Up to Exam 2 Flashcards

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1
Q

Four scales of measurements

A

Nominal scale : one object is different from another

Ordinal scale : one object is bigger or better or more of anything than another

Interval scale : one object is so many units (degrees ect) more than another

Ratio scale : Interval scale with an absolute zero

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2
Q

Validity and Reliability of Measures

A

Validity = the extent to which a measurement instrument measures what it is intended to measure

Reliability = the consistency with which a measurement instrument yields a certain result when the entity being measured hasn’t changed

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3
Q

what is cross - level analysis

A

researchers use data collected for one unit of analysis to make inferences about another unit of analysis

two reasons why one would use cross level analysis: cost and availability issues

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4
Q

what is ecological inferences

A

cross level analysis goal = using aggregate data to study the behavior of individuals

Relationship between schools average test scores and percentage of children receiving subsidized

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5
Q

what is ecological fallacy

NOT IN BOOK

A

it is the use of information that shows a relationship for groups to infer that the same relationship exists for individuals when in fact there is no such relationship at the individual level

Ecological Fallacy = a flaw

example: group x has characteristic y. person 1 is in group x, so that person must have characteristic y.

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6
Q

defining statements about concepts

six

A

concepts help us observe and understand aspects of our environment and help us communicate with others

a word or symbol that represents some idea

contributed to the identification and delineation of the scientific disciplines within which research is conducted

are developed through a process by which some human group (tribe, nation) agrees to give a phenomenon or a property a particular name

disappear from a group’s language when they are not longer needed, and ew ones are invented as a new phenomena are noticed that require names

we use concepts everyday to help cope with the complexity of reality by categorizing the things we encounter according to some of their properties that are relevant to us.

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7
Q

a concept is

A

a word or symbol that represents some idea

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8
Q

do we use concepts everyday?

A

yes

we use concepts everyday to help cope with the complexity of reality by categorizing the things we encounter according to some of their properties that are relevant to us

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9
Q

concepts in social science and in everyday

A

Social science concepts serve the same purpose as everyday concepts

they point to the properities of objects (people, political systems) that are relevant to particular inquiry. One observer might be interested in a a person’s personality structure, another is interested in partisan identification, and a third focuses on the person’s level of political alienation

the person has all of these properties and many more but only certain of the properties are relevant to any given piece of research

all three observers are dealing with he same reality; they simply choose to organize their perceptions of it differently.

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10
Q

Three Definitions: Concepts

Three things a concept must be

A

concepts help us to decide which of the many traits or attributes are important to our research

concepts, like theories, do not have a life of their own.

concepts are tools we create for specific purposes and cannot be labeled true or false, but only more or less useful.

Concepts must be
precise, accurate, informative

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11
Q

what makes a concept useful

A

the concept must refer to phenomena that are at least potentially Observable

a concept must refer to something that can be measured with our ordinary senses

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12
Q

examples of concepts

A

people simply do not have a class status in the way they have red hair, but if we know certain things about them (income, occupation) we can infer what their class status is

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13
Q

question about concepts

A

can we devise a set of procedures for using our senses to gather information that will allow us to judge the presence or absence of magnitude int he real world of the thing to which the Concept refers?

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14
Q

Empirical referents

A

if we can do this for a concept, it is said to have an empirical referents; it refers to something that is directly or indirectly observable

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15
Q

why is precision important in concepts?

A

it tells us what to observe in order to see how a concept is manifested in any given case

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16
Q

what is a theoretical import

A

a concept has a theoretical import

a concept has a theoretical import when it is related to enough other concepts in the theory that it play an essential role in the explanation of observed events

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17
Q

operationalization

A

deciding how to record empirical observations of the occurrence of an attribute or a behavior using numerals or scores

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18
Q

conceptualization to operationalization

A

researchers must define the concepts they use in their hypotheses through Conceptualization. they also must decide how to measure the presence, absence, or the amount of these concepts in the real world.

