SmartPLS - Measure Validity

We need to evaluate the measurement model before we can know how reliable and valid the model is. As I explained in the earlier post HERE, reliability can be measured by assessing the composite reliability and Cronbach's Alpha whereas validity can be measured through convergent and discriminant validity.

Have you download SmartPLS software? If "YES", you can just proceed to test the validity. If "NO", you can download the software and install it into your computer. In this post, I'll show you step by step on how to calculate validity. 

First, we need to examine the cross-loadings since they are the dominant approaches to measure discriminant validity.

Step 1: Asses indicator loadings by clicking "Default Result" --> "PLS" --> "Calculation Results" --> "Outer Loadings"

The table below shows a complete table of the outer loadings

From the table, we can see that all reflective indicators have loadings of 0.636 and higher. SmartPLS provide outer loadings and outer weights for all construct in the PLS path model, regardless of whether they are measured reflectively or formatively.

Step 2: Assess the Fornell-Larcker criterion, click "Report" --> "PLS" --> "Quality Criteria" --> "Latent Variable Criterion

This is the last step in analysing the cross-loadings. For reflective measurement model, we use Fornell-Larcker criterion. Fornell-Larcker criterion is the second and more conservative approach in assessing discriminant validity. It compares the square root of the AVE values with the latent variable correlations. The square root of each construct's AVE should be greater than its highest correlation with any other construct. The logic of this method is based on the idea that a construct shares more variance with its associated indicators than with any other construct.

The table below shows the Fornell-Larcker criterion before we calculate the square root of AVE

From the table above, we include the values of the square root of AVE

Finally, the table of Fornell-Larcker criterion is complete

These are the needed step in measuring the validity through SmartPLS. So, how can we explain those number? What are those number supposed to mean? Actually, you can refer these papers, HERE and HERE to understand how to analyse our collected data.

Till next time. Be safe and take care! 

Let's fight Covid-19 together!

Meta-Data Analysis

Meta-data analysis is the earliest step that students need to conduct before they can start writing their thesis or dissertation. This method is also known as systematic LR and systematic review, systematic analysis.

When I prepared my meta-data analysis, I analysed more than 300 articles before I know which specific topic to be researched and what we need to investigate in order to bridge the gap in the literature.

As for the first step, I gathered all the related articles that examined the dimensions of brand loyalty. This is how we can divide the column in the Meta-Analysis. The column includes name of author(s), Independent Variable (IV), Dependent Variable (DV), Interverning Variable (IIV) and/or Moderating Variable (MV), Methodology, Findings and Limitation/Recommendation/ Future Research.

We can gather more than 200 articles. It depends on how many articles you can retrieve. The more the better. By doing this, apart from we can understand the entire topic better, we can understand how previous researchers conducted their research and what are the limitations in their research. From there, we can find the need for future research to be done. 

As for the next step, we sort the elements out of the articles and categorise them into different group.

As we can see here in the figure above, I categorised the articles based on which author(s) measure IVs that influence DV. Some researchers measured only one element of brand loyalty while some other researchers measure two and more elements of brand loyalty. The IVs in my research consist of the factors that influence brand loyalty.

I also categorised which articles examined the mediating or intervening variable (IIV) in the brand loyalty relationship. So, from this analysis I can understand why researchers measure certain variables and left out other variables and why they need to measure the intervening effect on the relationship and what are the reason behind of their selection.

Based on these figures, we can now seek the research gaps and do our research. At the recommendation and future research section there are limitations of the present research. From there we can get an idea of what to examine in order to bridge the gaps.

For further reading on conceptual papers regarding meta-data analysis, I've prepared two papers. First paper, it's regarding the elements used in measuring customer brand loyalty in which you can view, cite and download it HERE. 

As for the second paper, it is a meta-analysis through a summary of literature review on brand loyalty with the inclusion of mediating variable, in which you can view, cite and download it HERE.

I strongly believe that this meta-data analysis really need consistency and hard work from you. No pain no gain. Wish you all the best in sorting out the needed information from your numerous gathered articles/papers.

Love,
Dr SAA