Measuring the menu, not the food: "psychometric" data may instead measure "lingometrics" (and miss its greatest potential).

This is a review of a range of empirical studies that use digital text algorithms to predict and model response patterns from humans to Likert-scale items, using texts only as inputs. The studies show that statistics used in construct validation is predictable on sample and individual levels, that this happens across languages and cultures, and that the relationship between variables are often semantic instead of empirical. That is, the relationships among variables are given a priori and evidently computable as such. We explain this by replacing the idea of "nomological networks" with "semantic networks" to designate computable relationships between abstract concepts. Understanding constructs as nodes in semantic networks makes it clear why psychological research has produced constant average explained variance at 42% since 1956. Together, these findings shed new light on the formidable capability of human minds to operate with fast and intersubjectively similar semantic processing. Our review identifies a categorical error present in much psychological research, measuring representations instead of the purportedly represented. We discuss how this has grave consequences for the empirical truth in research using traditional psychometric methods.

"Never mind the fine print": The interaction of semantics with attitude strength beliefs on corporate cover-ups.

This study examined the interaction between attitude strength and the cognitive constraints imposed by the semantic properties of measurement items. It made use of digital algorithms and built on the semantic theory of survey response (STSR), examining how people who hold strong beliefs about contemporary issues violate cognitive constraints in expressing strong attitudes. We examined the beliefs people hold concerning attempts to hide, or cover up, information about organisational scandals. Beliefs in cover-ups are related to beliefs in conspiracy theories in that they tend to overrate cues of wrongdoing, disregarding information that may render a more nuanced picture of events. We obtained responses from 405 people who rated their self-images and personal strengths, and explored how these variables influenced the respondents' beliefs in corporate cover-ups. Using latent semantic analysis (LSA), we differentiated between attitude strength and cognitive processing of the survey items. Results indicated that people with inflated self-images tended to override cognitive cues in endorsing extreme types of cover-ups such as removing accusers. Conversely, people who parse the information more carefully had a more tempered view on cover-ups and were more inclined to believe in subtle forms such as twisting stories.

Dispositional and ideological factor correlate of conspiracy thinking and beliefs.

This study explored how the Big Five personality traits, as well as measures of personality disorders, are related to two different measures of conspiracy theories (CTs)The two measures correlated r = .58 and were applied to examine generalisability of findings. We also measured participants (N = 397) general knowledge levels and ideology in the form of religious and political beliefs. Results show that the Big Five and ideology are related to CTs but these relationships are generally wiped out by the stronger effects of the personality disorder scales. Two personality disorder clusters (A and B) were significant correlates of both CT measures, in both cases accounting for similar amounts of variance (20%). The personality disorders most predictive of conspiracy theories were related to the A cluster, characterized by schizotypal symptoms such as oddities of thinking and loose associations. These findings were corroborated by an additional analysis using Latent Semantic Analysis (LSA). LSA demonstrated that the items measuring schizotypal and related symptoms are cognitively related to both our measures of CTs. The implications for the studying of CTs is discussed, and limitations are acknowledged.

Is psychological science progressing? Explained variance in PsycINFO articles during the period 1956 to 2022.

We aimed to numerically assess the progress of modern psychological science. Average explained variance in 1565 included articles was 42.8 percent, and this was constant during 1956 to 2022. We explored whether this could be explained by a combination of methodological conventions with the semantic properties of the involved variables. Using latent semantic analysis (LSA) on a random sample of 50 studies from the 1,565, we were able to replicate the possible semantic factor structures of 205 constructs reported in the corresponding articles. We argue that the methodological conventions pertaining to factor structures will lock the possible explained variance within mathematical constraints that will make most statistics cluster around 40 percent explained variance. Hypotheses with close to 100 percent semantic truth value will never be part of any assumed empirical study. Nor will hypotheses approaching zero truth value. Hypotheses with around 40 percent truth value will probably be experienced as empirical and plausible and, consequently, as good candidates for psychological research. Therefore, to the extent that the findings were indeed produced by semantic structures, they could have been known without collecting data. Finally, we try to explain why psychology had to abandon an individual, causal method and switch to studying whether associations among variables at the group level differ from chance. Psychological processes take place in indefinitely complex and irreversibly changing contexts. The prevalent research paradigm seems bound to producing theoretical statements that explain each other to around 40%. Any theoretical progress would need to address and transcend this barrier.