Political scientists refer to this process as Operationalization

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19
Q

operational definitions are…

A

seldom absolutely correct or incorrect

are evaluated according to how well they correspond to the concepts they are meant to measure

***Arriving at the operational definition is the last stage in the process of defining a concept precisely **

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20
Q

measurement

A

the process by which phenomena is observed systematically and represented by scores or numerals

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21
Q

test retest method of reliability

A

applying the same “test” to the same observations after a period of time and then comparing the results of the different measurements

same score over time over and over again

Difficulty arises when our measure involves interviewing people (as opposed to inanimate objects) if we repeat questions in a short time, interviewees may. remember their first answer and, in effort to be consistent, repeat that answer rather than respond truthfully in answering the question.

this can be problematic because what’s being measured can change, it is not unreliable

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22
Q

alternative form method of reliability

A

measuring the same attribute more than once but uses two different measures of the same concept rather than the same measure.
using two sets of questions about the same topic and seeing if the answers are reliable

different forms of the measure are applied to the same group of cases, or the same measure is applied to different groups at the same time.

if we can assume that these conditions are met, the more the score on the two measures, or the score of the two groups, are alike, the more confidence we have in the reliability of our measure.

If we cannot come up with comparable measures or groups, we cannot use the method properly

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23
Q

split-halves method of reliability

A

applying two measures of the same concept at the same time.

used with multi-item measures that can be split in two halves

in a survey, five questions represent one measure of the topic and the other represent a second half. if the scores are similar, the ten item measure is reliable

this methods avoids the problem that the concept being measured ay change between measures. Often used when a multi-item measure can be split into two equivalent halves.

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24
Q

validity

A

the degree of correspondence between the measure and concept it is thought to measure

Unlike reliability, which depends on whether repeated applications the same or equivalent measures yield the same results.

voting: always over estimated because of self-reported voting
invalid if it measures a slightly or very different concept than intended
more difficult to demonstrate empirically than reliability

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25
Q

face validity

A

asserted (not empirically demonstrated) when the measurement instrument appears to measure the concept it is supposed to measure.

measurements lack face validity when there are good reasons to question the correspondence of the measure to the concept in question. It is essentially a matter of judgement. if there is no consensus, the face validity is problematic

to assess the face validity of a measure we need to know the meaning of the concept being measured and whether the information being collected is relevant

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26
Q

Content validity

A

involves determining the full domain or meaning of a particular concept and then making sure all of the components of the meaning are included in the measure.
measuring democracy: two concepts political rights and civil liberties, eleven items in each. we must make sure that all eleven (twenty two) components in the definition

Content validity is similar to face validity but involves determining the full domain (whole content) or meaning of a particular concept and then making sure that ALL components of the meaning are including in the measure

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27
Q

Construct validity

A

Construct validation
is achieved by inferring the validity of a measure from evidence of the extent to which actual relationships between scores of various measures are consistent with what we expect from the theory that has led us to use a given indicator
Key term is inferring

two different ways to measure empirically
convergent construct validity
discriminant construct validity

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28
Q

convergent construct validity

A

measure of a concept is related to a measure of another concept with which the original concept is thought to be related.
two concepts ought to be related in a positive or negative manner. the researcher then develops a measure of each of the concepts and examines the relationship between them.

if one measure is positively or negatively correlated, then one measure has convergent validity for the other measure.
if there is no relationship between the measures, then the theoretical relationship is an error

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29
Q

discriminant construct validity

A

involves two measures that theoretically are expected to NOT be related; thus, the correlation between them is expected to be low or weak. If the measures do not correlate with one another, then discriminant construct validity is demonstrated

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30
Q

interitem association

A

the type of validity test most often used by political scientists

relies on the similarities of outcomes of more than one measure of a concept to demonstrate the validity of the entire measurement scheme because just one measure is more prone to error or misclassification of a case

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31
Q

correlation matrix

A

shows how strongly related each of the items in the measurement scheme is to all the other items.

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32
Q

random measurement error

A

an error in measurement that has no systematic direction or cause

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33
Q

what would happen if you had inaccurate measurements in your research?

A

inaccurate measurements may lead to erroneous conclusions, since they will interfere with our availability to observe the actual relationship between two or more variables

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34
Q

what are the two major threats to the accuracy of measurements?

