• Moderates, interferes, and alters the effect of predicting variable on criterion variable.

• Specifies the conditions under which a given effect occurs, as well as the conditions under which the direction (nature) or strength of an effect vary.

• Affects the relationship between two variables, so that the nature of the effect of the predictor on the criterion varies according to the level or value of the moderator (Holmbeck, 1997).

• The presence of a moderator modifies the original relationship between predictor & criterion variable.

• Interacts with the predictor in such a way as to have an impact on the level of the criterion variable.

Examples

1. Look at suggestion for future research.

2. Focus group discussion of respondent who are related to your study.

3. Look at other areas of study. We can borrow the idea from other study and use in our study.

4. Look at meta-analysis papers and find what are the factors influencing the focus of study.

5. Attend prestigious conference. People will present on going works, etc.

Should I hypothesize the form of the interactions in advance?

YES, not only should the existence of an interaction effect be predicted, but also its form. In particular, whether a moderator increases or decreases the association between two other variables should be specified as part of the a priori hypothesis (Dawson, 2014)

Example

H₁: The positive relationship between satisfaction and loyalty will be stronger when perceived image is high.

H₂: The positive relationship between satisfaction and loyalty would be stronger for male compared to female.

H₁: Body Mass Index (BMI) moderates the relationship between exercise and weight loss, such that for those with a low BMI, the effect is negative (i.e., you gain weight muscle mass), and for those with a high BMI, the effect is positive (i.e., exercising leads to weight loss).

An important consideration about categorical moderators is that they should only be used when the variable was originally measured as categories.

Continuous variables should never be converted to categorical variables for the purpose of testing interactions. Doing so...

1. Reduces the statistical power of the test (Stone-Romero and Anderson 1994; Cohen et al. 2003).

2. Difficult to detect significant effects (Stone-Romero and Anderson 1994; Cohen et al. 2003).

3. Throwing up theoretical questions about why particular dividing points should be used (Dawson, 2013).

Memon et al. (2019) also argued that such an issue will lead to...

1. Loss of information.

2. Undermines the interpretation of the moderator

3. Reduces the variance of the moderator variable.

4. The estimated moderating effects are biased downward (Aguinis, 1995).

5. Discards information

6. Reduces statistical power to detect moderating effects,

7. Attenuates the size of moderating effects.

Hence, this practice should be discontinued (Aguinis, 1995; Aguinis & Gottfredson, 2010).

Issue 4. Insufficient Statistical Power

It is always better to collect bigger sample size as we will create interaction terms that may require larger sample size.

Issue 5. Artificial Dichotomization of Continuous Moderators

Don't categorize the continuous moderator into low and high.

Issue 6. Presumed Effects of Correlations Between Product Term and Its Components

When we create an interaction effects, we need to see the possibility of having multicollinearity effects.

Issue 7. Interpreting First-Order Effects Based on Models Excluding Product Terms

We should not run the first order effect without interaction term and interpret the result in separeted models. Instead, IV + MV + MV*IV should be pointing into the DV and estimate all relationship in one model simultanously.

For the creation of the interaction term, use the two-stage approach when the exogenous construct and/or the moderator are measured formatively or when the aim is to disclose a significant moderating effect. Alternatively, use the orthogonalizing approach, especially when the aim is to minimize estimation bias of the moderating effect or to maximize prediction. Independent from these aspects, the two-stage approach is very versatile and should generally be given preference for creating the interaction term.

The moderator variable must be assessed for reliability and validity following the standard evaluation procedures for reflective and formative measures. However, this does not hold for the interaction term, which relies on an auxiliary measurement model generated by reusing indicators of the exogenous construct and the moderator variable.

Standardize the data when running a moderator analysis.

In the results interpretation and testing of hypotheses, differentiate between the direct effect (or main effect), on one hand, and simple effect, on the other. The direct effect expresses the relationship between two constructs when no moderator is included. On the contrary, the simple effect expresses the relationship between two constructs when moderated by a third variable, and this moderator has an average value (provided the data are standardized).

When testing a moderated mediation model, use Hayes’s (2015) index of moderated mediation.

Do not use mediated moderation models.

Interpreting interaction effects. Please click here.

Interaction. Please click here.

Moderation (Interaction Effect) Material. Please click here.

Interaction plot for the categorical data moderator. Click here to download.

Interaction plot for the continuous data moderator. Click here to download.

Aguinis, H., Edwards, J. R., & Bradley, K. J. (2017). Improving Our Understanding of Moderation and Mediation in Strategic Management Research. Organizational Research Methods, 20(4), 665-685.

Becker, J.-M., Ringle, C. M., & Sarstedt, M. (2018). Estimating Moderating Effects in PLS-SEM and PLSc-SEM: Interaction Term Generation*Data Treatment. Journal of Applied Structural Equation Modeling, 2(2), 1-21.

Dawson, J. F. (2013). Moderation in Management Research: What, Why, When, and How. Journal of Business and Psychology, DOI 10.1007/s10869-013-9308-7.

Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2019). Moderation Analysis: Issues and Guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-xi

Sharma, S., Durand, R. M., & Gur-Arie, O. (1981). ‘‘Identification and analysis of moderator variables’’. Journal of Marketing Research, 18(3), 291 -300.