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48 Cards in this Set
- Front
- Back
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Direct observables
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o Number of students, etc.
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Indicatory observables
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• Characteristics of what’s being observed
o Clothing of number of students observed |
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Constructs
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• Theoretical creations based on observations
• BUT cannot be observed directly or indicatory o Prejudice, anger, guilt |
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Conceptualization
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• Specifying what we mean when we use particular terms
o Prejudice, anger, guilt o Defining a feeling |
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Operationalization
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DETAILS
• Procedures/techniques used to find out something about your topic o To what extent is the research willing to combine attributes in fairly gross categories? • Income → how much details? • Attitudes → strongly agree and strongly disagree o 11 point scale → 0-10 |
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• Nominal
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just name, no rank
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• Ordinal
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rank order
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• Interval
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fill in the blank
o IQ scores o Temp o Zero doesn’t mean anything |
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• Ratio
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fill in the blank
o Income o Age o Zero means something |
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• Test-Retest Reliability
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o Give it now, give it later
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• Split-Half Reliability
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o Two parts
o 20 questions → one half has 10, and so does the other o Split it up between groups/people |
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• Alternate-Forms Reliability
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o Two different forms of the same test to the same person
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• Internal-Consistency Reliability
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o Overall degree of relatedness of all test items or raters
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Validity
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• How well does it measure what its supposed to measure
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• Face Validity
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o The quality of an indicator that makes it seem a reasonable measure of a variable
o Looks like it’d be ok |
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• Criterion Related Validity/Predictive Validity
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o The degree to which a measure relates to some external criterion
o Driving test → real driving o Design a test → relate to real life |
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• Construct Validity
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o The degree to which a measure relates to other variables as expected within a system of theoretical relationships
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• Convergent Validity
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o 2 measures that measure the same construct should be related
o happy people → high self-esteem |
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• Divergent Validity
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o 2 measures that measure different constructs should not be correlated
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Index and Scale Similarities
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o Both are ordinal measures of variables
o Both are composite → more than one data items (several indicators) |
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Index and Scale Differences
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o Indexes accumulate the scores assigned to individual attributes (support, stress)
• All items have same weight o Scales assign scores to patterns of responses • Items differ in the level of intensity • Strong topics |
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o Uni-dimensional
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(one aspect)
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o Variance
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(do you go to church?)
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Empirical Relationships
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• When respondents answer to one question help us predict how they will answer other questions
• “say yes to one, say yes to all” • If 2 items are empirically related, we can argue that each reflects the same variable and both can be included in the same index |
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Handling Missing Data
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• Few cases → excluded them from analysis
• Replace the missing data with the mean for a continuous variable o Income, Age. Etc. • Replace the missing values with middle value |
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• Pairwise Deletion
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o Use only completed data on only those variables selected for a particular analysis
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• Thurstone Scale
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o Large number of statements
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• Bogardus Social Distance Scale
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o How would you feel about having a member of X group…
• Close to my kids • In my club • As my neighbor |
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• Semantic Differential Scale
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o Rank answers between 2 extremes
o Good _ _ _ _ 5 Bad o Strong _ _ 3 _ _ Weak |
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• Likert Scaling
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o Use standardize response categories
o Really Strong, Strong, Neutral, Weak, Really Weak |
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• One of most visible uses of surveys
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political poll
estimating the votes of 100+ million by asking 2000 people |
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• Population
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o The theoretically specified aggregation of study element
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• Sampling
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o Selecting subgroups from a population
o People, objects, events |
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• Sampling Frame
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o List of elements from which a probability sample is selected
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• Sampling Unit
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o Element or set of elements considered for selection in some stage of sampling
o “Parents of children with autism”, “students attitude of teacher” |
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• Random Selection
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o Each element has an equal chance of selection independent of any other event in the selection process
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• Parameter
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o Description of the variable in population
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• Statistic
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o Description of the variable in sample
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• Rep Sample
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o Usually more important than the size of sample
o Needs to represent population in the best way possible |
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• Probability Sampling
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o Likelihood of any member of the pop being selected is known
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• Non Probability Sampling
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o Likelihood of any member of the pop being selected is not known
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o Convenience Sampling
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• Easy access to sample
• Quicker cheaper method |
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Problems with convenience sampling
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Nonrandom
Potentially biased Results cannot be generalized |
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o Purposive
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• Selecting a sample on people who have knowledge of a population, its elements, and the purpose of the study
• Participants has characteristics that the researcher wants |
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o Snowball Sampling
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• Collect data on members you can locate → then ask to locate other members of that population
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o Quota Sampling
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• Start with table describing characteristics of target population
Gender, Age, Education • Data is collected from people with the characteristics of a given cell |
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• Probability Sampling
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• All elements of a population have an equal chance of inclusion
“Fair” |
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o Simple Random Sampling
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• ID all elements of population
• List elements • Randomly select a sample from the list • Only works with a simple group of people |
