Exam 3 Flashcards Preview

HDFS 369 > Exam 3 > Flashcards

Flashcards in Exam 3 Deck (96):
1

Conceptualization

The process of specifying what is meant by a term. Its different aspects and different dimensions, how it is different from existing concepts.

2

Operationalization

The process of connecting concepts to observations.

3

Measurement

The level at which the data is actually being collected. Collection of operation indicators. (answers to survery's, drawing blood samples)

4

Exhaustive

Cover all possible responses- Can everyone in the study be classified or scored?

5

Mutually Exclusive

When every case can be classified as having only one attribute (value)-Can the answer only have one value?

6

Nominal level of measurement

Variables whose values have no mathematical interpretation; they vary in kind or quality but not in amount-Color, name, country, hometown.

7

Ordinal level of measurement

The numbers indicating a variable's values specify only the order of the cases, indicates the ranks.

8

Interval level of measurement

The numbers represent fixed measurement units but have no absolute zero point. Numbers can be added and subtracted, but ratios are not meaningful.

9

Ratio level of measurement

The numbers represent fixed measuring units and an absolute zero point (meaning no amount of whatever the variable indicates).-Income, household size, years married.

10

Validity

Accuracy

11

Reliability

Consistency

12

Face Validity

Singular view (looks at one thing), expert opinion (no data). Doesn't provide convincing evidence of measurement validity.

13

Content Validity

Expansive view, expert opinion. Often uses index set of questions.

14

Criterion Validity

Singular view, empirical testing(uses data). Compares stats to an established similar variable.

15

Concurrent Validity

Validity exists when a measure yields scores that are closely related to scores on a criterion measured at the same time.

16

Predictive Validity

Validity is the ability of a measure to predict scores on a criterion measured in the future.

17

Construct

Expansive view, empirical testing. Established by showing that measures are related to a variety of other measures.

18

Convergent Validity

Validity is acheived when one measure of a concept is associated with different types of measures of the similar concepts.

19

Discriminant Validity

Validity is a complementary approach to construct validation. Compared on scores of measures of different but related concepts.

20

Test-Retest Reliability

The same measure at two different points in time. Examines the stability of the measure over time.

21

Inter-Item Reliability

Examines the internal consistency of the measure.

22

Cronbach's Alpha

The statistical test for inter-item reliability. Above .70 indicates good.

23

Alternate-Forms Reliability

Examines the equivalence of measures in different forms. Ex. English-Spanish survey.

24

Inter-Observer Reliability

Do different observers report similarly? Examines whether there is agreement/consistency.

25

Probability

Allows us to know in advance how likely it is that any element of a population will be selected for the sample.

26

Simple Random Sampling

Identifies each element strictly on the basis of chance.

27

Systemic Random Sampling

First element is selected randomly and then every nth element is selected. Need to make sure there are no problems with peiodicty.

28

Stratified Random Sampling

All elements in the sampling are distinguished according to their value on some characteristics.

29

Multi-State Cluster Random Sampling

Useful when collecting comprehensive list, or when collecting data from a simple random sample.

30

Non-Probability

Sampling methods that do not let us know in advance the likelihood of selecting each element.

31

Convenience/Availability Sampling

Elements are selected for availabilty sampling because they are available or easy to find.

32

Quota Sampling

Selecting a pre-set number of elements based on characteristics in a population to ensure that the sample represents those characteristics in proportion to their prevalence in the population.

33

Purposive Sampling

Each sample element is selected because of the unique and unusual position of the sample elements.

34

Snowball Sampling

There is no sampling frame, but the members of which are somewhat interconnected.

35

Census

Gathers data from every element of the entire target population. It is a way to get participants, but technically is not a "sample" since it includes everyone.

36

Population

Entire set of individuals or other entities to which study findings are to be generalized

37

Sampling frame

A list of all elements or other units containing the elements in a population

38

Elements

The individual members of the population whose characteristics are to be measured

39

Systematic Bias

Overrepresentation or underrepresentation of some population characteritics in a sample resulting from the method used to select the sample.

40

Generalizability

Does the sample come close to representing the target population?

41

Simple random sampling

A method of sampling in which every sample element is selected only on the basis of chance, through a random process.

42

Random number table

A table containing list of numbers that are ordered solely on the basis of chance, it is used for drawing a random sample

43

Random digit dialing

The random dialing by a machine of numbers within designated phone prefixes, which creates a random sample for phone surveys.

44

Sampling Interval

The number of cases from one sampled case to another in a systematic random sample.

45

Periodicity

A sequence of elements that varies in some regular, periodic pattern

46

Stratified random sampling

A method of sampling in which sample elements are selected separately from population strata that are identified in advance by the researcher. Division of a population into smaller groups.

47

Proportionate

Sampling method in which elements are selected from strata in exact proportion to their representation in the population.

48

Disproportionate

Sampling in which elements are selected from strata in different proportions from those that apepar in the population.

49

Cluster

A naturally occurring mixed aggregate of elements of the population.

50

Availability Sampling

Sampling in which elements are selected on the basis of convenience.

51

Quota Sampling

A non probability sampling method in which elements are selected to ensure that the sample represents certain characteristics in proportion to their prevalene in population.

