Intermediate states of literariness

Abstract This study utilizes texts which sit between the literary and non-literary to explore the outcomes and mechanisms of literariness. Literariness can be activated by (a) linguistic foregrounding and (b) paratextual specification. In a 2 × 2 design, manipulated versions of two soldier narratives were produced (poetry/fiction; poetry/fact; prose/fiction; prose/poetry). 215 participants were randomly assigned to read one of the textual versions and respond to rating scales dealing with perception of textual features, empathy, sympathy, and cognitive perspective-taking. The results show that poetic form elicits significantly higher ratings for empathy and sympathy and that paratextual information specifying that a text is factual elicits significantly higher ratings for empathy and cognitive perspective-taking. Two structural equation models were defined: (a) a literariness model and (b) a factual accuracy model. The results suggest an additive dual model of processing in which both poetic form and factual definition contribute to outcomes characteristic of literariness. These results offer some support for the hypotheses of the Neuro-Cognitive Poetics Model proposed by (Jacobs, 2011).

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A Comparative Assessment of Graphic and 0–10 Rating Scales Used to Measure Entrepreneurial Competences

Axioms ◽

10.3390/axioms9010021 ◽

2020 ◽

Vol 9 (1) ◽

pp. 21 ◽

Cited By ~ 1

Author(s):

Laura Vall-Llosera ◽

Salvador Linares-Mustarós ◽

Andrea Bikfalvi ◽

Germà Coenders

Keyword(s):

Structural Equation ◽

Rating Scales ◽

Structural Equation Models ◽

Rating Scale ◽

Business Students ◽

Comparative Assessment ◽

Computer Assisted ◽

Measurement Instruments ◽

Production Scale ◽

Line Production

This article presents an empirical comparative assessment of the measurement quality of two instruments commonly used to measure fuzzy characteristics in computer-assisted questionnaires: a graphic scale (a line production scale using a slider bar) and an endecanary scale (a 0–10 rating scale using radio buttons). Data are analyzed by means of multitrait–multimethod models estimated as structural equation models with a mean and covariance structure. For the first time in such research, the results include bias, valid variance, method variance, and random error variance. The data are taken from a program that assesses entrepreneurial competences in undergraduate Economics and Business students by means of questionnaires administered on desktop computers. Neither of the measurement instruments was found to be biased with respect to the other, meaning that their scores are comparable. While both instruments achieve valid and reliable measurements, the reliability and validity are higher for the endecanary scale. This study contributes to the still scarce literature on fuzzy measurement instruments and on the comparability and relative merits of graphic and discrete rating scales on computer-assisted questionnaires.

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Measuring the Big Five in Self-Report and Other Ratings

European Journal of Psychological Assessment ◽

10.1027//1015-5759.16.1.31 ◽

2000 ◽

Vol 16 (1) ◽

pp. 31-43 ◽

Cited By ~ 24

Author(s):

Claudio Barbaranelli ◽

Gian Vittorio Caprara

Keyword(s):

Big Five ◽

Structural Equation ◽

Structural Equation Models ◽

Error Variance ◽

The Self ◽

Self Report ◽

Sources Of Information ◽

Third Person ◽

Fit Indexes ◽

Response Modes

Summary: The aim of the study is to assess the construct validity of two different measures of the Big Five, matching two “response modes” (phrase-questionnaire and list of adjectives) and two sources of information or raters (self-report and other ratings). Two-hundred subjects, equally divided in males and females, were administered the self-report versions of the Big Five Questionnaire (BFQ) and the Big Five Observer (BFO), a list of bipolar pairs of adjectives ( Caprara, Barbaranelli, & Borgogni, 1993 , 1994 ). Every subject was rated by six acquaintances, then aggregated by means of the same instruments used for the self-report, but worded in a third-person format. The multitrait-multimethod matrix derived from these measures was then analyzed via Structural Equation Models according to the criteria proposed by Widaman (1985) , Marsh (1989) , and Bagozzi (1994) . In particular, four different models were compared. While the global fit indexes of the models were only moderate, convergent and discriminant validities were clearly supported, and method and error variance were moderate or low.

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Collective Efficacy and Organizational Commitment in an Italian City Hall

European Psychologist ◽

10.1027/1016-9040.14.4.363 ◽

2009 ◽

Vol 14 (4) ◽

pp. 363-371 ◽

Cited By ~ 14

Author(s):

Laura Borgogni ◽

Silvia Dello Russo ◽

Laura Petitta ◽

Gary P. Latham

Keyword(s):

Organizational Commitment ◽

Collective Efficacy ◽

Structural Equation ◽

Structural Equation Models ◽

Practical Significance ◽

City Hall ◽

Italian City ◽

Public Sector Managers ◽

The Individual ◽

Level Group

Employees (N = 170) of a City Hall in Italy were administered a questionnaire measuring collective efficacy (CE), perceptions of context (PoC), and organizational commitment (OC). Two facets of collective efficacy were identified, namely group and organizational. Structural equation models revealed that perceptions of top management display a stronger relationship with organizational collective efficacy, whereas employees’ perceptions of their colleagues and their direct superior are related to collective efficacy at the group level. Group collective efficacy had a stronger relationship with affective organizational commitment than did organizational collective efficacy. The theoretical significance of this study is in showing that CE is two-dimensional rather than unidimensional. The practical significance of this finding is that the PoC model provides a framework that public sector managers can use to increase the efficacy of the organization as a whole as well as the individual groups that compose it.

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A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽

10.1027/1614-1881.1.2.81 ◽

2005 ◽

Vol 1 (2) ◽

pp. 81-85 ◽

Cited By ~ 5

Author(s):

Stefan C. Schmukle ◽

Jochen Hardt

Keyword(s):

Factor Analysis ◽

Maximum Likelihood ◽

Structural Equation ◽

Structural Equation Models ◽

Null Model ◽

Likelihood Estimation ◽

Parameter Estimates ◽

Fit Indices ◽

Cautionary Note ◽

Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽

10.1027/1614-2241/a000084 ◽

2014 ◽

Vol 10 (4) ◽

pp. 138-152 ◽

Cited By ~ 5

Author(s):

Hsien-Yuan Hsu ◽

Susan Troncoso Skidmore ◽

Yan Li ◽

Bruce Thompson

Keyword(s):

Structural Equation ◽

Structural Model ◽

Structural Equation Models ◽

Equation Model ◽

Model Fit ◽

Model Specification ◽

Simulation Design ◽

Measurement Models ◽

Fit Statistics ◽

Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

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