Ps 3000 First Test

Front 1

empirical analysis

Back 1

how and what we know

Front 2

normative analysis

Back 2

how we should use our knowledge

Front 3

quantitative

Back 3

statistical comparisons

Front 4

qualitative

Back 4

based on informed understanding

Front 5

normative

Back 5

developing and examining subjective goals, values, and moral rules to guide us in applying what we learned

Front 6

scientific research

Back 6

effective, explicit, systematic, controlled. method of testing theories and hypothesis by applying certain rules of analysis to the observation and interpretation of reality under strictly delineated circumstances

Front 7

research process

Back 7

1. theory
2. operationalization
3. select research techniques
4. observe behavior
5. analyze data
6. interpret results

Front 8

formulation of theory

Back 8

think of a research question. should fulfill a scientific or societal need.

Front 9

operationalization

Back 9

put theory into concrete terms so you can measure it. decide what socioeconomic status means - for example, annual income...

Front 10

selection of appropriate research technique

Back 10

qppropriateness of a technique depends on teh proglem

Front 11

observation of behavior

Back 11

actually doing the research

Front 12

generalizability

Back 12

how representative is your sample? how far can you say your findings represent the world?

Front 13

reactivity

Back 13

observation should not effect the subject

Front 14

hawthorne effect

Back 14

presence of an observer made workers more productive = research was invalid

Front 15

analysis of data

Back 15

is there a relationship? how confident and representative are your findings?

Front 16

theorizing

Back 16

attempt to create possible explanations for events

Front 17

theories

Back 17

sets of logically related symbols that represent what we think happens in the world

Front 18

exploratory research

Back 18

background research to understand more

Front 19

induction

Back 19

process of generalizing from what we have observed to what we have not observed

Front 20

empirically grounded

Back 20

theories built through inductions from observations

Front 21

deductions

Back 21

opposite of induction.. moving from generalizations to specifics

Front 22

theory construction

Back 22

1. use induction to make assumptions
2. use deduction to make predictions
3. test predictions against observations
4. revise assumptions

Front 23

theoretical import

Back 23

important in theory to explain

Front 24

proposition

Back 24

statement of relationship between concets

Front 25

covariation

Back 25

as one goes up or down, the other goes up or down. does not tell why, just that it happens

Front 26

causal

Back 26

one directly influences the other.

Front 27

testing causation

Back 27

1. must change together
2. cause must precede effect
3. identify causal linkage or process by which it happens
4. covariance must not be due to a 3rd factor

Front 28

spurious relationship

Back 28

when A and B vary together only because they are both caused by C

Front 29

theorizing

Back 29

attempt to create possible explanations for events

Front 30

theories

Back 30

sets of logically related symbols that represent what we think happens in the world

Front 31

exploratory research

Back 31

background research to understand more

Front 32

induction

Back 32

process of generalizing from what we have observed to what we have not observed

Front 33

empirically grounded

Back 33

theories built through inductions from observations

Front 34

deductions

Back 34

opposite of induction.. moving from generalizations to specifics

Front 35

theory construction

Back 35

1. use induction to make assumptions
2. use deduction to make predictions
3. test predictions against observations
4. revise assumptions

Front 36

theoretical import

Back 36

important in theory to explain

Front 37

proposition

Back 37

statement of relationship between concets

Front 38

covariation

Back 38

as one goes up or down, the other goes up or down. does not tell why, just that it happens

Front 39

causal

Back 39

one directly influences the other.

Front 40

testing causation

Back 40

1. must change together
2. cause must precede effect
3. identify causal linkage or process by which it happens
4. covariance must not be due to a 3rd factor

Front 41

spurious relationship

Back 41

when A and B vary together only because they are both caused by C

Front 42

causal model

Back 42

shows all relationships in a theory

Front 43

hypothesis

Back 43

a statement of what we believe to be true

Front 44

intervening variables

Back 44

link independent and dependent variables

Front 45

antecedent variable

Back 45

comes before independent variable.
parents strong party ID leads to child's strong party ID leads to more likely to vote

Front 46

alternative rival hypothesis

Back 46

another hypothesis that only one can be true. typically a spurious relationship hypothesis can be tested against original hypothesis

Front 47

research design

Back 47

the scheme that guides teh process of collecting, analyzing and interpreting data. it is a logical model of proof that allows teh making fo valid causal inferences. what you intend to do, how, and why in this particular way

Front 48

exploratory research

Back 48

to learn more in order to make a specific question
FLEXIBLE

Front 49

descriptive research

Back 49

better explain or describe a phenomenon in order to make a better hypothesis
ACCURATE

Front 50

explanatory research

Back 50

to test a hypothesis. when we already know a lot about a phenomenon. if you can use the results of the study to argue that one thing causes another.
BASIS FOR CAUSAL INFERENCE

Front 51

coping with alternative rival hypothesis

Back 51

you can have a control group. show that the difference in the DV between the two groups is the IV.

Front 52

adequacy

Back 52

a research design needs to be logically support the inferences that you want to make. you need to look for alternative rival hypotheses.

