Measuring needs with the Thematic Apperception Test: A psychometric study.

Tuerlinckx, Francis; Boeck, Paul De; Lens, Willy

doi:10.1037/0022-3514.82.3.448

Cited by 69 publications

(86 citation statements)

References 40 publications

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“…This dynamic Thurstonian model additionally includes a dynamic IRT process (Verguts & De Boeck, 2000;Verhelst & Glas, 1993) and is inspired by the idea that motivational tendencies are dynamic and that their strength declines when they are acted out (Atkinson & Birch, 1970;Tuerlinckx, De Boeck, & Lens, 2002).…”

mentioning

confidence: 99%

Modeling motive activation in the Operant Motive Test: A psychometric analysis using dynamic Thurstonian item response theory.

Runge¹,

Lang²,

Engeser³

et al. 2016

Motivation Science

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The Operant Motive Test (OMT) is a picture-based procedure that asks respondents to generate imaginative verbal behavior that is later coded for the presence of affiliation, power, and achievement-related motive content by trained coders. The OMT uses a larger number of pictures and asks respondents to provide more brief answers than earlier and more traditional picture-based implicit motive measures and has therefore become a frequently used measurement instrument in both research and practice. This article focuses on the psychometric response mechanism in the OMT and builds on recent advancements in the psychometric modeling of the response process in implicit motive measures through the use of Thurstonian item-response theory. The contribution of the article is twofold. First, the article builds on a recently developed dynamic Thurstonian model for more traditional implicit motive measures (Lang, 2014) and reports the first analysis of which we are aware that applies this model to OMT data (N = 633) and studies dynamic motive activation in the OMT. Results of this analysis yielded evidence for dynamic motive activation in the OMT and showed that simulated IRT reliabilities based on the dynamic model were .52, .62, and .73 for the affiliation, achievement, and power motive in the OMT, respectively. The second contribution of this article is a tutorial and R code that allows researchers to directly apply the dynamic Thurstonian IRT model to their data. The future use of the OMT in research and potential ways to improve the OMT are discussed.

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mentioning

confidence: 99%

Modeling motive activation in the Operant Motive Test: A psychometric analysis using dynamic Thurstonian item response theory.

Runge¹,

Lang²,

Engeser³

et al. 2016

Motivation Science

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“…In addition, the effect of the time-perspective manipulation was not significant when the three motives were tested separately. Although there might be some theoretical reasons for the differences between the motives, the most obvious explanation of the non-significant results regarding the individual motive scores is the relatively low reliability of the implicit motive measurement as compared to other methods (see Tuerlinckx, De Boeck, & Lens, 2002).…”

Section: Stability Of Implicit Motivesmentioning

confidence: 99%

“…2 Including more PSE pictures as a method to increase reliability is often not AGE, TIME PERSPECTIVE, AND IMPLICIT MOTIVES 13 viable as it increases the assessment time and changes the measurement itself (Tuerlinckx et al, 2002). Future studies are needed to replicate the findings in a larger sample.…”

