Data Science – Business Analytics – Quantitative Research
This tutorial is about graphing a moderated mediation model with PROCESS for R, with Hayes’s PROCESS source code, and my MD2C graphing template.
Continue reading “Graphing a moderated mediation model with PROCESS for R (Model 14)”
This tutorial is about graphing a simple moderation model with PROCESS for R, developed by Andrew Hayes, and my MD2C graphing template. There is a Github Repository for it as well, containing the R code.
Continue reading “Graphing a simple moderation model with PROCESS for R (Model 1)”
This FAQ PROCESS overview can hopefully help you to use my templates to graph conditional effects. They are made for the PROCESS macro from Andrew Hayes to calculate conditional effects within SPSS.
For an example how to use this template, you are welcome to read our blogs:
https://www.md2c.nl/graphing-moderation-of-process-v3-0-model-1/
https://www.md2c.nl/graphing-conditional-indirect-effects/
I am very thankful for all the great feedback and questions we are receiving from fellow-researchers, and I hope that you will continue doing this.
With this list of Frequently Asked Question, I am trying to cover most of the questions, but please don’t hesitate to reach out to me!
FAQ PROCESS Question: I can’t see any graph, what goes wrong?
Answer: Please check first if you have “.” or “,” as a decimal separator. The templates require a “.” as decimal separator. A quick solution could be the usage of “find-and-replace” in Excel, where you replace the “,” for the “.” for the input rows. Secondly, please check the axis of the graph: sometimes you have to reset them by right-clicking on the required axis, select “Format Axis”, and reset the Minimum and/or Maximum Bounds values.
FAQ PROCESS Question: The axis are crossing the middle of the graphs (mostly at value 0), what can I do?
Answer: you can right-click on the required axis, select “Format Axis”, and set the value at which the axis crosses
FAQ PROCESS Question: There isn’t a template for my model, can you make one?
Answer: if you could contact us, and provide us the data (we won’t use it other than just for inspection and possible creation of the template), we can let you know if that is possible or not.
FAQ PROCESS Question: I haven’t received the download link, what can I do?
Answer: it could take some business days to process the paypal transfer, but sometimes, the email is in your spambox of emails are blocked. In any case: please feel free to reach out to me and I will make sure you will get your template. You can reach me by sending an email to info [at] md2c [dot] nl
FAQ PROCESS Question: I can’t download the template, what can I do?
Answer: please check your browser. The download works best with Mozilla or Edge, and does not work correctly with Chrome currently. In any case: please feel free to reach out to me and I will make sure you will get your template. You can reach me by sending an email to info [at] md2c [dot] nl
Imagine you are an HR-Manager, and you would like to know which employees are likely to stay, and which might leave your company. Besides you would like to understand which factors contribute to leaving your company. You have gathered data in the past (well, in this case Kaggle simulated a dataset for you, but just imagine), and now you can start with this Hands On Lab – Predict Employee Leave to build your prediction model to see if that can help you.
In this lab, you will learn how to create a machine learning module that predicts whether an employee will stay or leave your company. We are aware of the limitations of the dataset but the objective of this hands on lab is to inspire you to explore the possibilities of using machine learning for your own research, and not to build the next HR-solution.
We created a starting experiment for you on the Azure AI Gallery to give you a smooth start. Continue reading “Human Resources Analytics – Predict Employee Leave”
This blog is about graphing moderation with the help of SPSS with the PROCESS macro, and our corresponding MD2C Graphing template for PROCESS v3.0 Model 1 – Moderation.
The case that we used is based on the article of Chapman and Lickel (2016), and you can find a detailed elaboration of this case in Andrew Hayes’ second book about Introduction to Mediation, Moderation, and Conditional Process Analysis (Hayes, 2017). You can download the data from Hayes’ website. The datafile you need for this example is called DISASTER. Besides, you can also download the PROCESS V3.0 macro for SPSS and SAS (and much more) from the site: http://www.processmacro.org/
Continue reading “Graphing moderation of PROCESS v3.0 Model 1”
This short blog is about exploring the relationships between stress, satisfaction and self-evaluation. For an assignment of the course Introduction to Psychology, I had to gather 20 responses to answer some questions. Due to the huge amount of responses, I thought it could be nice to share the results to thank all of you for your participation. Continue reading “The effect of Stress on Satisfaction and Self-Evaluation”
NOTE: 11 December 2017 – This blog is about the PROCESS v2.16 version. We have also an example with PROCESS v3.0!
This blog is about graphing conditional indirect effects with the help of SPSS with the PROCESS v2.16 macro, and our MD2C Graphing moderated mediation Excel template. Continue reading “Graphing conditional indirect effects with the MD2C Excel Template”
This blog is part of a lecture about descriptive statistics and exploring graphs with SPSS. Some of the data is of the students themselves, and for other graphs, I used the datasets from Andy Field’s Discovering Statistics Using IBM SPSS Statistics, a book I highly recommend! Continue reading “Research Methodology – Descriptive statistics and exploring graphs with SPSS”
This blog gives you some reflections on predictive modeling and human interaction.
The nice thing of predictive modeling is that it gives you possible answers, which you could use to define you or your customers’ actions. You can classify things or trying to predict numbers, like your sales. Another nice thing is that you can retrain your models over time to get -hopefully, but not guaranteed- better results. Continue reading “Predictive modeling and human interaction”
This is a simple example about optimizing prediction models on Azure. In this case we will use a Boosted Decision Tree model. We will show you how you can use the Permutation Feature Performance module to prune your trees.
We start with the Student Performance Classifier from a previous blog. We already found out that the Boosted Decision Tree algorithm gave the best results, so we will start with that one to train our model with. Continue reading “Optimizing prediction models on Azure – pruning the trees”