DISTINCTIONS. Initial Data Analysis. Some Distinctions. Population vs. Sample Descriptive vs. Inferential stats Variables Types of data Quantitative versus Categorical Measurement scales. Population. The entire collection of events that you are interested in generalizing to .
Control1 vs. Experimental group
Oftentimes we want to look at the effects of some treatment e.g. a drug, teaching strategy, memory technique etc.
To study the effects of the treatment we’ll often give one or more groups the treatment and one group no treatment and then compare the groups
Random assignment reduces the likelihood that groups differ in some critical way other than the treatment since everyone has an equal chance to be put in one of the treatment groups.Random Assignment
Example: Stop-smoking study.
Now we must select the variables we wish to study, with the term variable referring to a property of an object or event that can take on different values.
Example: # of cigs smoked, abstinence after one week (yes or no).
Note the distinction; # of cigarettes smoked is a continuous variable, whereas abstinence is a categorical variable.
A variable is to be contrasted with a constant, that which only takes on one value.1Variables
Continuous vs. Discrete
Example: GPA during college vs. GPA for class
Example: 9 point “Likert” scale- continuous or discrete? 20 point?
Categorical (frequency, nominal, qualitative) Data
Named data e.g. different brands, political party, race, gender
How you think about your data and what scale of measurement your variables are is very important. What you decide about the variable will have a say on the analyses available, and even possibly even have vast effect on the theory itself.
Early developmental theories suggested clear cut stages which imply categories of developmentTypes of Data
Consider the grouping variable in which people are classified as In-patient, Out-patient, and Control groups.
There is only one variable in a theoretical sense, and our goal is to determine the relationship between the grouping variable and the outcome, and an ANOVA table in this sense would speak to the overall effect group membership has on the outcome.
Statistically speaking there are actually two coded variables if we are applying the general linear model
See dummy coding, effects coding etc.1
What you want to keep in mind is that a single group has no relationship with the outcome, as membership in a single group is a constant.
Which one is predicting and which is being predicted?
Both predictor and dependent variables can be quantitative or categorical
Example: Whether or not we give a subject the stop-smoking treatment would be the independent variable, and the # of cigarettes smoked would be a dependent variable.
Other examples: age:income, shoe size:intelligence, gender:hostility, intelligence:voting outcomeVariables
Examples: graphing, calculating, averages, looking for extreme scores.
Exploratory/Initial data analysis (Tukey, Chatfield, others) typically relies on descriptive information mostWhat Do We Do With The Data?