Difference: Moderation Effect & Interaction Effect

-

  • Theoretically moderator effect differs slightly from interaction effects
  • Moderator refer to variable that alter an observed relation in the population
  • While interaction effect refer to any situation in which the effect of one variable depends on the level of another variable  
  • Moderator is not part of a causal sequence but qualifies the relation between X and Y
  • For intervention research, moderator variables may reflect subgroups of persons for which the intervention is more or less effective than for other groups
  • In general, moderator variables are critical for understanding the generalisability of a research findings to subgroups



Example:

  • The true relationship between X & Y is more revealed when critical moderating variables are inserted in the model
  • It is point out that ‘size of the hotel’ is an influential factor which effects the relationship between X & Y, because small hotels are much more exposed to risks than large franchise hotels. (Kang et al.2015), Pivato and Misani 2008, and WU and KO 2013)
  • Other researchers have also reported the effect of firm size in other contexts (e.g. Namazi et al. 2015 and Niresh and Velnampy, 2014) 
Sources from: Professor Dr. Dileep Workshop on Role of Theory, Moderation & Mediation on 3 August 2019.  

Comments

Post a Comment

Popular posts from this blog

Analysing and Reporting Moderating Effect

-
First researcher should focus on the significance of the moderating effect (Z) To clarify, it is possible that a moderator variable (M) may or may not have an effect on the dependent variable (Y) Thus, the decision as to whether there is any moderating effect should be made based on a significance relationship between the moderating effect (Z) and the dependent variable (Y)     Second , researchers must calculate and report the effect size (f 2), and how much it contributes to R2 as a function of the moderator Only a few software packages (e.g. Smart PLS3.0) calculate f 2 by default For other there are online spreadsheets which can be used to calculate effect size (see: http://statwiki.kolobkreations.com ) Lastly, researchers must execute and report a simple slope plot for visual inspection of the direction and strength of the moderating effect SmartPLS users can check out a simple slope plot under ‘Final Results’ and ‘Simple Slope Analysis...
Read more

Proposal Construction: Chapter 1 to 3: Chapter 1 Part 3

-
Narrowing Down Research Topic: To narrow down your topic / focus area, you could ask these kind of questions: Whom for you doing the research? (Specific populations / Stakeholders) What are the factors you have taken for a research? is it relevant, contemporary or not? ( Relevant topic ) Where exactly you want to do the research? (Specific geographical area) This particular time when you do the research, is it relevant or not? (Specific time period) Why the issue occurs?  Example: Topic: Research Topic on Eating disorder Who –  Initially this topic is too broad, to make it narrower, we opt for population that we can focus on. Social demographic factor like: race, gender, age which are connected with eating disorder. So, the topic can be eating disorder in elderly female . You are connected gender, the age and disorder. We are fine tuning this problem into this factor.   What types of eating disorder -  2 types of eating disorder, ...
Read more

Approaches For Moderation Analysis

-
When performing statistical analysis using structural equation modelling, specifically in PLSSEM, there are several approaches for moderation analysis, including: Product- Indicator Two Stage Orthogonalizing  Prior to that, researcher must identify his or her theoretical frame falls under reflective construct or  just formative construct. Depending on researcher’s objective, he or she can choose any of the three approaches. 2-stage approaches has highest power, it is the most likely approaches to create significant attraction. It is recommended to make use of 2-stage approaches and make analysis. I f researcher has smallest population, he or she suppose to use orthogonalizing because the interaction effect size is the most correct and it maximises explained variance in his or her dependent variable. Product indicator approach has no particular advantage but still if it is formative construct can use product indicator approach Formative cons...
Read more