It has been an awesome experience navigating the SmartPLS platform, and I am happy that I am able to replicate all that I have read and learned in creating a model on this platform. Though I am still reading and interpreting my results, I am quite satisfied with what I have achieved so far. Here is a recording of how I have been able to create a model on the PLS algorithm. I will be exploring Bootstrapping in my next video.

At the beginning of this course, I indicated that I wanted to learn how to use Smart PLS and possibly the R software for data analysis. During the second week, I explored what smartPLS is about and how to download the software, and I also did some readings. During the third week, I signed up for an online workshop on the R studio, where I learned about the user interface of R studio and how to run some commands. I must tell you that the R software is very difficult to navigate, and I doubt if I can be very efficient in using that software.

However, as I began exploring the SmartPLS user interface, I realized that I needed to familiarize myself with some key ideas and the meaning of some statistical techniques to enable me to explore the assessment model in SmartPLS. These techniques include exploratory factor analysis (EFA), confirmatory factor analysis (CFA), path analysis, and bootstrapping. So I had to check on YouTube as my go-to learning platform.

To further enhance my learning, I came across this slideshare presentation, Confirmatory factor analysis overview, which also provided guidance on the rule of thumb for factor loadings on a specific model when doing CFA or EFA. I also used Practical Introduction to CFA and explored more on the path analysis using the below YouTube video, and I was able to get a soft copy of the 6th edition of Advanced and Multivariate Statistical Methods. 

I latter realized that I will need a set of data to enable me to explore this software effectively. Why I am busy reading up on the various techniques, I also had to ask a colleague to help me share an online survey I drafted for this purpose.

Just as I said earlier, my goal is to be able to run a sophisticated and complex statistical analysis that involves structural equation modeling. I want to do a video of my learning, but this can only happen when I start running my own model. which I think I will begin in my next phase since the participants that had access to my questionnaire through my colleague have started responding. But I will first need to clean up the data and do a code book from an Excel sheet before transferring the data to the smartPLS platform. This is what my week 5 and 6 update will entail because coding participants responses is a bit of work.

My second week started with a mission to explore the R software program and R Studio. Seriously, I have never done this before, nor am I good with statistics, but I must tell you that my week was not so good because the learning was a bit on the edge for me. As mentioned in the beginning of this class, I am trying to learn the R and Smart PLS software for data analysis purposes.

I have managed to pull through the first stage of the SmartPLS, which is understanding the user interface, and I thought the same should be done for the R software as well. As part of my learning process, I had to join an online workshop titled R for Data Visualization. To better understand the R platform and how it has been used, the facilitator shared some helpful resources for using R and ggplot ( Data Visualization with ggplot Cheat Sheet and the R Graph Gallery.

ggplot is the tool used for visualizing data, while gapminder is the tool where the data is extracted from.

• Firstly, download the R software from https://www.r-project.org/
• Then I downloaded the RStudio from https://posit.co/downloads/ (We were instructed during the workshop to use the Free/Open Source version of RStudio Desktop, not the Pro version)
• Please note that one will be asked to select a CRAN Mirror during the download and installation process. You can choose one of the three Canadian CRAN mirrors:
  • https://mirror.rcg.sfu.ca/mirror/CRAN
  • https://muug.ca/mirror/cran/
  • https://mirror.csclub.uwaterloo.ca/CRAN/

I had to learn the terminologies used on the platform, which included the meaning of keywords like

• Character data: words and letters (called “strings”)
• Numeric data: whole numbers or decimal places, can be positive or negative
• Integer data: whole numbers, can be positive or negative
• Logical data: TRUE or FALSE
• Vector: a sequence of data elements of the same type (i.e., only character or only numeric)
• Boolean: operators meaning and (&), or (|), not (!) which let us combine inputs
• Function: an action being performed on an object (or argument). For example, in class(x), class() is the function
• Argument: the object for a function. For example, in class(x), x is the argument
• Optional argument: an object you don’t need to include for the function to work. For example, in round(sqrt(10), digits=2), digits=2 is an optional argument.
• Non-optional argument: the necessary argument for the function to work. For example, in round(sqrt(10), digits=2), sqrt(10) is a non-optional argument.
• Library or package: a suite of specialized functions for different types of data or different projects
• Tibble or Dataframes: tabular data
• Vectorized operation: operations, such as adding, subtracting or multiplying, that can be applied to two vectors in parallel
• For loops: a way to repeat a block of code
• Conditional: an if-else statement

After this, I learned how to get help in R as well as the common commands and functions needed to work effectively on the platform. My intention is to see which of the two softwares will be easy to use. I think i’ll rather go with the SmartPLS since I have explored the user interface of both softwares. In my next post I will be looking at how to build models usings the SmartPLS.