Moderator variables: A clarification of conceptual, analytic, and psychometric issues

doi:10.1016/0030-5073(82)90254-9

Organizational Behavior and Human Performance

Volume 29, Issue 2, April 1982, Pages 143-174

Organizational Behav...

https://doi.org/10.1016/0030-5073(82)90254-9 Get rights and content

Abstract

A distinction is drawn between the degree of relationship between two variables X and Y and the form of the relationship between the same variables. The correlation coefficient r_xy is the index of degree of relationship, while the regression coefficient B_yx is the index of the form of relationship. If both the form and degree are constant across values of some third variable Z, the X − Y relationship is constant or unconditional with regard to Z. If the degree of relationship varies with values of Z, Z is said to “moderate the degree” of the X − Y relationship. If the form of relationship varies with values of Z, Z is said to “moderate the form” of the relationship. The separate statistical tests which are required to test for each type of moderator variable are outlined. Hierarchical multiple regression is the appropriate method to test for different forms of relationship, but not for different degrees of relationship. The differing substantive implications and interpretation of the two types of moderated relationships are discussed. Finally, the effects of unreliability of measures on the power of tests to detect differing forms and degrees of relationships are outlined and demonstrated.

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Citation Excerpt :
We also note in Table 4 that a company's industry membership (that is, being a member of the environmentally sensitive industries) is correlated negatively significantly (at the 1% level) with both its commitment to taking action on climate change (COMMIT) and the volume of media coverage (MEDIA) it receives (see Table 4). A joint consideration of the significant effect of the interaction term with the significant correlation coefficients suggests that industry membership of a firm moderates the form of the relationship between the media's coverage of a firm and its commitment to climate change actions (Arnold, 1982; Hartmann and Moers, 1999; Sharma et al., 1981). In other words, the slope of the association between the media's coverage of a firm and its commitment to climate change actions is steeper for firms in the environmentally sensitive industries relative to those in other industries (Hartmann and Moers, 1999; Sharma et al., 1981).
The study examines whether the media's attention to a firm shapes its commitment to climate change, and if there is a systematic inter-industry variation in the association between the two variables. Analyzing data drawn from S&P 1500 companies, for the years 2015–2019, we provide robust evidence that heightened media attention accentuates the odds of a firm committing to take more action on climate change. We also demonstrate that the likelihood of companies in environmentally sensitive industries committing to action on climate change is lower, relative to those in other industries. Additionally, membership of a firm in environmentally sensitive industries moderates the form of the association between media coverage of a firm and corporate commitment to action on climate change. Our findings suggest that media-driven environmental legitimacy and reputational costs exceed the investments required to enhance a firm's commitment to climate change action. The findings imply that the agenda setting, salience transfer and issue framing functions of the media could be used to enhance corporate response to climate change issues, particularly the response of those firms that appear to have a lower proclivity towards taking action to address climate change.

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^★: Frank Schmidt provided extremely helpful advice in the development of the manuscript. The suggestions and comments of Martin Evans, Art Jago, Stephan Motowidlo, and several anonymous reviewers are also gratefully acknowledged.

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Organizational Behavior and Human Performance

Abstract

References (33)

Testing multiplicative models does not require ratio scales

Organizational Behavior and Human Performance

Sex differences in predictability of college grades

Educational and Psychological Measurement

Negro—white differences in validity of employment and training selection procedures

Journal of Applied Psychology

Differential prediction: A methodological artifact?

Journal of Applied Psychology

On the interpretation of the correlation coefficient as a measure of predictive efficiency

Journal of Educational Psychology

Test bias: Prediction of grades of Negro and White students in integrated colleges

Journal of Educational Measurement

Errors of measurement in statistics

Technometrics

Multiple regression as a general data analytic system

Psychological Bulletin

Statistical power analysis for the behavioral sciences

Partialled products are interactions: partialled powers are curve components

Psychological Bulletin

Applied multiple regression/correlation analysis for the behavioral sciences

Another look at “cultural fairness”

Journal of Educational Measurement

Differential predictability in the use of test scores

Educational and Psychological Measurement

Adjustment to college: A study of 10,000 veteran and nonveteran students in sixteen American colleges

Differentiation of individuals in terms of their predictability

Journal of Applied Psychology

The prediction of predictability

Educational and Psychological Measurement

Cited by (479)

Estimating human sensitivity to curving of segmented paths within room-size environment

The predictive effect of relative intuition on social entrepreneurship orientation: How do exploratory and exploitative learning and personal identity interact?

Artificial intelligence and relocation of production activities: An empirical cross-national study

Moderator variables in consumer research: A call for caution

Organizational disidentification and change-oriented citizenship behavior

Media attention and its impact on corporate commitment to climate change action

Moderator variables: A clarification of conceptual, analytic, and psychometric issues★

Abstract

Organizational Behavior and Human Performance

Sex differences in predictability of college grades

Educational and Psychological Measurement

Negro—white differences in validity of employment and training selection procedures

Journal of Applied Psychology

Differential prediction: A methodological artifact?

Journal of Applied Psychology

On the interpretation of the correlation coefficient as a measure of predictive efficiency

Journal of Educational Psychology

Test bias: Prediction of grades of Negro and White students in integrated colleges

Journal of Educational Measurement

Errors of measurement in statistics

Technometrics

Multiple regression as a general data analytic system

Psychological Bulletin

Statistical power analysis for the behavioral sciences

Partialled products are interactions: partialled powers are curve components

Psychological Bulletin

Applied multiple regression/correlation analysis for the behavioral sciences

Another look at “cultural fairness”

Journal of Educational Measurement

Differential predictability in the use of test scores

Educational and Psychological Measurement

Adjustment to college: A study of 10,000 veteran and nonveteran students in sixteen American colleges

Differentiation of individuals in terms of their predictability

Journal of Applied Psychology

The prediction of predictability

Educational and Psychological Measurement