1. Jargon & Basic Concepts

2. Questions Define and illustrate: Population, Sample
Parameter, Statistic
Descriptive, inferential statistics
Random selection (sampling), assignment
Internal, External validity
Discrete, continuous variables
Scale types (nominal, ordinal, interval, ratio)

3. Population vs. Sample
Population – collection of all the objects of interest to researcher (you).
College students, students at USF
Sample – subset of objects from the population
Want a representative sample
Samples are relatively practical
Random samples have good properties
One person’s sample is another’s population

4. Parameter vs. Statistic
Parameter – numerical summary of population
E.g., mean, standard deviation
Statistic – numerical summary of sample
Typically we compute statistics and estimate parameters using statistics.

5. Descriptive vs. Inferential
Descriptive statistics describe a sample
How tall are these students?
Inferential statistics use sample statistics to make decisions about populations.
Is one method of instruction better than another?

6. Random Select & Assign
Random selection is a process of picking a sample from a population so that each element has the same probability of being sampled.
E.g., lottery, every 3rd name from a list (this is actually a systematic sample but it’s good)
Random assignment is assignment to treatment so that each element has an equal probability of being assigned to each treatment.
E.g., lottery, every other name, etc.
Both are typically accomplished by lists (aka frames) and computer generated numbers (e.g., SAS PROC PLAN)

7. Internal, External Validity
Internal validity - quality of inferences about the study itself. Random assignment, history, maturation, etc.
External validity – quality of inferences from the study to the larger domain of interest. Representative sample of participants, task relevance, behavioral consequents, etc. Aka generalizability of the results (but not generalizability study).

8. Variable & Distribution
Variable vs. constant
Attribute either varies across objects or not
Distribution: Collection of data
Distribution: Array of scores
Height
Beck Depression Index
Rat bar press
Wonderlic

9. Discrete vs. Continuous
Math
Integer vs. real numbers
Data
Categorical vs. continuous (many valued, ordered)
Examples
Political party, job satisfaction, response time, country of origin

10. Scale types Nominal, ordinal, interval, ratio
Nominal – categories. No ordering; mean has no connection to attributes
Ordinal – rank order only
Interval – rank order plus equal interval. ratio of differences has meaning
Ratio – rank order, equal intervals, rational zero point. Ratio of numbers has meaning.

11. Scale Types: Footrace review
Nominal
Ordinal
Interval
Ratio
ID number
Rank order of finish
Time of day of finish
Elapsed time from start
043 1
10:57 a.m.
4 min
011 2
10.59 a.m.
6 min
136 3
11:01 a.m.
8 min
112 4
11:02 a.m.
9 min
086 5
11:04 a.m.
11 min

12. Review Find a partner to work on this exercise.
Suppose you want to know whether one brand of tennis shoe is better than another. You have about $10K from a grant to study this. Describe a study you might conduct to find out. What might be your population, sample, independent and dependent variables? What statistics might you want to compute? Never mind the actual statistical test at this point. What data would you gather? What might a critic say about the internal and external validity of your study? What scale types are your IV and DV?