2000
DOI: 10.1177/109442810033002
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Interaction in Linear versus Logistic Models: A Substantive Illustration Using the Relationship between Motivation, Ability, and Performance

Abstract: A binary performance measure (high school graduation) is examined as a function of motivation (educational goal), ability (scores in an intelligence test), and their interaction. The interaction was positive when a logistic model was used and negative when a linear probability model was used. The reason for the difference in the results of the two models is examined, and the conditions under which this difference occurs are discussed.

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Cited by 35 publications
(27 citation statements)
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“…Th e statistical procedures for determining the presence of mediator and moderator eff ects are intuitive and straightforward for models with continuous dependent variables ( Aguinis 2004; Baron and Kenny 1986 ), binary dependent variables ( Ganzach, Saporta, and Weber 2000 ), and ordinal dependent variables ( Jaccard 2001 ), such as those used here. Nonetheless, establishing those eff ects requires a several-step process, which is briefl y described here.…”
Section: Methodsmentioning
confidence: 99%
“…Th e statistical procedures for determining the presence of mediator and moderator eff ects are intuitive and straightforward for models with continuous dependent variables ( Aguinis 2004; Baron and Kenny 1986 ), binary dependent variables ( Ganzach, Saporta, and Weber 2000 ), and ordinal dependent variables ( Jaccard 2001 ), such as those used here. Nonetheless, establishing those eff ects requires a several-step process, which is briefl y described here.…”
Section: Methodsmentioning
confidence: 99%
“…We estimated predicted mean of each knowledge or practice indicator by time and study arm and compared the change between baseline and endline by study arm, controlling for maternal and household background characteristics described above. Linear probability regression models were used to test the null hypothesis that the difference-in-difference was zero [27]. Robust standard errors were adjusted for clustering on each union.…”
Section: Methodsmentioning
confidence: 99%
“…The analysis was performed hierarchically, because in logistic regression analysis, regression weights and significance tests of predictors can not be interpreted as main effects when their products (interaction terms) are also predictors in the analysis (Jaccard, 2001). Because interactions in logistic regression analysis might lead to inconsistencies between interpretation of proportions (which fit with how humans tend to think about effects) and interpretation of logits (i.e., the natural logarithms of the odds for those proportions, which fit with the estimated parameters in logistic regression; Ganzach, Saporta & Weber, 2000), the amount of cooperation is given both in proportions and in logits of cooperative choice. The results are presented in Table 1 (regression weights, odds ratios, and significance tests) and Table 2 (proportions and logits).…”
Section: Cooperationmentioning
confidence: 99%