On the Johnson‐neyman Technique and Some Extensions Thereof

Potthoff, Richard F.

doi:10.1002/j.2333-8504.1963.tb00116.x

Cited by 46 publications

(68 citation statements)

References 3 publications

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“…1 There is a distinction between simultaneous and nonsimultaneous confidence bands and regions of significance (Pothoff, 1964). For nonsimultaneous bands, rejection rates are accurate for any given conditional value of the moderator.…”

Section: Moderationmentioning

confidence: 99%

See 1 more Smart Citation

Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions

Preacher

Rucker²,

Hayes

2007

Multivariate Behavioral Research

7,868

6,813

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This article provides researchers with a guide to properly construe and conduct analyses of conditional indirect effects, commonly known as moderated mediation effects. We disentangle conflicting definitions of moderated mediation and describe approaches for estimating and testing a variety of hypotheses involving conditional indirect effects. We introduce standard errors for hypothesis testing and construction of confidence intervals in large samples but advocate that researchers use bootstrapping whenever possible. We also describe methods for probing significant conditional indirect effects by employing direct extensions of the simple slopes method and Johnson-Neyman technique for probing significant interactions. Finally, we provide an SPSS macro to facilitate the implementation of the recommended asymptotic and bootstrapping methods. We illustrate the application of these methods with an example drawn from the Michigan Study of Adolescent Life Transitions, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.

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Section: Moderationmentioning

confidence: 99%

“…Only nonsimultaneous confidence bands and regions of significance are discussed here, as we consider them more practically relevant. For more information on the distinction between simultaneous and nonsimultaneous bands, consult Pothoff (1964) and Rogosa (1981).…”

Section: Moderationmentioning

confidence: 99%

Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions

Preacher

Rucker²,

Hayes

2007

Multivariate Behavioral Research

7,868

6,813

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“…By probe, we mean the calculation of point estimates along with standard errors and confidence intervals that provide inferential tests of the relation between the repeated measures and time conditioned on one or more exogenous predictor variables. There are three procedures that are of particular importance here: tests of simple slopes (Cohen & Cohen, 1983), Johnson-Neyman tests of regions of significance (Johnson & Neyman, 1936;Pothoff, 1964;Rogosa, 1980Rogosa, , 1981, and computation of confidence bands for the conditional effects (Rogosa, 1980(Rogosa, , 1981. Although these tests are well developed for standard regression models (see Aiken & West, 1991), we are not aware of any prior work that has extended these tests to the latent curve modeling framework.…”

Section: The Conditional Latent Curve Modelmentioning

confidence: 99%

“…We now show how we can gain even further information about the conditional effects of x and time by computing regions of significance, a technique originally applied in regression models by Johnson and Neyman (1936) with subsequent extensions by Pothoff (1964) and Rogosa (1980). 4 Regions of significance will allow us to assess at precisely what periods of time the simple slopes of x predicting y pass from significance to nonsignificance.…”

Section: Regions Of Significancementioning

confidence: 99%

Testing Main Effects and Interactions in Latent Curve Analysis.

Curran¹,

Bauer²,

Willoughby³

2004

Psychological Methods

156

149

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A key strength of latent curve analysis (LCA) is the ability to model individual variability in rates of change as a function of 1 or more explanatory variables. The measurement of time plays a critical role because the explanatory variables multiplicatively interact with time in the prediction of the repeated measures. However, this interaction is not typically capitalized on in LCA because the measure of time is rather subtly incorporated via the factor loading matrix. The authors' goal is to demonstrate both analytically and empirically that classic techniques for probing interactions in multiple regression can be generalized to LCA. A worked example is presented, and the use of these techniques is recommended whenever estimating conditional LCAs in practice.

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“…The Johnson-Neyman [1936] technique, as modified by Potthoff [1964], for locating the values of x for which a significant difference can be asserted between the population regression lines, is a simple application of the WorkingHotelling procedure.…”

Section: Summary and Introductionmentioning

confidence: 99%