Section: Stability Of Implicit Motivesmentioning

confidence: 99%

The effect of age and time perspective on implicit motives

Nikitin¹,

Freund²

2013

PsycEXTRA Dataset

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People differ in how open-ended or limited they perceive their future. We argue that individual differences in future time perspective affect the activation of implicit motives. Perceiving the time remaining for the satisfaction of one's motives as limited should be associated with a higher activation of these motives than perceiving one's future as more open-ended. Given that future time perspective decreases across adulthood, older adults should score higher on implicit motives than younger adults. This hypothesis was supported in a study with young (n = 53, age M = 25.60 years) and older adults (n = 55, age M = 68.05 years). Additionally, an experimental manipulation of future time perspective showed that age-related differences in implicit motives are influenced by future time perspective. These findings demonstrate that future time perspective is an important factor to explain the strength of motives. and older adults (n = 55, age M = 68.05 years). Additionally, an experimental manipulation of future time perspective showed that age-related differences in implicit motives are influenced by future timeperspective. These findings demonstrate that future time perspective is an important factor to explain the strength of motives.Keywords: implicit motives, future time perspective, adult age differences, lifespan development AGE, TIME PERSPECTIVE, AND IMPLICIT MOTIVES one's goals but also one's motives, perceiving one's future as limited might also be associated with an increased urgency to satisfy implicit motives. In other words, perceiving the time for the satisfaction of one's motives as limited should be associated with a stronger activation of these motives than believing that the satisfaction of one's motives is possible in an extended future.Given that future time perspective decreases across adulthood (Lang & Carstensen, 2002), older adults should show a higher intensity in their implicit motives than younger adults. We tested this hypothesis in a study with young and older adults. Additionally, we manipulated future time perspective in the same sample to compare the intensity of implicit motives between individuals with an experimentally extended and reduced future time perspective. The results of the current study will help to clarify if and why implicit motives change across adulthood. Implicit Motives and Their Development across AdulthoodImplicit motives are relatively enduring preferences for a broadly defined class of incentives that direct and energize behavior (McClelland, Koestner, & Weinberger, 1989). They are related to the intrinsic enjoyment of and affective preference for activities congruent with the AGE, TIME PERSPECTIVE, AND IMPLICIT MOTIVES 4 motive (Brunstein, 2006;McClelland et al, 1989). Three distinct motives are often referred to as the "big three," namely achievement, power, and affiliation/intimacy (Winter, 1994).Achievement motives denote the desire to accomplish something difficult and to attain a high level of excellence (Pang, 2010a;Winter, 1994). At the core of p...

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“…An item bundle may also consist of more than two dependent items. An application of such a model to projective testing can be found in Tuerlinckx, De Boeck, and Lens (2002).…”

Section: Item Bundle Models With Fixed Dependency Effectsmentioning

confidence: 99%

A nonlinear mixed model framework for item response theory.

Rijmen¹,

Tuerlinckx²,

Boeck³

et al. 2003

Psychological Methods

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196

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Mixed models take the dependency between observations based on the same cluster into account by introducing 1 or more random effects. Common item response theory (IRT) models introduce latent person variables to model the dependence between responses of the same participant. Assuming a distribution for the latent variables, these IRT models are formally equivalent with nonlinear mixed models. It is shown how a variety of IRT models can be formulated as particular instances of nonlinear mixed models. The unifying framework offers the advantage that relations between different IRT models become explicit and that it is rather straightforward to see how existing IRT models can be adapted and extended. The approach is illustrated with a self-report study on anger.Mixed models (see, e.g., Diggle, Heagerty, Liang, & Zeger, 2002;Goldstein, 1995;Longford, 1993;Verbeke & Molenberghs, 2000) are a collection of statistical tools that are well suited for analyzing clustered data, such as, for example, data from students nested within schools or repeated measurement data (measurements nested within participants). Modeling clustered data under a model that assumes independent observations is inappropriate because the observations on the subunits (students, measurements) of the same unit (school, participant) tend to be more homogeneous than the observations on subunits of different units. The heterogeneity between units can be taken into account by assuming that (some of) the parameters of the model follow some random distribution over the population of units. Hence, (some of) the parameters of the model are random variables, and the model is a random effects model, or mixed model.The random effects represent the unit effects. The parameters that are not assumed to be random realizations from a distribution, and hence are not considered to be unit specific, are called fixed effects.Mixed models and related methods were first developed in the context of analysis of variance and regression analysis, leading to the linear mixed model. Other commonly used terms are multilevel models (Goldstein, 1995), hierarchical models (Raudenbush & Bryk, 2002), and random coefficient models (Longford, 1993). More recently, nonlinear mixed models were also developed. A nonlinear mixed model is a model with random coefficients in which the fixed and/or random effects enter the model nonlinearly (Davidian & Giltinan, 1995). As we explain below, a subset of nonlinear mixed models is the class of generalized linear mixed models (McCulloch & Searle, 2001).The main purpose of this article is to explain how item response theory (IRT) models can be conceptualized as nonlinear mixed models and to provide a framework for this conceptualization. There are four important assets of this approach. First, this conceptualization relates IRT to the broad statistical literature on mixed models. Second, applying the same framework to different IRT models can help in the understanding of their differences and commonalities. Third, using this framework, one can read...

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Measuring needs with the Thematic Apperception Test: A psychometric study.

Cited by 69 publications

References 40 publications

Modeling motive activation in the Operant Motive Test: A psychometric analysis using dynamic Thurstonian item response theory.

Modeling motive activation in the Operant Motive Test: A psychometric analysis using dynamic Thurstonian item response theory.

The effect of age and time perspective on implicit motives

A nonlinear mixed model framework for item response theory.

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