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PO 217 Intro to Research > Section 3 > Flashcards

Section 3 Flashcards

0
Q

Reliability

A
  • are we getting a consistent measurement (regardless of whether it is wrong/right)?
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1
Q

Validity

A
  • are we measuring what we think we are measuring?
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2
Q

Measurement Errors

A
  • differences in values that are a result of flaws in the measurement process
  • Systematic: getting the same error each time
  • Random: getting a different value each time
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3
Q

Pragmatic Validity

A
  • Use of multiple sets of indicators and values to test the validity of a given concept
  • Concurrent: involves come pairing the results of one indicator to another
  • Predictive: involves using the indicator to predict other behavior
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4
Q

Construct Validity

A
  • involves relating an indicator to an overall theoretical framework
  • External Validation: based on the knowledge of the concept we seek to measure, we can postulate relationships between that concept and other concepts
  • Convergent Validity: different methods of measuring the same concept should produce similar results
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5
Q

Discriminant Validity

A
  • inferring validity according to the indicators ability to differentiate across concepts
  • the degree of which it is unrelated to indicators for other concepts
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6
Q

Content Validity & Face Validity

A

Content: concerned with the content of what is being measured
Face: can knowledgable people be persuaded of this validity?

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7
Q

Assessing Reliability

A
  • An empirical matter; three different methods of assessment
  • Test-Retest Method: Can we get the same results?
  • Alternative/Parallel Forms Method: Using two ways of measure for the same case
  • Subsample/Split-Half Method: Dividing sample into sub-samples; similar results should be achieved by all sub-samples
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8
Q

Probability Sampling

A
  • every member of population has a known and non-zero probability of being included in the sample
  • this avoids unconscious bias
  • allows use of inferential statistics
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9
Q

Non-Probability Sampling

A
  • no way to specify probability of inclusion; some pop. may not have any chance of inclusion
  • used when convenience and economy out weight risk of bias
  • also when no pop. list is avaliable
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10
Q

Simple Random Sampling

A
  • every member of pop. has same probability of inclusion
  • Could result in extreme samples
  • also can be time-consuming
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11
Q

Systematic Random Sampling

A
  • dividing the pop. size by size of desired sample to achieve sampling yield (denoted at ‘k’)
  • then researcher will take every ‘kth’ person on the pop.list
  • if the first individual is selected randomly, there is no restriction on inclusion
  • can potentially produce extreme samples depending on how pop. list is ordered
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12
Q

Proportionate Stratified Random Samples

A
  • ensure key groups in pop. are represented in their correct proportions
  • requires information about each person in the pop. before conducting study
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13
Q

Disproportionate Stratified Random Sample

A
  • same as proportional, except that this deliberately over-samples certain pop. groups
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14
Q

Multi-Stage Random Cluster Samples

A
  • used when no pop. list is available
  • Convenience: whatever people happen to be available
  • Volunteer: people self-select to participate
  • Purposive: researcher uses their judgement to pick people of a pop.
  • Snowball: start will small group of participants and ask them to reach out and ask more participants
  • Quota: select sample that represents microcosm of pop.
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15
Q

Descriptive Statistics

A
  • are used to describe characteristics of a population or a sample
16
Q

Inferential Statistics

A
  • are used to generalize from a sample to the population from which the sample was drawn
17
Q

Univariate Statistics

A
  • describe (descriptive) or make inferences about (inferential) the values of a SINGLE variable
18
Q

Bivariate Statistics

A
  • describe (descriptive) or make inferences about (inferential) the relationship between the values of TWO variables
19
Q

Multivariate Statistics

A
  • describe (descriptive) or make inferences about (inferential) the relationship among the values of THREE OR MORE variables
20
Q

Three Characteristics of Each Variable in Data Analysis

A
  • The Distribution: How many cases take each value?
  • The Central Tendency: Which is the most typical value?
  • The Dispersion: How much do the values vary?
21
Q

Frequency Distribution

A
  • number of observations in each category of variables

- Can be Raw or Relative

22
Q

Central Tendancy

A
  • indicates the most typical value that best represents entire distribution (mean, median, mode)
  • Nominal: Mode, uses variation ratio to represent proportion of cases that do not fall into the modal category
  • Ordinal: Median
  • Interval Ratio: Mean
23
Q

Dispersion

A
  • tells us how much the variables vary
  • Ordinal: Range
  • Interval/Ratio: Standard Deviation
24
Q

Demonstrating Covariation

A
  • Form: Which values of the DV are associate with which values if the IV
  • Statistical Significance: can the relationship be generalized from the sample to the greater pop.
  • Degree: how strong is the relationship between the IV and the DV?
25
Q

Statistical Significance

A
  • how likely it is that the relationship occurred by chance
  • known as level of statistical significance
  • lower the probability, the higher the significance
26
Q

Type 1 & Type 2 Errors

A
  • Type 1: inferring there IS A relationship when there IS NONE
  • Type 2: inferring there IS NO relationship when there IS ONE
  • Type 1 is more dangerous
27
Q

Chi-Square Distribution

A
  • give likelihood of each possible degree of relationship occurring in a sample if there were no relationship in the population from which the sample was drawn
  • Test to ensure lower chance of type 1 error
28
Q

Measure of Association (correlation coefficient)

A
  • single number that summarizes the degree of association between two variables
  • -1 (negative/inverse association), 0 (no association), +1 (positive/direct association)
29
Q

Proportional Reduction in Error

A
  • PRE provides an intuitive approach to measuring association
  • allows us to see how much better our predictive ability will be when it comes to the DV
30
Q

Calculating Lambda(a)

A
  • PRE-based measurement of association
  • use when both variables are nominal
  • measures how much our predictive ability is improved by knowing the values of cases on the IV.
  • Ranges from .00 (no improvement) to 1.00 (perfect predictability)
  • Will always be zero is modal values is the same for all categories of IV
    • In this case use Cramer’s V (based on Chi-Square logic, not PRE)
31
Q

Measures of Association (Ordinal-Level)

A
  • measure ranges from -1.00 to +1.00
  • Negative coefficient: IV and DV will have inverse relationship
  • Positive coefficient: IV and DV will have positive relationship
32
Q

Gamma

A
  • PRE measurement at Ordinal level
  • It positive pairs/relationships dominate, Gamma is positive, and vice versa
  • The size of the coefficient indicates the strength of relationship, while the sign indicates the direction
  • Gamma can artificially inflate results because it ignores ‘ties
  • Only use when both variables are ordinal, or both are dichotomous
33
Q

Tau

A
  • Ordinal measure of association
  • ranges from -1.00 to +1.00
  • Tau(b) is used when both variables have same number of values (same number of columns and rows in table)
  • Tau(c) is used when one variable has more than the others
  • Only used when both variable are ordinal, or one or both are dichotomous

PO 217 Intro to Research (3 decks)

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