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soc research methods > exam 2 > Flashcards

exam 2 Flashcards

1
Q

concept

A

an idea that can be named, defined, and eventually measured

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2
Q

conceptualization

A

process of precisely defining ideas and turning them into variables

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3
Q

operationalization

A

turning abstract concepts into measurable observations

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4
Q

categorical variables

A

have a finite set of possible values
no known distances between values
includes nominal and ordinal variables

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5
Q

nominal

A

catalog states or statuses that are parallel and cannot be ranked or ordered

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6
Q

ordinal

A

have categories that can be ordered in some way, but the distance between the values is not known

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7
Q

example of ordinal variables

A

clothing sizes ranging from XS to xl

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8
Q

continuous variables

A

have an infinite set of possible variables
values have fixed distances between them
includes interval and ratio variables

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9
Q

interval

A

have a continuum of values with meaningful distances between them, but no true zero. the values can be compared directly, but they cannot be used in proportions or mathematical operations.

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10
Q

example of interval variables

A

SAT score or temperature

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11
Q

ratio

A

are interval variables that do not have a true zero, and the distances between values can be measured, and values can be expressed as proportions

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12
Q

dimensions

A

manifestations, angles, or units of the concept

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13
Q

indicators

A

the values assigned to a variable to provide the blueprint for measurement

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14
Q

unit of analysis

A

the level of social life about which we want to generalize

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15
Q

individual unit of analysis

A

used to refine our understanding of the ties that bind individuals together into a society

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16
Q

group unit of analysis

A

how social structure and forces affect whole categories of people on the basis of race, class, or gender

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17
Q

organization or insitution unit of analysis

A

can be used to understand how corporations impact various aspects of social and economic life

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18
Q

society unit of analysis

A

used for understanding how larger social structures shape us

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19
Q

mismatches between units of analysis

A

when one unit of analysis does not translate into another, the results of study can be invalidated

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20
Q

ecological fallacy

A

a mistake that researchers make by drawing conclusions about the micro level based on some macro level analysis

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21
Q

reductionism

A

a mistake that researchers make by drawing conclusions about macro level unit based on analyses of micro level data

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22
Q

four basic forms of measurements

A

reports
observation
artifact counts/assessments
manipulation

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23
Q

reports (open-ended and close-ended questions)

A

open-ended allow subjects to respond in their own words
close-ended have preset response categories

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24
Q

observation

A

the process of seeing, recording, and assessing social phenomena

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25
Q

artifact counts/assessments

A

manipulation

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26
Q

calculation reliability: cronbach’s alpha and internal reliability

A

measures a specific kind of reliability for a composite variable

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27
Q

how to assess a cronbach alpha score

A

score of 0 would mean no reliability, or that items are not tapping a common underlying phenomenon
score of 1 would mean perfect reliability, though this is not practical

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28
Q

intercoder reliability

A

reveals how much different coders or observers agree with one another when looking at the same data

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29
Q

internal validity

A

the degree to which the study establishes a causal effect of the independent variable on the dependent variable. the measures truly and accurately capture concepts.

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30
Q

generalizability

A

extent to which results or conclusions based on one population can be applied to others

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31
Q

representativeness

A

the degree of similarity of a study population compared to an external population

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32
Q

face validity

A

a dimension concerning whether a measure looks valid

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33
Q

concurrent validity

A

how closely the measure is associated with a preexisting measure

34
Q

predictive validity

A

how closely the measure is correlated with something it should be correlated with

35
Q

content validity

A

concerns how well a measure encompasses the many different meanings of a single concept

36
Q

construct validity

A

concerns how well multiple indicators are connected to some underlying factor

37
Q

robustness

A

the capacity to produce unbiased results when small changes happen

38
Q

split-half method

A

assesses robustness by testing the similarity of results after administering one subset of an item to a sample and then another subset

39
Q

test-retest method

A

assesses robustness by administering a measure to the same sample at two different times

40
Q

pilot testing

A

a method of administering some measurement protocol to a small preliminary sample of subjects as a means of assessing how well the measure works

41
Q

sampling

A

the process of deciding what or whom to observe when you cannot observe and analyze everyone or everything

42
Q

literary digest poll: how did it go wrong?

