An IRT analysis of motive questionnaires: The Unified Motive Scales

Schönbrodt, Felix D.; Gerstenberg, Friederike X. R.

doi:10.1016/j.jrp.2012.08.010

Cited by 160 publications

(199 citation statements)

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“…Many researchers might have observed that the magnitude of a correlation is pretty unstable in small samples, as the following empirical example demonstrates. Multiple questionnaire scales have been administered in an open online study (Schönbrodt & Gerstenberg, 2012;Study 3). The thick STABILITY OF CORRELATIONS 4 black line in Figure 1 shows the evolution of the correlation between two scales, namely "hope of power" and "fear of losing control" when after each new participant the correlation is recalculated.…”

Section: Introductionmentioning

confidence: 99%

At what sample size do correlations stabilize?

Schönbrodt

Perugini

2013

Journal of Research in Personality

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Sample correlations converge to the population value with increasing sample size, but the estimates are often inaccurate in small samples. In this report we use Monte-Carlo simulations to determine the critical sample size from which on the magnitude of a correlation can be expected to be stable. The necessary sample size to achieve stable estimates for correlations depends on the effect size, the width of the corridor of stability (i.e., a corridor around the true value where deviations are tolerated), and the requested confidence that the trajectory does not leave this corridor any more. Results indicate that in typical scenarios the sample size should approach 250 for stable estimates.

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Section: Introductionmentioning

confidence: 99%

At what sample size do correlations stabilize?

Schönbrodt

Perugini

2013

Journal of Research in Personality

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1,652

1,265

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“…We propose that situations which allow people to have impact on others (e.g., the moral decision of harming someone, or, alternatively, the moral decision to save multiple others), act as an incentive for individuals with a strong power motive. This motive is defined by a desire to have an impact on others by influencing their attitudes, emotions and behaviours as well as by a desire to attain prestige and reputation (e.g., Heckhausen & Heckhausen, 2008;Schönbrodt & Gerstenberg, 2012;Winter, 1988). Similar to other motives such as affiliation, achievement, or intimacy, researchers usually differentiate between an implicit and an explicit/ self-attributed motive component (e.g., Schönbrodt & Gerstenberg, 2012).…”

Section: Introductionmentioning

confidence: 99%

Individual differences in the explicit power motive predict “utilitarian” choices in moral dilemmas, especially when this choice is self-beneficial

Suessenbach

Moore

2015

Personality and Individual Differences

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a b s t r a c tWe all face moral decisions, whether we are judges, politicians, or just riding the bus. The most well studied of these involve concerns of harming or caring for other people, which have often been researched by employing hypothetical moral dilemmas. This study investigated how the explicit power motive, more precisely the hope to gain power (h_Power), predicts decisions for these types of problems. We found that h_Power was positively related to deciding that it was morally acceptable to kill one person to save multiple others (i.e., making a utilitarian choice). In an exploratory analysis, we found that the probability of making such choices as a function of h_Power was even higher when participants' own lives were at stake as compared to only the lives of others. These findings complement previous research showing that personality variables as well as situational factors predict moral decision making. Finding biases in moral decision making is important, as only when we know these biases we can consciously counteract them.

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“…This method adopts advanced educational and psychological measurement theories and regards all measurement as based on items, and latent traits of the measured objects can be predicted through responses of the objects to a few items. The so-called items are the general elements and subdomains in researchers' analysis (Schönbrodt & Gerstenberg, 2012). In the current study, the above mentioned eight types of questions that teachers ask are set as items.…”

Section: Methodsmentioning

confidence: 99%

Classroom Questioning Tendencies from the Perspective of Big Data

Wang

Rongxiao²

2016

Front Educ China

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We are now living in an era of big data. From the perspective of data analysis of classroom questioning, the paper chooses three districts in City B that have significant differences. These are educationally developed District D, less developed District F, and developing District M. The study uses the stratified sampling method to choose from three different groups of teachers, namely novice teachers, competent teachers, and experienced teachers, by way of video case analysis, the Item Response Theory (IRT) model method, and the inductive and deductive method, to analyze the characteristics of teachers' classroom questions. It was found that: (1) In terms of open questioning, all three districts with their different levels in educational development need to improve open questioning levels among their experienced primary school teachers. In middle schools, novice teachers' open questioning tendency is significantly lower than that of qualified and experienced teachers; (2) From the quantitative study of three kinds of tendencies, the lowest level of the three tendencies in the classroom questions is problem-solving; (3) In the three districts, the experienced and qualified primary school teachers in the developing district has a prominent advantage in raising critical and creative questions, while in the middle schools, novice teachers generally have a lower level than qualified and experienced teachers in raising critical and creative questions. The results of the big data analysis enable us to draw a conclusion about teachers' value orientation regarding questioning in class. At present, they pay attention to the local value of classroom questioning, but ignore the overall value; pay attention to the instrumental value of classroom questioning, but ignore the objective value; and pay attention to the superficial value of classroom questions, but ignore the underlying value.

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An IRT analysis of motive questionnaires: The Unified Motive Scales

Cited by 160 publications

References 63 publications

At what sample size do correlations stabilize?

At what sample size do correlations stabilize?

Individual differences in the explicit power motive predict “utilitarian” choices in moral dilemmas, especially when this choice is self-beneficial

Classroom Questioning Tendencies from the Perspective of Big Data

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