Research & the Role of Statistics Variables & Levels of Measurement

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

2. The Structure of Research & TheRole 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: • 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: • beexhaustive – 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 Scales for Measuring Students’ Living Arrangements

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.