Social Research 2

Front 1

Conceptualization

Back 1

Defining concepts including levels of abstraction (concept of age would have a low abstraction, the concept of emotion would have high abstraction.

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Inductive (Qualitative)

Back 2

How was the quality of parent-child relationships expressed

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Deductive (Quantitative)

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decide before hand what the definition is.

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process of operationalization

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how you are going measure your concepts. (eg using surveys, collect unobtrusively, make observation, ask a question )

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operational indicator

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what are you going to look for when making observations that represent the concept you have defined.

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What is a variable

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discrete or continuous (fixed or infinite)

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attributes of a variable

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ex. male or female, married or single

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mutually exclusive

Back 8

fits into one and only one attribute of a variable

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mutually exhaustive

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fit into more than one attribute of a variable

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ordinal

Back 10

greater than or less than

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nominal

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variation in kind, type or quality (no ordering)

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continuous

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fixed measurement of units (eg age, years of education)

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Index

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use index to measure concepts with high levels of abstraction

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measurement error

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the degree to which the true score on the variable deviates from the measured score.

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sources of measurement error

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method factors: researcher error
ex. measurement operation, bad operational indicator, etc

*can be controlled

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Generic factors

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common problems
ex. social desirability, sensitive topic, recall

*can be controlled

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Idiosyncratic factors

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a few cases random problem
ex. participant unable to focus, etc.

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measurement validity

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accuracy

how well the empirical indicators match what we intend to measure.

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Face validity

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Does the measure make sense?
Logically represents the intended concept more than other concepts

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Content Validity

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Is the measure comprehensive?
full range of the concept's meaning

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Concurrent criterion validity

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empirical association with same concept that was measured in a different way

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criterion validity

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is the measure comparable to a criterion?
-criterion= external source
1) a more direct measure of the same concept (eg. observation rather than survey)
2) a previously validated measure of the same concept
3) a different concept that should empirically correlate with the concept

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Predictive criterion validity

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-empirical association with different concepts
-does the measure behave as hypothesized?
-should have a strong (positive or negative) association

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Measurement Reliablity

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Does the measure consistently yield the same result (given the concept didn't change)?

-assessing reliability is always based on empirical analysis of data (such as an empirical associations)

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test-retest reliability

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comparing two time points of the measured concept within a person.

-or within an observer = intra-observer reliability

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inter-observer reliability

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comparing multiple observers measuring the same concept

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inter-item reliablity

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internal consistency of an index
-used for multiple indicator indices only to assess unidimensionality
-empirical coefficient=Cronbach's alpha (.75 or ^)

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sampling

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the process of selecting cases from the population to be elements in the sample

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Population

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Is the whole group you want to know about

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Sample

Back 30

the group that has been selected from the population

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Cases

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selected by chance
ex. every 5th person through the door

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Elements

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number of people in the sample

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Sampling frame

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a list of all cases in your population

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probability procedures

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known likelihood of selection into the sample (p=n/N, p>0)
--no systematic bias

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non-probability procedures

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-unknown likelihood of selection into the sample.
-cases are not selected by chance
--systematic bias is present

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sufficiently large for probability cases

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generally 1000 elements is sufficiently large for probability cases.

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Where is the common site of Cylindroma?
whats the histology?

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Skin of scalp and forehead
Nest of epithelial cells that are surrounded by thickened basement membrane

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sample generalizability

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the degree to which research findings, such as empirical associations, from the sample hold true for the population

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what is representative

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Assessing sample representativeness is often based on demographic characteristics (e.g. race, gender, age, religion, social class, etc)

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representative sample

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will have demographic characteristics that are very similar to the population.

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sampling error

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the degree to which the sample characteristics deviate from population characteristics

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sources of sampling error

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-random
-small sample "n" or small sub-group "n"
-incomplete sampling frame
--a list of cases in the population used for selecting elements in the sample
-refusal to participate/failure to locate element
-sample attrition (longitudinal studies
-non-response to operational indicator (70% response rate is good)

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non-respondents

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individuals selected for the sample but for whom the researcher has no information (i.e. data)

sampling error occurs if non-respondents systematically differ from respondents (i.e. people who provide information) on demographics and other concepts of research interest.

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Simple Random Sampling

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all cases in the population have an equal probability of selection (n/N)
-number cases in the sampling frame and then randomly select cases
--lottery, random tables of numbers, etc
--random digit dialing

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Stratified random Sampling

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1. put all cases in one and only one stratum
2. randomly select elements within each stratum

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strata

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are based on relevant person characteristics

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proportionate

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-equal probability of selection for all cases
-% of relevant sample characteristic = % in population

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disproportionate

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(balancing out population groups)
-probability of selection varies by strata
--higher probability among cases within "small" stratum
--lower probability among cases within "large" stratum
--% of relevant sample characteristic is not equal to % in population

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Cluster

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naturally occuring aggregates
-write list of all clusters
1. draw random sample of clusters
2. randomly select elements within each cluster
-multi-stage cluster sampling

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cause

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A cause is an explanation for some characteristic, attitude or behavior of individuals, groups (such as families or organizations) or for events.