Unobtrusive measures of prejudice: Estimating percentages of public beliefs and behaviours.

This study was concerned with how accurate people are in their knowledge of population norms and statistics concerning such things as the economic, health and religious status of a nation and how those estimates are related to their own demography (e.g age, sex), ideology (political and religious beliefs) and intelligence. Just over 600 adults were asked to make 25 population estimates for Great Britain, including religious (church/mosque attendance) and economic (income, state benefits, car/house ownership) factors as well as estimates like the number of gay people, immigrants, smokers etc. They were reasonably accurate for things like car ownership, criminal record, vegetarianism and voting but seriously overestimated numbers related to minorities such as the prevalence of gay people, muslims and people not born in the UK. Conversely there was a significant underestimation of people receiving state benefits, having a criminal record or a private health insurance. Correlations between select variables and magnitude and absolute accuracy showed religiousness and IQ most significant correlates. Religious people were less, and intelligent people more, accurate in their estimates. A factor analysis of the estimates revealed five interpretable factors. Regressions were calculated onto these factors and showed how these individual differences accounted for as much as 14% of the variance. Implications and limitations are acknowledged.

Limits of a Second Language: Native and Second Languages in Management Team Communication.

Cultural differences in speech acts are common challenges in management involving Chinese and Western managers. Comparing four groups - Native-speaking Chinese, English-speaking Chinese, Chinese-speaking Westerners, and non-Chinese- speaking Westerners, we assessed the effects of language and ethnicity on the ability to predict communication obstacles in a management team scenario. Bilingual subjects were less likely to be influenced by ethnic biases. Still, bilinguals were not more likely to adjust their metacognitions about communication toward those of the native speakers. The study creates a link between management, cognition and linguistics, as well as having consequences for the study of metacognition in cross-cultural management.

Dark- and bright-side reactions to government advice about Covid-19, and a test of a method to moderate such reactions.

The aim of this paper was to study how individual differences in personality shape reactions to authorities' health advice during the COVID-19 pandemic and how such reactions can be modified. Three studies, with between 249 and 407 participants, investigated this. Study 1 used a longitudinal design, and included measures of personality (NEO-FFI3, SCATI), political orientation, age and gender as predictors of reactions toward COVID-19 advice and regulations. Studies 2 and 3 were randomised experiments testing effects of principles for behaviour modification on such reactions. In study 1, we found that being female, older, or having liberal political views, as well as neuroticism, agreeableness and conscientiousness in the higher ranges, were associated with constructive reactions. Externalising personality disorders were related to opposite reactions. In study 2, we found that the experimental instructions had a significant positive impact on such reactions. These results were replicated in study 3. Implications and limitations are discussed.

Are Chinese Teams Like Western Teams? Indigenous Management Theory to Leapfrog Essentialist Team Myths.

Our study analyzes a gap in research on Chinese and Western management teams, based on a broad literature review. We claim that prevalent theoretical perspectives in the management team literature might be biased toward a Western-centric view of team dynamics. This obscures alternative ways of understanding top teams encompassing Chinese cultural traditions. We outline how an essentialist team conceptualization leads to a paradox consisting of three mutually contradicting myths. Myth 1 implies that Western groups of managers comply with theoretically "ideal" team processes and characteristics. Myth 2 derives from research literature on Chinese teams claiming that team features are assumed absent or weak in China due to cultural particularities. Paradoxically, the same research tradition constructs another third myth by reporting that Chinese teams successfully comply with the Western ideal team model. The three coexisting myths point to a theoretical confounding of contextual mediators in team processes. We discuss how indigenous Chinese leadership theory and Chinese systems of philosophy give Chinese teams access to distinct and effective team processes to reach high-performance outcomes. This paper aims to open the rich possibilities of Chinese management and team practices to the cross-cultural context, and on return to novel understanding of Western teams beyond traditional essentialist theory anchors.