A

measures may be unreliable or invalid

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35
Q

what does an unreliable measure produce?

A

an unreliable measure that produces inconsistent results– sometimes higher sometimes lower

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36
Q

what is reliability?

A

describes the consistency of results from a procedure or measure in repeated tests or trials
in the context of measurement, a reliable measure is one that produces the same result each time the measure is used

Can we get the same value for any given case when we apply the measure several different times, or does each application result in the assignment of a different value to each case?

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37
Q

a measure may be reliable without being valid BUT

A

it cannot be valid without being reliable.

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38
Q

systematic and random error in validity and reliability

A

validity is challenged by both systematic and random error, but reliability is jeopardized only by random error

This means that if a measure have convincingly validated in prior studies, we can use it without being worried about its reliability; it has to be reliable if it is valid.

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39
Q

how do we guard against unreliability?

A

preventing unreliability depends on our being aware of the various sources of random measurement error and doing what we can to control them

Catch it before it happens

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40
Q

how do we determine whether or not a given measure is reliable?

A

PRETESTING

thinking through the actual measurement process and pretesting our measuring instruments to discover previously unrecognized causes of random error

pretest= smaller sample size

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41
Q

why is it difficult to determine whether or not we have devised a reliable measure in the social sciences?

A

the true value of the variables with which we are concerned can change dramatically with time and circumstance– people change their opinions

it is hard to distinguish the effects of random measurement error from genuine fluctuations in the concepts being measured.

So, tests of reliability should be conducted over a short period of time.

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42
Q

Steps to the split halves method

A

administer the test to a large group of students (ideally, over 30)

randomly divide the test question into two parts. separate even and odd questions

score each half of the test for each student

find the correlation coefficient for the two halves

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43
Q

Split halves

NOT FROM TEXT BOOK

A

used for measuring the internal consistency of the test
it measures the extent to which all parts of the test contribute equally
a test or group is divided into two parts
scores collected from two half tests or two parallel groups

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44
Q

Nominal measurement

A

provides the LEAST amount of information about a phenomenon

Nominal measurement is obtained by simple naming cases by some predetermined scheme of classification

Nationality is typically measured at the nominal level by classifying people as Swiss, American eat.

That measurement neither tells us how much of the characteristic nationality different individuals have nor allows us to rank-order them

Using nominal measurement simply gives us a way of sorting cases into groups designated by the names used in a. classificatory scheme

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45
Q

Ordinal measurement

A

provides more information because it allows us both to categorize and to order, or rank, phenomena

ordinal measurement allows us to associate a number with each case. That number tells us not only that the cause is different from some there cases, and similar to still others (with respect to the variable being measured) but also how it relates to those other cases in terms of how much of a particular property it exhibits

With ordinal measurement we cans ay which cases have more or less of the measured quality that other cases and we can rank cases in the order of how much of the quality they exhibit

that ranking gives us more detailed and precise information about the cases than we would get from a nominal measurement

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46
Q

Interval measurement

NOT IN TEXTBOOK

A

Provides even more information

We can classify and rank order cases when they have been measured at the Interval level, we can also tell HOW MUCH (or less) of the measured property they contain than other cases

Ordinal measurement is NOT based on standardized unit of the variable in question and does NOT allow us to tell how far cases are from one another in terms of that variable. It allows us ONLY to say that some have more or less of it than others.

Interval measurement is based on the idea that there is some Standard Unit of the property being measured.

