Worksheet 8

Packages

library(tidyverse)
library(car)
library(lme4)

Neurocognition in individuals with schizophrenia

A study was carried out to evaluate patterns and levels of performance on neurocognitive measures among individuals with schizophrenia and schizoaffective disorder using a well-validated, comprehensive neurocognitive battery specifically designed for individuals with psychosis.

The main interest was in determining how well these measures distinguished among all groups and whether there were variables that distinguished between the schizophrenia and schizoaffective groups. Age and sex were also measured for each individual, but we ignore those in our analysis.

Variables of interest, all quantitative except for the first one:

• Dx Diagnostic group, categorical with levels Schizophrenia Schizoaffective Control
• Speed Speed of processing score
• Attention Attention/Vigilance score
• Memory Working memory score
• Verbal Verbal Learning score
• Visual Visual Learning score
• ProbSolv Reasoning/Problem Solving score
• SocialCog Social Cognition score

The clinical sample comprised 116 male and female patients who had a diagnosis of schizophrenia (\(n = 70)\) or schizoaffective disorder (\(n = 46\)) confirmed by a standard test.

Non-psychiatric control participants (\(n = 146\)) were screened for medical and psychiatric illness and history of substance abuse. Patients were recruited from three outpatient clinics in Hamilton, Ontario, Canada. Control participants were recruited through local newspaper and online classified advertisements for paid research participation.

The data are in http://ritsokiguess.site/datafiles/NeuroCog.csv.

1. Read in and display (some of) the data.

2. Why is this dataset suitable for a multivariate ANOVA analysis? Explain briefly.

3. Create a suitable response variable for a MANOVA. Show the first few rows of your response variable. NOTE: if you display it all, all 242 rows will be displayed. Be careful.

4. Run a suitable MANOVA using the manova command, displaying the output.

5. Run a suitable MANOVA using Manova from the car package, displaying the results.

6. What are you able to conclude from your analyses? (The conclusion should be the same for both of them.)

7. Carry out Box’s M test. What do you conclude from it?

8. Make boxplots of all seven response variables against diagnosis (Dx), on one ggplot. The best answer will display the graphs so that they are easy to read. Hint: the idea is the same as plotting residuals against all of the explanatory variables in a regression.

9. Looking at your boxplots, why do you think your MANOVA came out significant, and what do your boxplots tell you about the relative test scores for patients with diagnoses of schizophrenia or schizoaffective?

The questions below are on repeated measures. We likely won’t get to the material for questions 14 and 15 this week; if we don’t, leave these questions for next week. (Assignment 6 won’t go any further than we go in lecture.)

You have intelligent rats

Each of 12 students trained rats to run a maze, and the number of successful runs (out of 50 attempts) was recorded on each of 5 days. Some of the students were told that the rats they were training were “bright” (intelligent) and some of them were told that their rats were “dull” (not intelligent). In actual fact, all the rats were from the same source, and none of them were any more intelligent than the others. Did it make a difference whether the students were told that their rats were intelligent on the number of mazes the rats successfully ran, and, if so, was the effect consistent over time? The same group of rats were trained by the same student throughout this experiment, so it makes sense to treat the data as repeated measures.

The data are in http://ritsokiguess.site/datafiles/intelligent-rats.csv. The columns of interest to us are:

• Student: a numerical identifier for each student
• Treatment: what the student was told about their rats
• Day1 through Day5: the number of successful runs on each day.

There are some other variables that will not concern us here.

10. Read in and display (some of) the data.

11. Set up and run an appropriate repeated-measures ANOVA. What do you conclude from it? (Hint: set up the response variable, and go through the steps required to run the Manova. Obtain the appropriate P-values, describing your process.)

12. Draw a suitable interaction plot (for treatment and time). How does this clarify your conclusions of the previous part? (Hint: you’ll need longer data. If you want to be consistent with me, use Day for the names of the days, and runs for the numbers of runs.)

13. For each time point (day), run a simple effects analysis: that is, using an ordinary aov, test whether the number of successful runs depends on whether the rat was labelled as “dull” or “bright”.

14. Do a mixed model analysis (hint: use the long data that you made for your interaction plot.)

15. Compare your findings from the mixed model to the ones from the Manova analysis you did earlier.