Front 53

rival hypothesis

Back 53

means that both it and the original hypothesis cannot be true

Front 54

experimental design

Back 54

experimental group with subjects exposed to the IV stimulus and a control group not exposed to the stimulus

Front 55

pretest

Back 55

measure DV before experiment

Front 56

posttest

Back 56

measure DV after experiment

Front 57

test effect

Back 57

when subjects react to the pretest

Front 58

solomon two-control-group research design

Back 58

removes test effect from scores

Front 59

solomon three-control-group research design

Back 59

removes changes due to external factors

Front 60

randomization

Back 60

helps have similar groups. rules out alternative rival hypotheses that say that differences in groups made the results.
the key to successful laboratory experiments

Front 61

precision matching

Back 61

identify characteristics that may influence subjects responses and make sure the groups have matched people with the same characteristics

Front 62

frequency distribution control

Back 62

make groups with same average characteristics and distribution fo characteristics. same number of males, same average age, more practical than precision matching

Front 63

political communication experiments

Back 63

expose subjects to newspaper or TV ads, newscasts

Front 64

field experiments and non-experimental designs

Back 64

when you change IV and exposure but not other factors

Front 65

quasi-experimental design

Back 65

most common. cannot control Iv exposure or conditions but can gather data or analyze data and stimulate an experimental design. "proceed as if we had exercised all control characteristic of a true experiment and they provide a basis for causal inferences"

Front 66

ex-post facto experiment

Back 66

= collect data and analyze. survey randomly to see if college education affects voting behavior. it's as if they set up an experiment years ago and exposed some to college and some not

Front 67

time series design p. 110

Back 67

researcher makes several observations before and after the introduction of a causal phenomenon and compares DV before and after.
shows if IV changed the trend

Front 68

regression toward the mean

Back 68

extreme DV measurements tend to return to teh average value regardless of IV exposure. you can control this by using a controlled time-series design. compare to a data set like ours that is not exposed to the IV (like seeing if a policy worked to reduce crime...)

Front 69

research papers

Back 69

empirical but not normative = do not suggest policy initiatives...

Front 70

confirm a hypothesis if...

Back 70

1. find covariation between X and Y
2. find covariation was not due to chance
3. disconfirms alternative explanations

Front 71

quasi experiments

Back 71

only mediocre at deciding on alternative explanations.

Front 72

omitted variable bias

Back 72

when z could be related and you ignore it

Front 73

intervening variable

Back 73

when X causes Y but it goes through something - how X leads to Y

Front 74

spurious relationship

Back 74

when A and B vary together only because they are both related to C

Front 75

antecedent variable

Back 75

comes before independent variable. like parents strong party ID - kids stong ID - more likely to vote

Front 76

coding

Back 76

process of assigning numerical values to observations

Front 77

codes

Back 77

allows the researacher to come to conclusions...numerical manifestations of operational definitions

Front 78

nominal

Back 78

categories

Front 79

ordinal

Back 79

ranking

Front 80

interval ratio

Back 80

dollars, years, numbers.

Front 81

codebook

Back 81

listing of variables, values, and codes associated with the experiment

Front 82

coding sheet

Back 82

data recording device

Front 83

operationalization

Back 83

selecting observable phemonena to represent abstract concepts

Front 84

instrumentation

Back 84

specification of steps to take in making observations

Front 85

measurement

Back 85

application of an instrument to assign numerical values to cases

Front 86

indicator

Back 86

to measure a variable (like height in inches)

Front 87

observation

Back 87

process of using a measuring instruement to assign values to a phenomenon in cases

Front 88

multidimensional

Back 88

using multiple indicators of a variable

Front 89

operational definitions

Back 89

explicitly define valid and reliable measures of concepts studied

Front 90

NOIR

Back 90

nominal - ordinal - interval - ratio

Front 91

measurement error

Back 91

differences between cases that result because of a flawed measuring process, not because of actual differences.

Front 92

systematic errors

Back 92

from confusion of variables of the nature of teh instrument

Front 93

random errors

Back 93

due to characteristics/variations by chance

Front 94

validity

Back 94

extent to which our measures correspond to the concepts they are intended to reflect. are you measuring what you want to measure?

Front 95

internal validity

Back 95

are we measuring what we want to measure?

Front 96

external validity

Back 96

generalizablity of our results

Front 97

pragmatic validation

Back 97

degree to which a measure allows us to predict behavior and events

Front 98

predictive validity

Back 98

the better we are at predicting, the more valid the measure

Front 99

construct validation

Back 99

infer validity. determine the extent to which a variety of measures are consistent with what our theory predicts

Front 100

discriminant validation

Back 100

whether using it as an indicator of a given condept allows us to distinguish that conept form other concepts

Front 101

face validation

Back 101

it may seem valid, but you should do other tests

Front 102

reliability

Back 102

how stable the values are. do you consistently get the same answer?

Front 103

representative sample

Back 103

reflects teh population

Front 104

sample

Back 104

a subset of a population

Front 105

sampling bias

Back 105

nonrandom differences between sample and population

Front 106

sampling error

Back 106

chance difference between sample statistic and population parameter

Front 107

what's the difference between sample bias and sample error?

Back 107

sampling bias is your fault. sampling error is simply due to chance

Front 108

simple random sample

Back 108

number population 1-N
choose a sample size n
randomly choose n cases

Front 109

cluster sample

Back 109

divide population into clusters
choose clusters at random

Front 110

bad sampling techniques:
convenience sampling
volunteer sampling
quota sampling

Back 110

response bias, self-selection bias

Front 111

aggregate data

Back 111

individual data - single human beings based on surveys

aggregate data - based on characteristics of an entire group
average level of US education

Front 112

confidence interval

Back 112

40-60%
confidence interval is a concrete measure of sampling error - random chance

Front 113

confidence interval shrinks...

Back 113

as sample size grows

Front 114

confidence interval grows...

Back 114

as level of confidence grows.
49-51% grows as 95% goes to 99%

Front 115

sampling error

Back 115

due to chance.
even if you have a valid, unbiased measure, you can still have sampling error

Front 116

bigger sample size means...

Back 116

less sampling error

Front 117

52, 48, 52

Back 117

sampling error. not reliable

Front 118

70, 70, 70,

Back 118

sampling bias. not valid