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nomothetic causation

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-causal explanation (based on empirical evidence)
--Variation in one variable (independent), on average, leads to variation in another variable (dependent), all other variables being equal
--probabilistic tendencies: average experiences

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causal validity

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the degree to which we can trust our conclusion that the independent variable (x) causes the dependent variable (y)
--three criteria

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1. emperical association (ie found in data)

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x and y must vary together
x is associated with y

ex.
positive associate
x:education ---> y: $$$
higher levels of education are associated w/ higher levels of annual income

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Simple Random Sampling

Back 54

all cases in the population have an equal probability of selection (n/N)
-number cases in the sampling frame and then randomly select cases
--lottery, random tables of numbers, etc
--random digit dialing

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Stratified random Sampling

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1. put all cases in one and only one stratum
2. randomly select elements within each stratum

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strata

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are based on relevant person characteristics

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proportionate

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-equal probability of selection for all cases
-% of relevant sample characteristic = % in population

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disproportionate

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(balancing out population groups)
-probability of selection varies by strata
--higher probability among cases within "small" stratum
--lower probability among cases within "large" stratum
--% of relevant sample characteristic is not equal to % in population

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Cluster

Back 59

naturally occuring aggregates
-write list of all clusters
1. draw random sample of clusters
2. randomly select elements within each cluster
-multi-stage cluster sampling

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cause

Back 60

A cause is an explanation for some characteristic, attitude or behavior of individuals, groups (such as families or organizations) or for events.

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nomothetic causation

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-causal explanation (based on empirical evidence)
--Variation in one variable (independent), on average, leads to variation in another variable (dependent), all other variables being equal
--probabilistic tendencies: average experiences

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causal validity

Back 62

the degree to which we can trust our conclusion that the independent variable (x) causes the dependent variable (y)
--three criteria

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1. emperical association (ie found in data)

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x and y must vary together
x is associated with y

ex.
positive associate
x:education ---> y: $$$
higher levels of education are associated w/ higher levels of annual income

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Time order

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variation in x must occur before variation in y

ex
x:edu --> y:$$$
grad in 07 in 09

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nonspuriousness

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an association is spurious when it is due to variation in a third variable

ex
x ---> y
^z^

there is no causal link between x and y
z is an extraneous variable - also called control variables or confounding covariates

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causal validity is increased when...

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-x is empirically associated with y
-x occurs before y (time order is established)
-the association between x and y is shown to be nonspurious

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meeting causal criteria in research

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-ensuring association
--bivariate statistical analysis of x and y

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ensuring proper time order

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cross-sectional studies(tricky)
--x is an attribute assigned at birth, a prior circumstance or a discrete event
--panel studies (longitudinal) (better)
---measure x at time 1 and y at time 2

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ensuring nonspuriousness

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first researcher must design their studies to measure a large # of and the appropriate kind of extraneous variables
-then multivariate statistical analyses with x,y and z

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statistical control

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an extraneous variable is held constant and the association between x and y remains the same (effect size and statistical significance)

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Writing Good Survey Questions: Avoid Confusion

Back 71

-ask very specific and clear questions
-avoid very long questions and sentence fragments
-avoid double-barreled questions

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Writing Good Survey Questions: Avoid bias

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-avoid "emotional" language
--eg feminist, terrorist, prejudice
-avoid status associations
--eg "Obama-care", prestigious or otherwise
-avoid one-sided or leading questions
--To what extent do you oppose a tax increase?
--How mad does your mother-in-law make you?
-Avoid overlapping or unbalanced response choices

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Writing Good Survey Questions: Additional consideration

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-Sensitive or threatening questions
--illegal behavior, sex, money, etc
-Expecting too much of the respondent
--memory
---use specific and reasonable time periods
--Knowledge
---floaters: include a "don't know" response choice
--relevance
---use skip pattern/contingency questions
---confusing beliefs with reality
Respondents cannot give valid and reliable responses to confusing, biased or otherwise problematic

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Method of Data Collection

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-nuts & bolts
-strengths & weaknesses
-ethical concerns

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Surveys

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-question: brief response
-secondary data

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Participant Observation

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-observe: social setting

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Content Analysis

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-unobtrusive: books, newspapers, etc

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In-depth Interviews

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question: elicit long detailed responses

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Experiments

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-expose to stimulus then observe or question. (all are qualitative data)

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Focus

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-question group: elicit long group discussion
-observe: group discussion

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Surveys -Research Design

Back 81

Ex. 2000 presidential election survey design during the Bush/Gore election

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Surveys- Research Questions

Back 82

-prevalence and predictions: describing large populations
-self-reported beliefs, behaviors, characteristics, etc

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Surveys- Conceptualization

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-define concepts prior to writing survey questions

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Surveys- Operationalization

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-operational indicators: asking survey questions determine, validity and reliability of the measure

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Surveys- Sampling

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Probability procedure
-generalizability is very strong

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Surveys- Causal Validity enhanced by:

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-longitudinal panel designs compared to cross sectional designs
--to establish time order
-(number) and quality of non-focal in survey
--to establish non-spuriousness

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Survey Instrument: Interview Schedule

Back 87

-administered by trained interviewer
--in person or by telephone

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Survey Instrument: Questionaire

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-self-administered
--computer/web or paper & pencil

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Response Choices to Survey Questions

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Open ended: no response choices provided to the interviewee
-used when unknown range or many possible responses
--survey questions must be very specific

Close ended
-mutually exclusive (not overlapping and exhaustive
--exception: checks all that apply
*** Respondents cannot find on appropriate answer they will either skip the question or provide an innaccurate response***

Front 90

Designing the Survey Instrument

Back 90

-assurance of anonymity or confidentiality
-directions for
--answering questions(questionaire)
--asking questions (interview schedule)
-question order
--starting with interesting questions
-group questions by theme
--general questions first then specific questions
-sensitive topics asked towards the end of the survey

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Questionaire formatting

Back 91

-attractiveness
--plenty of white space
--vertical (not horizontal) response choices
-clarity
--formatting of directions differs from question stem
--skip patterns: use of graphics and provide instructions

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Indices

Back 92

-provide instructions
-response choices for each question must be the same
--matrix format response choices