SmartPLS - Measure Reliability

Have you ever heard about PLS? If "YES", it means that you are already in Chapter 4 (Data Analysing). It just few more steps before you can complete your thesis. So, how can we measure PLS model? The answer is, we need to first measure whether our model is reliable or not. Reliability can be measured by assessing the composite reliability and the Cronbach's Alpha of the model whereas validity can be measured through convergent and discriminant validity.

First of all, we need download the SmartPLS software from HERE and install it into our computer and click 'Run'. The software will be automatically installed.

In this post, I'll show you step-by-step how to calculate reliability through SmartPLS. Fyi, I use SmartPLS version 2 since it FREE. Hehehe.

Step 1: Click the draw button and design the IVs and DV

Step 2: Click "Calculate" --> PLS Algorithm

Step 3: Calculate the PLS. Click "Finish"

Step 4: Remove the loading items less than 0.6. According to Hair et al., (2011), the loading should be more than 0.6

Step 5: The remaining items should be greater than 0.6

Step 6: The items with value greater than 0.6 remain in the model and ready to be calculated

Step 7: Retrieve the composite reliability (CR), Cronbach's alpha and Average Variance Extracted (AVE) by clicking "Report" --> Html (Print) Report

Step 8: The report will be exported to HTML and we can still access the data offline. How great isn't it? No need to worry even if we have an unstable Internet connection (^_^)

Step 9: The first criterion to be evaluated is typically internal consistency reliability. The traditional criterion for internal consistency is Cronbach's alpha. In PLS we are using internal consistency reliability to represent the Cronbach's alpha.

Step 10: Copy the table

Step 11: The table can be "paste" [ctrl+v] in the Microsoft Excel. Then, highlight the value of Cronbach's Alpha and click "decrease decimal" to reduce the decimal point. So, it can increase the readability. 

Step 12: The value of Cronbach's Alpha now can be easily read

Step 13: Repeat Step 9 to Step 12 to calculate the value of Composite Reliability, AVE, Coefficients of Determination (R²), Latent Variable (LV) correlations and Path Coefficients.

Step 14: Copy the overview of result and "paste" in Microsoft Excel

Step 15: This is how the result looks like in Microsoft Excel before we reducing the decimal points and re-design the table below before we can include it in our report.

From the result, it shows that all reflectively measured constructs have AVE values of 0.605 and higher, which is considerably above the critical value of 0.5. The AVE value of at least 0.5 indicates sufficient convergent validity, meaning that a latent variable is able to explain more than half of the variance of its indicators on average. In addition, all composite reliability values are well above the critical threshold of 0.7.

These are the steps in measuring reliability through SmartPLS. But, how can we explain those numbers? What are the meaning of those numbers? To guide you in analysing those numbers, you can refer these papers HERE and HERE. These papers also explain further on structural model evaluation and hypothesis testing.

That's all for now. 

Till next time and take care! (^_^)

Reliability and Validity Analysis

Previous scholars have shown that Partial Least Squares (PLS) is a robust technique that has been frequently used in the literature. In PLS, to analyse a model, we need to follow the two-step procedure which are the measurement model and structural model (Hair et al. 1998).

1. Measurement Model
In this step, we measure convergent validity and discriminant validity. 

What are the purpose of measuring convergent validity? The reason for analysing convergent validity is to measure the close relations exist among the items of the same construct. We need to measure composite reliability (CR) and average variance extracted (AVE).

In PLS, the factor loading should be greater than 0.7 (Hair, 2013). We need to remove items loading with less than 0.7 to increase CR or AVE in the first order of the component of independent variables (IVs).

Fornell and Larcker (1981) stated that the CR values should be more than 0.7 and AVE should be greater than 0.5 in order for the result to be accepted. Convergent validity is established if all the values of CR are greater than 0.7 and all the values AVE in a study are greater than 0.5. To know more about how to calculate CR and AVE, you can click HERE (Scopus-indexed journal).

Discriminant validity explains the degree of irrelevance between constructs. To measure discriminant validity, there are two steps which are confirmatory factor analysis and AVE analysis. All items should have high loading on their corresponding constructs. Then, we calculate the square root of the AVE that exceed the inter-correlation of the construct in the proposed model. In order to support discriminant validity, each construct's AVE square root should be greater than its correlations with other constructs. 

To see more about the explanation in analysing convergent and discriminant validity you can either click HERE or HERE. 

2. Structural Model
Structural equation modelling tested the hypothesised paths of the structural model. All coefficient estimates were significant (p<0.05) in accordance with the hypothesised directions. In testing the proposed hypotheses, the standardised path coefficient is expected to be at least 0.2 and preferably greater than 0.3 (Chin and Newsted, 1999).

The reliability of the coefficients in the study is obtained through a bootstrapping procedure (500 resamples). All t values need to be higher than the theoretical t value of 2.57 for a 5% probability of error. Meanwhile, the p values of 0.000 indicate that all path relationship are significant at a 5% probability of error.

Predictive accuracy of a model can be measured by analysing the coefficient of determination. A rule thumb on the acceptable coefficient of determination is 0.75, 0.50 and 0.25, explaining substantial, moderate or weak level of predictive accuracy, respectively (Hair et al., 2014).

For further explanation on how to calculate the structural equation model analysis and how to explain the predictive accuracy of the model, you can click HERE (Scopus-indexed journal).

Love,
Dr SAA
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