How to do a Pearson Correlation in JASP (13-9)
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- Опубликовано: 6 июл 2024
- Using a dataset about burnout and job satisfaction, we learn how to conduct a simple correlation, how to interpret it, and how to write it up in APA style. We begin by exploring the theoretical relationship among the variables to understand how correlation works. After we walk through the five steps of hypothesis testing, we open a .CSV dataset in JASP, explore the check the assumption of linearity and normality, conduct the test, interpret the results and write up the findings in APA style.
Cartoon explanation of Vovk-Sellke maximum p-ratio: jasp-stats.org/2017/06/12/mys...
Download the Friendly, Free, Flexible, Functional JASP software from the official JASP statistics website: jasp-stats.org
This video teaches the following commands and techniques in JASP:
Importing a CSV into JASP
Pearson correlation
This video uses the dataset JobSatisfaction.csv and JASP version 12.0
Bass Walker - Film Noir by Kevin MacLeod is licensed under a Creative Commons Attribution license (creativecommons.org/licenses/...)
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Artist: incompetech.com/
Link to a Google Drive folder with all of the files that I use in the videos including spreadsheets, the Bear Handout, and the JobSatisfaction.csv dataset. As I add new files, they will appear here, as well.
drive.google.com/drive/folder...
To download, hover your cursor over the file icon and a blue download icon will appear. You do not need to request access to a file.
Very comprehensive tutorial! It helped me a lot in my assignment. Will be watching more from your playlists. Thank you!
Thank you so much for your video!
You are so welcome!
Hi, I appreciate your videos. I would like to ask whether we need to have the write-up of the assumption test before we explain the correlation?
Excellent video, I learned a lot. I just have one question. How would you make the correlation matrix with values for male participants above the diagonal and with values for female participants below the diagonal?
That is a GREAT question. I think that I would use the format with three rows and columns for each variable. I would report one set of correlations for the overall, then one each for male and female. Remember that you would be reporting a point biserial correlation. Report exactly what the software gives you, but it is technically a different test.
thank you 😄
How did you identify null, alternative and the level of significance?
Hello! How do I create a data set with p < .05 and p < .01, sir? 😊
Your data set is just the numbers from your research. You can set your significance level at .05 or at .01 for a specific test (like correlation or a t-test). In SPSS , the default in .05, but there is usually a setting where you can adjust it depending on your test. Good luck
Please we need SEM MODLLING ...
Please please please make a general linear mixed model for germination data or eggs hatching or something along those lines.
I added your request to my list of upcoming videos. I will have one on mixed models in ANOVA - a repeated measures ANOVA with a between subjects factor. Hopefully that will help. Cheers.
hello can i delete the number on the heatmap box? so it just showed colors
I do not see the jobsatisfaction.csv file in the Google Drive folder!
I added that CSV. Should be there now. Thanks
Where did you get r^2 effect size that made you state a conclusion that the level of burnout that the counselor experienced accounted for a large portion (42%) of the variability in job satisfaction?
That was from my dissertation. I don't think the example was part of this video. That was illustrating how you would write up such a finding in APA style
@@ResearchByDesign thanks for a quick reply!
How do I set the significance level, sir?
You, as the researcher, select the significance level and it can be any level that you choose. That said, the most common and acceptable levels are .05, then .01 and .10. If you choose significance levels other than those conventional levels, you should explain why in your write-up. Choosing .05 is the safest and most common.