How to Download and Install SmartPLS

Steps To Download and Install SmartPLS

1.      Initiate the SmartPLS website

 2.      Register  in the SmartPLS website by filling in your particulars and submit

 3.      Within three days after registration, a key will be sent to you that can be used to activate the software after it has been downloaded.

 4.      Every three month, the website management will ask you to provide a new key that can be generated from the website as in the following

Good questionnaires are difficult to construct; bad questionnaires are difficult to analyze.

The questionnaire designed to collect the data of the study is the instrument that should be calibrated and its validity and reliability should be examined before it is to be used. This implies rigorous methodology to study the existing literature and review the available measures and exert a great attention to the development and the assessment of the questionnaire.

That is why GOOD QUESTIONNAIRE IS DIFFICULT TO CONSTRUCT.

The data generated based on ill-developed questionnaire will have low quality and may not be useful to the phenomenon under investigation. In addition, the conclusions drawn based on that will be poor and lack the reliability and validity.

That is why BAD QUESTIONNAIRES ARE DIFFICULT TO ANALYZE.   

KMO and Bartlet’s Test in Factor Analysis

Question:

How to use KMO and Bartlett's Test to Check Whether or not the Factor Analysis can be applied to my Data? 

 Answer:

Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity are very important measures to conclude the worthiness of factor analysis. KMO takes values between 0 and 1. A value of 0 indicates that the sum of partial correlations is large relative to the sum of correlations, indicating diffusion in the pattern of correlations and the factor analysis is not appropriate to be conducted. A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors. 

In other words, KMO indicates the amount of variance shared among the items designed to measure a latent variable when compared to that shared with the error. Kaiser (1974) recommends accepting values greater than 0.5 as acceptable. More specifically, values between 0.5 and 0.7 are considered mediocre, values between 0.7 and 0.8 are considered good, values between 0.8 and 0.9 are deemed great and values above 0.9 are superb (Hutcheson and Sofroniou, 1999). A value more than 0.7 is the common threshold for confirmatory analysis (Hair et al., 2010).

Before being able to run the factor analysis, one should ensure that the data has an adequate level of multicolinearity, the multicolinearity issue is not desirable in regression analysis but it is a prerequisite here. Bartlett's measure tests the null hypothesis that the original correlation matrix is an identity matrix.

H₀:The Correlation Matrix= I(Identity Matrix)

H₁: The Correlation Matrix≠ I(Identity Matrix)

The identity matrix is the matrix in which all the diagonal elements are ones and the off diagonal elements are zeros. Meaning that there original data has no correlations among its variables.

Factor analysis cannot be performed on the data for which the correlation matrix is the identity matrix. Therefore, we want this test to be significant (i.e. has a significance value less than 0.05). If the P value is less than 0.05 we have to reject the null hypothesis thus there are some relationships between the variables we considered in the analysis.

T Test, One Way ANOVA, and the Two Way ANOVA

T test is used to examine the equality of the means between TWO groups or categories. For  instance, if the purpose is to test how different are the males from the females who are working in an organization in terms of job satisfaction, T test can be used. In this case, the Gender is the Independent variable (IV) and the Job satisfaction is the dependent variable.

If the IV has more than TWO categories, like the race and the religion for example, the ONE ay ANOVA will be the test to examine the equality of the means across the Race groups. As an example in Malaysia, if the purpose is to examine the differences among Malay, Chinese, and Indians who are working in an organization in terms of job satisfaction, the ANOVA test can be used.

Based on the levels of TWO IVs, if the purpose is to examine how INTERACTION between  the TWO IVs can make the differences among the categories, the TWO WAY ANOVA can be applicable. Referring to Job satisfaction example, if one wants to examine whether the levels of job satisfaction across the race groups is affected by the gender variable, TWO WAY ANOVA or Factorial ANOVA can be used

SmartPLS Workshop 5th & 6th June 2013

In a two-day workshop, we were discussing how the PLS path modeling could be conducted using SmartPLS. With more than 20 Participants, the discussion started with some general overview of the current PLS related literature and why researchers are increasingly giving more attention to employing PLS in their analysis of the research in various fields

The two main steps in conducting SEM analysis were discussed. Specifically, the participants were guided on how to establish the validity and reliability of the outer, measurement, model before being able to examine the structural model and test the hypotheses. Next, the model predictive relevance was the topic of discussion and the participants were involved in hands-on learning to ensure their ability to perform their analysis. In the last session, moderation and mediation analysis were discussed and the participants were engaged in conducting the analysis and testing 
.the related hypotheses

For me, I learned a lot in that workshop, Thank you dear friends and hope we can have other future
workshops to enhance our knowledge towards producing a high quality research

Have a Nice Time Always    

Data Analysis Using SPSS

In the doctoral training module that had taken place on 4th June, we have discuss in general some of the aspects related to analyzing the data using SPSS. Further information are in the attached slides

Data Analysis Using SPSS