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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.

Steps of Hypothesis Testing 2
null hypothesis  the opposite of the research hypothesis given as a fixed value.

Steps of Hypothesis Testing 3
Assume the null  to derive a sampling distribution. A mathematical probability distribution, we do not find it empirically

Steps of Hypothesis Testing 4
Determine the critical region  5% or less

Steps of Hypothesis Testing 5
Calculate a test (sample) statistic

Steps of Hypothesis Testing 8
Make a decision  infer about research hypothesis

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

Steps of Hypothesis Testing 7
 Type 1 Error: Rejecting a True Null
 Type 2 Error: Affirming a False Null

What is a significance level?
Probability of a type 1 error

What are confidence levels?
The probability that the value of a parameter falls within a specified range of values. Constructed with Z distributions.

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.

What is a PRE?
How much better can we estimate Y, the dependent variable, using X, than if we estimate Y by itself

Five Measures of Bivariate Association (description, associated levels, problems)
1. Lambda
2. Gamma
3. Kendall's Tau
4. "r squared"
5. "r" not PRE

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.

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

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.

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"

Kendall's Tau?
A measure of association for two ordinal variables. It corrects the gamma problem by including “ties.” NOT a PRE

What is "r squared" for?
It is a PRE, scale of 0 to 1, for intervals and ratios

What is "r" (nonPRE)?
Uses a scale of 1 to 1, both assume linear relationship, for intervals and ratios

What factors are appropriate to consider in control and elaboration? (5)
 Demographics
 Time
 Place
 Interest
 Concern

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 Openended
Response Format for Closedended
Question order

8 Considerations for evaluating a poll
1. Topic: is it something people know about?
2. Screen for nonattitudes – 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. Subsample 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?

Intervieweradministered Surveys  Advantages (3)
Higher response rate
Can probe and clarify
Quick turnaround

Intervieweradministered Surveys  Disadvantages (3)
High costs
Possible interviewer bias
No anonymity

Selfadministered Surveys  Advantages (4)
Low cost
No interviewer bias
Greater anonymity
Considered responses

SelfAdministered Surveys  Disadvantages (4)
Lower Response Rate
Requires simple questions and answers
Nobody there to clarify/probe
Takes time (opportunities for outside phenomena to interfere)

Secondary Analysis  Advantages (3)
Less expensive
May be the only source
You can replicate/triangulate someone else's research with your own

Secondary Analysis  Disadvantages (3)
May only approximate the data you want
May have hidden biases
Sometimes access is a problem

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

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

Issues of Ethics in Conducting Research (5)
Settings and procedures
Informed consent: full disclosure, individual comprehension, competence
Privacy: public/private settings
Confidentiality
Anonymity

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.

