exist the motivation it is hypothesized to generate (see also Slaney & Racine 2013). Similar to a stereotype, the authors write of attractiveness bias as the manifestation of a psychological adaptation, which ancestrally conferred the benefit of surrounding its possessor with an increased number of potential fertile mates.…”

Section: Sebastian Hafenbrädl and Jason Danamentioning

confidence: 99%

Moving forward with interdisciplinary research on attractiveness-related biases

2017

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In our response, we review and address the comments on our target article made in the 25 commentaries. First, we review and discuss the commentaries that recognized the value of our approach, accepted the main premises and conclusions of our target article, and suggested further avenues for research on attractiveness-related biases. We then respond to commentators who either misinterpreted some parts of our target article or made statements with which we disagree. These commentaries provided us with an opportunity to clarify some aspects of our target article, for example, the fact that we address both the functional significance of attractiveness-related biases and their underlying mechanisms. We provide a rebuttal to two commentaries, in which we are accused of poor scholarship. We conclude our response by addressing two commentaries that discussed the societal implications of the occurrence of attractiveness-related biases in the labor market by briefly discussing the relationship between scientific research and social policy.

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What’s in a name? Psychology’s ever evasive construct

Cited by 42 publications

References 25 publications

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

The failing measurement of attitudes: How semantic determinants of individual survey responses come to replace measures of attitude strength

Moving forward with interdisciplinary research on attractiveness-related biases

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