Meet and Repeat: PART I
Welcome to the Analysis of longitudinal data chapter.
Many studies in the behavioral sciences involve several measurement or observations of the response variable of interest on each subject in the study. For example, the response variable may be measured under a number of different experimental conditions or on a number of different occasions over time; such data are labelled repeated measures or longitudinal data. In the first part (I) of this chapter useful methods for the graphical exploration of this type of data are described and a simple method for their analysis are introduced, with the warning that although simple the method should be used only in the initial stage of dealing with the data; more appropriate methods will be discussed in part II.
In the first part we will dwelve in to the BPRS data, in which 40 male subjects were randomly assigned to one of two treatment groups and each subject was rated on the brief psychiatric rating scale (BPRS) measured before treatment began (week 0) and then at weekly intervals for eight weeks. The BPRS assesses the level of 18 symptom constructs such as hostility, suspiciousness, hallucinations and grandiosity; each of these is rated from one (not present) to seven (extremely severe). The scale is used to evaluate patients suspected of having schizophrenia.
This exercise is part of the course
Helsinki Open Data Science
Exercise instructions
- Read the
BPRSdata into memory - Print out the (column) names of the data
- Look at the structure of the data
- Print out summaries of the variables in the data
- Pay special attention to the structure of the data
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Read the BPRS data
BPRS <- read.table("https://raw.githubusercontent.com/KimmoVehkalahti/MABS/master/Examples/data/BPRS.txt", sep =" ", header = T)
# Look at the (column) names of BPRS
names(BPRS)
# Look at the structure of BPRS
# Print out summaries of the variables