The Priest, the Sex Worker, and the CEO: Measuring Motivation by Job Type.

This study uses latent semantic analysis (LSA) to explore how prevalent measures of motivation are interpreted across very diverse job types. Building on the Semantic Theory of Survey Response (STSR), we calculate "semantic compliance" as the degree to which an individual's responses follow a semantically predictable pattern. This allows us to examine how context, in the form of job type, influences respondent interpretations of items. In total, 399 respondents from 18 widely different job types (from CEOs through lawyers, priests and artists to sex workers and professional soldiers) self-rated their work motivation on eight commonly applied scales from research on motivation. A second sample served as an external evaluation panel (n = 30) and rated the 18 job types across eight job characteristics. Independent measures of the job types' salary levels were obtained from national statistics. The findings indicate that while job type predicts motivational score levels significantly, semantic compliance as moderated by job type job also predicts motivational score levels usually at a lesser but significant magnitude. Combined, semantic compliance and job type explained up to 41% of the differences in motional score levels. The variation in semantic compliance was also significantly related to job characteristics as rated by an external panel, and to national income levels. Our findings indicate that people in different contexts interpret items differently to a degree that substantially affects their score levels. We discuss how future measurements of motivation may improve by taking semantic compliance and the STSR perspective into consideration.

Culture Blind Leadership Research: How Semantically Determined Survey Data May Fail to Detect Cultural Differences.

Likert scale surveys are frequently used in cross-cultural studies on leadership. Recent publications using digital text algorithms raise doubt about the source of variation in statistics from such studies to the extent that they are semantically driven. The Semantic Theory of Survey Response (STSR) predicts that in the case of semantically determined answers, the response patterns may also be predictable across languages. The Multifactor Leadership Questionnaire (MLQ) was applied to 11 different ethnic samples in English, Norwegian, German, Urdu and Chinese. Semantic algorithms predicted responses significantly across all conditions, although to varying degree. Comparisons of Norwegian, German, Urdu and Chinese samples in native versus English language versions suggest that observed differences are not culturally dependent but caused by different translations and understanding. The maximum variance attributable to culture was a 5% unique overlap of variation in the two Chinese samples. These findings question the capability of traditional surveys to detect cultural differences. It also indicates that cross-cultural leadership research may risk lack of practical relevance.

Semantic algorithms can detect how media language shapes survey responses in organizational behaviour.

Research on sensemaking in organisations and on linguistic relativity suggests that speakers of the same language may use this language in different ways to construct social realities at work. We apply a semantic theory of survey response (STSR) to explore such differences in quantitative survey research. Using text analysis algorithms, we have studied how language from three media domains-the business press, PR Newswire and general newspapers-has differential explanatory value for analysing survey responses in leadership research. We projected well-known surveys measuring leadership, motivation and outcomes into large text samples from these three media domains significantly different impacts on survey responses. Business press language was best in explaining leadership-related items, PR language best at explaining organizational results and "ordinary" newspaper language seemed to explain the relationship among motivation items. These findings shed light on how different public arenas construct organizational realities in different ways, and how these differences have consequences on methodology in research on leadership.

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

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.

Predicting survey responses: how and why semantics shape survey statistics on organizational behaviour.

Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60-86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain.

What's measured is not necessarily managed: cognitive contingencies of organizational measurement.

Organizational measurement is necessary for managing incremental improvements or change efforts. Some have argued that indicators of financial performance measure the past and should be joined by "lead" measures such as management development and customer relations, assumed to be drivers of performance. Approaches of "lead" measures have met criticism for not contributing to operational management. The present is an empirical case study where measures from a customer satisfaction survey and a management assessment instrument were analyzed and compared to actual measures of financial performance. The effects of measures obtained seem to depend on the interpretations and reflection taking place in the organization. The pursuit of "lead" indicators did not seem to drive results. Counter-intuitively, higher scores on these measures were related to poorer financial performance. The explication of and reflection on the underlying dynamic seemed to turn the development in a profitable direction.