plot(lm_primary_data
This is not ggplot, I would recommend using ggplot because the look of the graph will be a lot more customizable
15 Matching Annotations
- Last 7 days
-
avishnevetsk.quarto.pub avishnevetsk.quarto.pub
-
-
geom_histogram(binwidth = .5)
With your scale for the x-axis, you should edit the binwidth so each bar on the histogram is more visible
-
-
yoominkim.quarto.pub yoominkim.quarto.pub
-
While these results expand on earlier research, the study had some limitations
Probably best to make limitations its own paragraph
-
Gender Differences in Self-Efficacy
I like the structure of this section! I don't have any edits to make here.
-
Print the Test
I would add some explanation between each output. What do these numbers mean for your results?
-
Visualize
If possible, change "GENDER01" just to "Gender" as the legend title
-
Transform
Your inline comments are very good, I would recommend adding just a small paragraph at the end of this section explaining your preprocessing (removing NAs, transforming variables, etc)
-
This study explored whether self-efficacy differed between genders, asking the research question: Are there differences in different groups of self-efficacy based on gender?
I think the inclusion of the research question feels a bit forced and awkward. Unless Dr. Shane specifically said you have to include your research question like this, I would reword the sentence to just start with "This study explored..." and then just explain what you did rather than end the sentence with a question mark.
-
-
ghurford.quarto.pub ghurford.quarto.pub
-
Discussion
Overall solid section, I like how you briefly reexplain the process and what the relationships turned out to be. Additionally, it was good to propose an explanation for your results. If I were to add anything, it would be explaining why you suggest those certain topics for future research.
-
These findings fail to reject the null hypothesis,
Good!
-
yellow
Again, the yellow is not very visible
-
Motivations to Start Using Social Medi
I know there are figure captions for each graph, but you should also have a small summarization at the end of each section (1.4.2, 1.4.3, etc) that motivates why you chose certain variables and explains your results a bit.
-
"yellow"
I'm not loving the color choice here it is not very visible on a white background. Some people might have dark mode but it should be visible in both modes.
-
status_plot + labs (x = "Participants' perceived social status on a scale of 1-10 (ladder method)")
Graph title?
-
Transform
After this section, you should include a small paragraph explaining your preprocessing (2-3 sentences maybe). Generally no need to explain your library imports, but you should give some broad info on what all your variables cover.
-