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1. Research & the Role of Statistics 2. Variables & Levels of Measurement

This article discusses the role of statistics in research, specifically focusing on variables and the different levels of measurement. It covers how to narrow down research questions, operationalize concepts, form hypotheses, observe and analyze data, and reach conclusions. The article also explains the different levels of measurement and provides examples for each level.

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1. Research & the Role of Statistics 2. Variables & Levels of Measurement

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  1. 1. Research & the Role of Statistics2. Variables & Levels of Measurement

  2. 1. 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)

  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

  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 cell phones?

  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. 2. Variables & Levels of Measurement • Reminder: • 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. 3 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. 3 Levels of Measurement Scales for Measuring Students’ Living Arrangements

  23. 3 Levels of Measurement Scales for Measuring Students’ Living Arrangements

  24. 3 Levels of Measurement Scales for Measuring Students’ Living Arrangements

  25. 3 Levels of Measurement Scales for Measuring Students’ Living Arrangements

  26. 3 Levels of Measurement Scales for Measuring Students’ Living Arrangements

  27. 3 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

  28. 3 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

  29. 3 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

  30. 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.

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