Research Methods II.2

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Research Methods II.2
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2014-04-24 14:56:14
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updated gspm research nachmias
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  1. Steps of Hypothesis Testing 1
    research hypothesis - an empirically testable statement derived from theory. It is stated in terms of the population. It holds for the whole population, but we only have a sample.
  2. Steps of Hypothesis Testing 2
    null hypothesis - the opposite of the research hypothesis given as a fixed value.
  3. Steps of Hypothesis Testing 3
    Assume the null - to derive a sampling distribution. A mathematical probability distribution, we do not find it empirically
  4. Steps of Hypothesis Testing 4
    Determine the critical region - 5% or less
  5. Steps of Hypothesis Testing 5
    Calculate a test (sample) statistic
  6. Steps of Hypothesis Testing 8
    Make a decision - infer about research hypothesis
  7. Steps of Hypothesis Testing 6
    One tailed test: a statistical test where extreme results leading to rejection of the null hypothesis can be located at only one tail

    Two Tailed Test: A statistical test where extreme results leading to rejection of null hypothesis will be located at both left and right tails
  8. Steps of Hypothesis Testing 7
    • Type 1 Error: Rejecting a True Null
    • Type 2 Error: Affirming a False Null
  9. What is a significance level?
    Probability of a type 1 error
  10. What are confidence levels?
    The probability that the value of a parameter falls within a specified range of values. Constructed with Z distributions.
  11. Explain what happens to a sampling distribution as sample size increases and why this occurs.
    As sample size increases sampling distribution decreases and vice versa. This occurs because there is a smaller chance of an error.
  12. What is a PRE?
    How much better can we estimate Y, the dependent variable, using X, than if we estimate Y by itself
  13. Five Measures of Bivariate Association (description, associated levels, problems)
    1. Lambda

    2. Gamma

    3. Kendall's Tau

    4. "r squared"

    5. "r" not PRE
  14. Elaboration
    For nonspurious bivariate relationships.

    Usually involves the introduction of other variables to determine the links between the independent and dependent variables, or specification of the conditions under which association takes place.
  15. What can happen when you control for a third variable in a bivariate relationship? (3 Things)
    1. Nothing (Z has no effect)

    2. The correlation between X and Y reduces drastically. This may mean: spuriousness OR Z is an intervening variable

    3. The relationship between X and Y changes but does not disappear: then Z is a conditional variable
  16. What is Lambda?
    (PRE) measure of association for two nominal variables or 1 nominal+1 ordinal variable. Problem: it equals zero when the modes all fall in one value of the dependent variable. Then you use a percentage comparison instead.
  17. What is Gamma?
    (PRE) measure of association for two ordinal variables. Can be negative, varies from -1.00 to +1.00; Problem: it inflates the correlation because it doesn't include “ties"
  18. Kendall's Tau?
    A measure of association for two ordinal variables. It corrects the gamma problem by including “ties.” NOT a PRE
  19. What is "r squared" for?
    It is a PRE, scale of 0 to 1, for intervals and ratios
  20. What is "r" (non-PRE)?
    Uses a scale of -1 to 1, both assume linear relationship, for intervals and ratios
  21. What factors are appropriate to consider in control and elaboration? (5)
    • Demographics
    • Time
    • Place
    • Interest
    • Concern
  22. Discuss the major issues in questionnaire construction (5 Things)
    What do you want to know? What do you expect respondents to know?

    Question Content: Fact or Opinion, wording, provide middle alternatives

    Question Type: Closed vs Open-ended

    Response Format for Closed-ended

    Question order
  23. 8 Considerations for evaluating a poll
    1. Topic: is it something people know about?

    2. Screen for non-attitudes – Did the organization give respondents middle alternatives?

    3. Question wording: Did the organization give you the question wordings?

    4. Question Order: Questions can be placed to lead respondents in certain ways

    5. Sample definition: Probability sample? From what population?

    6. Sub-sample sizes and error margins. What happens if certain demographics are pulled out? (error margin increases)

    7. Who sponsored the poll?

    8. What's the spin?
  24. Interviewer-administered Surveys - Advantages (3)
    Higher response rate

    Can probe and clarify

    Quick turnaround
  25. Interviewer-administered Surveys - Disadvantages (3)
    High costs

    Possible interviewer bias

    No anonymity
  26. Self-administered Surveys - Advantages (4)
    Low cost

    No interviewer bias

    Greater anonymity

    Considered responses
  27. Self-Administered Surveys - Disadvantages (4)
    Lower Response Rate

    Requires simple questions and answers

    Nobody there to clarify/probe

    Takes time (opportunities for outside phenomena to interfere)
  28. Secondary Analysis - Advantages (3)
    Less expensive

    May be the only source

    You can replicate/triangulate someone else's research with your own
  29. Secondary Analysis - Disadvantages (3)
    May only approximate the data you want

    May have hidden biases

    Sometimes access is a problem
  30. Focus Groups - Advantages (5)
    Less prep time

    Less expensive

    Don’t need to construct a survey

    With a survey you only get what you ask for

    In depth information
  31. Focus Groups - Disadvantages (3)
    No external validity

    Results open to interpretation

    Demand characteristics: People know they are being recorded/films. So people may not want to state their true feelings

    Dominant personalities
  32. Issues of Ethics in Conducting Research (5)
    Settings and procedures

    Informed consent: full disclosure, individual comprehension, competence

    Privacy: public/private settings

    Confidentiality

    Anonymity
  33. 8 Steps of Hypothesis Testing
    Research Hypothesis: an empirically testable statement derived from theory. It is stated in terms of the population. It holds for the whole population, but we only have a sample. 

    Null Hypothesis: the opposite of the research hypothesis given as a fixed value.

    Assume the Null: to derive a sampling distribution. A mathematical probability distribution, we do not find it empirically

    Determine the Critical Region: 5% or less

    Calculate a test (sample) statistic

    One and Two Tailed Tests: One tailed test: a statistical test where extreme results leading to rejection of the null hypothesis can be located at only one tail. Two Tailed Test: A statistical test where extreme results leading to rejection of null hypothesis will be located at both left and right tails

    2 Error Types: Type I error is to rejecting a true null. Type II is failing to reject a false null.

    Make a decision - infer about your research hypothesis.

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