Basic Concept of PLS

What is PLS? 

PLS is an acronym for Partial Least Squares. There are actually many alternatives for students to measure the reliability and validity of a model. For example, they can use either IBM software for SPSS, SmartPLS software for PLS, SEM, AMOS or other statistical software. However, during my study, I have employed PLS to measure the research model.

The process of analysing the data can be done by using the statistical software SmartPLS (Smart PLS 2.0) for Windows or PLS. In an investigation, a structural equation modelling approach using PLS technique is employed to evaluate the proposed hypotheses through the research model.

SmartPLS performs a Confirmatory Factor Analysis (CFA) while evaluating the partial least squares (PLS). The examination on CFA is done to confirm the subscales to be in the right group. The application of CFA is particularly appropriate in an investigation if there is an argument about the dimensionality or factor structure of a scale or measure (Kelloway, 1998). Hence, conducting CFA in an investigation would assist the researcher to address the issues arising in the construct validity that is related to the measurement of scale. The investigation conducts a Cronbach alpha test to solve the issues related to the scale measurement reliability. As for the last analysis, Partial Least Square Path Modeling is measured with an attempt to examine the bond between latent variables.

According to Hair et al. (2014), PLS-SEM is a prediction-oriented, variance-based approach to SEM that is based on limited predictions about the distribution of the variables. PLS-SEM denoted as PLS path modelling (PLS-PM) is primarily used to extend theories in exploratory researches by focusing on explaining the variance in the dependent variables as the researchers measured the model (Hair et al., 2013). PLS is justified to have assumptions on non-normal data, small sample sizes and formatively measured constructs (Hair et al., 2014).

Hair et al. (2013) insisted that PLS-SEM can extensively estimate the path relationships in the model using the available data as it minimizes the error terms, which is the residual variance of the endogenous constructs. The researchers further listed the data and model characteristics of PLS-SEM which included: (i) Sample size is small and or the data are non-normally distributed; (ii) Achieve a high level of statistical power with small sample sizes; (iii) Larger sample sizes increase the precision of PLS-SEM estimations; (iv) No distributional assumptions; (v) Handle extremely non-normal data; (vi) Handle construct measured with single and multi-item measures; (vii) Handle both formative and reflective measurement models equally well; (viii) Handle complex models with many structural model relations; (ix) Larger numbers of indicators are helpful in reducing the PLS-SEM bias.

Moreover, researchers also benefit from high efficiency in parameter estimation when applying PLS-SEM instead of CB-SEM (i.e. Covariance-based SEM). To sum up, the several reasons why we use PLS to calculate the measurement model are:

• PLS can be used for theory confirmation or theory development
• PLS makes fewer demands regarding sample size (e.g. <20)
• PLS better than CBSEM when data were normally distributed, with a small sample size and correlated exogenous variables
• PLS does not require normal-distributed data (CBSEM use the usual maximum likelihood estimation method, which assumes multivariate normality)
• PLS can be applied to more complex structural equation model
• PLS able to handle both reflective and formative construct
• PLS is better suited for theory development than for theory testing
• PLS is especially useful for prediction (prediction intention - prediction is more important than parameter estimation)
• PLS has the ability to handle multicollinearity among IVs and small samples

The calculation of reliability and validity using PLS method use can be further viewed in these articles published. The influence of a moderator on the relationship also been measured in these articles:

1. Does the fear of new car technologies influence brand loyalty relationship?

2.The moderating role of technology anxiety on BSQ, BI and their relation to brand loyalty

That's all for now. 

I will update on how to measure PLS model in the next post. 

Wassalam.

Love

Dr SAA