A

their sample did not represent their population

43
Q

probability sample

A

sample chosen via selection

44
Q

advantages of probability sampling

A

it doesn’t represent one group more than another
bias doesn’t reflect into research

45
Q

sampling error

A

the difference between the estimates from a sample an the true parameter that arise due to random chance

46
Q

systematic error

A

a flaw built into the design of the study that causes a sample estimate to diverge from the population parameter

47
Q

what’s the difference between systematic and sampling error

A

sample error is the difference between the sample and true parameter while systematic error is flaw that causes sample estimate to diverge from population parameter

48
Q

margin of error

A

the amount of uncertainty in an estimate

49
Q

sampling distribution

A

a probability distribution of a statistic that comes from choosing random sample of a given population

50
Q

target population

A

a group about which social scientists attempt to make generalizations about

51
Q

census

A

includes data on every member of a population

52
Q

population parameter

A

a number that describes something about an entire population or group

53
Q

confidence levels

A

the probability that an estimate includes the population parameter

54
Q

confidence intervals

A

the range implied by the margin of error. my average would fall in between [#,#]

55
Q

what is the commonly accepted confidence interval?

A

95%

56
Q

what happens to the margin of error when the sample size increased?

A

the bigger the sample, less chance for error

57
Q

simple random sampling

A

a type of probability sample in which each individual has the same probability of being selected

58
Q

what is the sampling frame

A

a list of population members from which a probability sample is drawn

59
Q

systematic sample

A

a probability sampling strategy in which sample members are selected by using a fixed interval, such as taking every fifth person on a list of everyone in the population

60
Q

cluster smapling

A

researchers divide up the target population into groups, or “clusters”, first selecting clusters randomly and then selecting individuals within those clusters

61
Q

stratified sampling

A

the population is divided into groups, or strata, and sample members are selected in strategic proportions from each group

62
Q

oversampling

A

a group that is deliberately sampled at a rate higher than its frequency in the population

63
Q

weighting

A

used to account for the fact that the sample is no longer representative

64
Q

non representative samples

A

sample is not representative of the population

65
Q

David Snowden’s nun study

A

allowed him to learn more about Alzheimer’s disease than he could have with a representative sample

66
Q

issues of generalizability with non representative samples

A

researchers are not able to conclude that a hypothesis holds true in the same way throughout all population subgroups

67
Q

case-oriented research

A

scientists rather large amounts of data about a single case or small number of cases

68
Q

how does case oriented research differ from variable oriented research

A

variable oriented studies a large number of cases but only a limited amount of data. case oriented gathers large amounts of data from one single case or a small number of cases.

69
Q

purposive sampling

A

cases are selected on the basis of features that distinguish them from other cases

70
Q

why is purposive sampling better than probability sampling for case oriented research

A

potential candidates are thoughtfully chosen

71
Q

sequential sampling and the role it plays

A

enables researchers to make decisions about what additional data to collect based on their findings from data they’ve already collected

72
Q

sampling for range

A

maximizing respondents’ range of experiences with the phenomena under study

73
Q

saturation

A

when new materials fail to yield new insights and simply reinforce what the researcher already knows

74
Q

typical cases: typicality

A

a case is typical when its features are similar in as many respects as possible to the average of the population it represents

75
Q

extreme cases: extremity

A

focusing on “extreme” cases can provide researchers with particularly vivid examples of the phenomenon they wish to study

76
Q

why would researchers choose deviant cases?

A

to choose cases that are unusual, unexpected, or hard to explain given what is currently known about a topic

77
Q

contrasting outcomes

A

researchers may sometimes choose a pair of cases that represent a “puzzle” or in some way have different outcomes in response to the same stimulus

78
Q

key differences, past experiences, and intuition

A

researchers may be interested in the range of consequences that might follow from a key difference between two cases. they may use their past experiences or their intuition about which settings will be useful for studying their research.

79
Q

sampling and big data

A

data sets with billions of pieces of information, typically created through individuals’ interactions with technology

80
Q

administrative records

A

data collected by government agencies or corporations as part of their own record keeping

81
Q

example of nominal variables

A

gender or religion

soc research methods (5 decks)

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