1. Research & the Role of Statistics Variables & Levels of Measurement

2. The Structure of Research & The Role of Statistics

3. Begin with Broad Questions
Most social research originates from some general problem or question
Curious/troubled about some aspect of society

4. Begin with Broad Questions
Example: What influences how a child does in school?
General question that can’t be adequately addressed by 1 study

5. Narrow Down, Focus In
Next, we come up with a more specific research question
one we can realistically address
Here, a review of the scientific literature can serve as a guide
Tells you what other researchers have found
Gives “bearing” to your research study

6. Narrow Down, Focus In
Example: What is the relationship between family structure and school performance?

7. Narrow Down, Focus In Also can be stated as a causal theory –
an explanation of the relationships b/t phenomena
Example: Children with more parental support/guidance will tend to perform better in school.

8. Theory
Children with more parental support/guidance will tend to perform better in school.
Underlined terms are concepts – abstract ideas
concepts are ambiguous

9. Operationalize
operationalize – define a concept in a way that it can be measured

10. Operationalize Put another way: turning concepts into…
variables
something measurable
any trait that can change values from case to case
Some concepts easier to operationalize than others
Examples:
Parental support/guidance  # parents in home (1 or 2)
School performance  GPA (1 to 4)
OTHER OPERATIONALIZATIONS?

11. Group Exercise: “Operationalization”
Working with the person (or 2) closest to you, come up with variables (something measurable) that could be used as indicators of the following concepts:
Healthy lifestyle (of an individual)
Economic health of Duluth
Success of UMD grads

12. Operationalize Hypothesis: derived from theory
statement about a relationship between variables
therefore:
it is more specific/exact than a theory
it is testable

13. Operationalize Hypothesis example: Independent variable (x)
Students living in homes with 2 parents/guardians will tend to have higher GPA’s than students from 1-parent households.
Independent variable (x)
cause (i.e., # of parents)
Dependent variable (y)
effect or outcome measure (GPA)
x  y

14. Observe Observations allow for hypothesis testing
Science is a systematic method for explaining empirical phenomena
Empirical means measurable & observable

15. Observe Research methods are the tools used at this stage
How are data to be sampled & gathered?
Lab experiment?
Survey?
Analysis of existing data?
Observations produce data
Observation vs. Anecdote

16. Analyze Data & Reach Conclusions
Our focus in this class:
hypotheses are tested by comparing observations to theoretical predictions
Statistical procedures give the ability to tell:
whether the data support our hypotheses
& by extension, whether our theory is supported

17. Analyze Data & Reach Conclusions
Two classes of statistical techniques:
Descriptive – used to summarize/organize/ describe data.
Example: What is the avg. # of hours per week people spend on face book?

18. Analyze Data & Reach Conclusions
Two classes of statistical techniques:
2. Inferential – used to generalize findings from a sample to a population
Example: polling just a few hundred voters to predict how a presidential election will turn out.

19. Generalize Back to Questions
What do the results tell us about our original broader question?
Determined by:
How theories are operationalized
The nature of the observed sample

20. Variables 101
VARIABLES are any trait that can change values from case to case
Attribute – specific value on a variable
Example: sex has 2 attributes, male & female
Variables ALWAYS should:
be exhaustive – variables should consist of all possible values/attributes
have mutually exclusive attributes; no case should be able to have 2 attributes simultaneously

21. Levels of Measurement
Nominal – mutually exclusive & exhaustive categories that cannot be meaningfully ordered (e.g., sex, religion, political affiliation)
Categories need to be relatively homogenous

22. Levels of Measurement A B D *With parents *With roommates *Dorms
Scales for Measuring Students’ Living Arrangements A B D
*With parents
*With roommates
*Dorms
*Apartment
*House
*Apartmnt
*Other

23. Levels of Measurement
2. Ordinal – categories can be ranked in addition to being categorized.
Example: “I would rather get beat with a lead pipe than attend this class.”
1 = strongly disagree
2 = disagree
3 = neutral
4 = agree
5 = strongly agree

24. Levels of measurement What’s Wrong with This Question:
How long have you been attending UMD?
1 to 11 months
1 to 2 years
2 to 3 years
3 to 4 years
5 or more years

25. Levels of measurement 3. Interval-Ratio – categorical units are equal
Examples: prison sentence in months, population of Duluth, age
This level permits all mathematical operations (e.g., someone who is 34 is twice as old as one 17)
Pointy headed issue
Interval = no meaningful zero point
Ratio = meaningful zero point
DOESN’T MATTER ONE BIT FOR DATA ANALYSIS
SPSS calls both interval and ratio variables “SCALE”

26. Group Exercise
Research Hypothesis = Males who experience hair loss become more likely to experience depression.
What is the IV? What is the level of measurement for this variable?
What is the DV? Operationalize the DV so that it is measured at the nominal, ordinal, and interval/ratio levels.