52

Purposive Sampling

Non probability sampling method in which elements are selected for a purpose, usually because of their unique position.

53

Snowball sampling

Where existing study subjects recruit future subjects from among their acquaintances.

54

Sample size increases the power of the study..

by shrinking the confidence interval, there is less chance of error. Small samples make an increase in the confidence interval.

55

Cross-Sectional research design

A study in which data are collected at only one point in time.

56

Longitudinal Research Design

A study in which data are collected that can be ordered in time; also defined as researchin which data are collected at tow or more points in time.
Goal: identify the trend of change in the population, not the individuals.

57

Panel-Fixed-Sample Panel Design

Same participants were followed up after the initial data collection.
Goal: Examine the change of some individuals or groups.

58

Cohort Designs

Data from the same cohort (those who share a common event or starting point) were collected at multiple time points.

59

Event based repeated cross sectional design

Different samples from same cohort.

60

Event based Panel design

Same individuals from same cohort are followed up over time.

61

Unit of analysis

The level of social life on which a research question is focused, such as individuals, groups, towns, or nations.

62

Unit of observation

Researchers base conclusions on information that is collected and analyzed, so using defined units of observation in a survey or other study helps to clarify the reasonable conclusions that can be drawn from the information collected.

63

Ecological Fallacy

An error in reasoning in which incorrect conclusions about individual-level processes are drawn from group-level data.

64

Reductionist Fallacy

An error in reasoning that occurs when incorrect conclusions about group-level processes are based on an individual.

65

Idiographic Causal Explanations

Identifies the concrete individual sequence of events, thoughts, or actions that resulted in a particular outcome for a particular individual.

66

Nomothetic Casual Explanations

Identifies common influences on a number of cases or events. Typically, the independent variable is the presumed cause and the dependent variable is the potential effect.

67

5 Criteria for nomothetical casual explanations.

Association
Time Order
Non-Spuriousness
Mechanism
Context

68

Association

Relationship between two variables.

69

Association in experimental research

Determine association by manipulating the independent variable.

70

Association in non-experimental research

Determine association based on whether values of cases that differ on the independent variables tend to differ in terms on the dependent variable.

71

Time order

The variation in the dependent variable occurred after the variation in the independent variable

72

Cross sectional design

Data is collected at a single time point.

73

Longitudinal Design

Data is collected at more than one time point

74

Time order in experimental research

Controlled by researchers

75

Time order in non-experimental research

Our ability to establish time order depends on the timing of measurement

76

Non-Spuriousness

A criterion for establishing a causal relation between two variables; when a relationship between two variables is not caused by variation in a third variable.

77

Non-spuriousness in experimental research

Randomization--The use of random techniques to designate subjects into comparison groups

78

Non-spuriousness in non-experimental research

Statistical control-- A method in which the effects of hugely influential variables are taken into account so that there can be more confidence on the effects of the predictors

79

Mechanism

A discernible process that creates a causal connection between variation in an independent variable and the variation in the dependent variable it is hypothesized to cause.

80

Context

A set of interrelated circumstances that alters a relationships between other variables or social processes.

81

Moderators

Variables that define context

82

5 Causal Criterial

Correlation
Longitudinal Design
Randomization
Statistical Control
Substantive Theory
Moderator

83

Three types of longitudinal designs

Trend
Panel
Cohort

84

Randomization

The random assignment of cases as by the toss of a coin.

85

Statistical Control

A method in which one variable is held constant so that the relationship between two other variables can be assessed without the incluence of variation in the control variable

86

A researcher is going to use a phonebook in Ames to investigate how many families with landline phones also carry cellphones.

probability- systemic

87

A member of ISU diversity committee is attending the university lecture series searching for students representing diverse backgrounds to sit on panels and in focus groups

non-probability- quota

88

A researcher wants to do a representative study about immigration experiences comparing those who came to the US to seek jobs and those who came to escape the warfare in their home country.

probability- stratified

89

A researcher is collecting love stories, from veterans of WWII, and begins by interviewing her grandfather and a prominent neighbor who happens to live down the street

non-probability- snowball

90

a student researcher is going to investigate the quality of janitorial services at isu by taking dust samples from the floor space in 25 classrooms- the university however, doesn't want to furnish the student with a list of classrooms for this purpose. The results will be compared to a nationwide average

probability-- multi-stage cluster

91

a researcher wants to evaluate satisfaction with the concessions prices for season ticket holders of the Chicago cubs baseball team. The report will be used by executives to adjust prices at the stadium.

probability- simple

92

A student wants to gather a few quotes in support of or against his review of the newly-released Hollywood flow : voldemort vs. vader.

non-probably- convenience / availability

93

A researcher who is investigating the relationship quality of first-time mothers with friends and family members who provide social support during the birthing process seeks out and interviews a midwife who has helped deliver more than 1000 babies in homes during her career

non probability- purposive

94

Know the rule of thumb used in this class about sample size.

1,000 is good, 100 is okay, 10 is worthless.
The more participants, the better the accuracy.

95

Ceteris Paribus

All things being equal, relates to experimental designs and the use of statistical control variables.

96

Retrospective Data

Going back into the past, ask a question about someones past. k