Interval measurement provides information on the “distance” between cases

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47
Q

Levels of Measurement

A

measuring procedures provide a means of categorizing and ordering phenomena

Some procedures produce more precise and detailed distinctions between events than do others. Because of this, there are various Levels of Measurements

When we say a procedure produces a given level of Measurement, we are classifying it according to how much information it gives us about the phenomena being Measured and their relationship to one another

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48
Q

Sets needed to make nominal measurement useful

NOT IN TEXTBOOK

A

to be useful, nominal measurement schemes must be based on sets that are Mutually Exclusive and Collectively Exhausive

It must not be possible to assign any single case to more than one category

the categories should be set up so that ALL cases can be assigned to some category

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49
Q

Nominal example: US voters

NOT IN TEXTBOOK

A

if we want to classify voters in the US by use of a nominal measuring scheme, we cannot use the categories democratic republic liberal and conservative successfully because these categories are not mutually exclusive

Since US political parties each appeal to a broad spectrum of voters, it is possible for a person to be both DEM and conservatives or liberal, or both a. Republican and a conservative or liberal

the categories do not allow us to differentiate among voters in all cases

If we try to categorize voters by party affiliation using only two categories-repub and dem- we will find that our categories are not collectively exhaustive, because some voters consider themselves independents or members of other parties

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50
Q

Ordinal Measurement example

NOT IN TEXTBOOK

A

Example: social class- often measured at the ordinal level with individuals being ranked as lower middle or upper class

51
Q

Interval Measurement Example

NOT IN TEXTBOOK

A

income is usually measured in units of currency

We cannot do that with ordinal measurement. IF we measure income ordinal by dividing people into such income categories as under 10K and 10 K to 19,999 we can say that tone person has more income than another, but we cannot say exactly how far apart they are in income difference between a person in category 1 (under 10K) and a person in category 2 can be as little as one dollar or as much s 10 k depending on their exact incomes but we cannot make this distinction from an ordinal measure.

52
Q

Interval Measurement example 2

NOT IN TEXTBOOK

A

Interval measurement lets us make accurate statements about the relative differences between concepts.

Example: we agree that 50k people are twice as large as 25k people because we ca speak meaningfully of a place that has no population

there is a zero point in true interval measures, and it is at least conceivable possible for a case to score zero on such measures

because there is no meaningful zero point on an ordinal scale, we cannot say, for example, that upper class people have twice as much class as lower class people because we don’t know what it means to have no class standing

53
Q

validity

A

a valid measure is one that measures what is supposed to measure

unlike reliability which depends now whether repeated applications of the same or equivalent measures yield that same result

validity refers to the degree of correspondence between the measure and the concept it is thought to measure

54
Q

validity-voter turnout

A

many studies examine the facotrs that affect voter turnout and thus require an accurate measurement of voter turnout

one way of measuring voter runout is to ask people if they voted int he last election- self reported voting

given the social desirability of voting in the us wearing the I voted sticker or position I voted on social media can bring social rewards- will nonvoters adroit to their failtree to vote to an open interviewer

Some nonvoters may claim in surveys to have noted resulting inanimate invalid measure of voter turnout that overstates the number of voters. In fact, this is what usually happens. voter surveys yes commonly overestimate turnout by several percentage points

55
Q

Validity- ideology

A

a measure can also be invalid if it measures a slightly or very different concept than intended

Example: researcher purposes to measure ideology, conceptualized as an individual’s political views on a continuum between Conservative, Moderate and Liberal

if he asks which one do you feel closest to, Democratic or Republican, this measure would be invalid because it fails to measure ideology as conceptualized

Partisan affinity, while often consistent with Ideology, is not the same as Ideology. This measure should be valid measure of party ID, but not ideology

56
Q

validity – indicators

A

we can seldom obtain direct measures of the concepts used in social science theories. such concepts as power, democracy, and representation cannot quantified as simply such concepts as length and weight

we have to use indicators that correspond only indirectly tot he concepts the represent. there is always a chance that the indicators we choose will not adequately reflect the concepts we want to measure

validity is the term we used to refer to the extent to which our measures correspond to the concepts they are intended to reflect.

57
Q

to be valid, a measure must be

A

appropriate and complete

achieving validity is often viewed as the basic problem of measurement in the social sciences.

example: public edcuation number of teachers in the schools as an indicator of the quality of educational services

This measure is inappropriate, because the number of personnel in school systems is determined largely by the number of students and the size of the city and may have little to do with the quality of education.

**if we use the ratio of students to teachers as our indicator of educational services, we willl have a more appropriate measure in that differences caused by city size will be reduced or eliminated. this measure will still be incomplete though

education involves more than teachers. it also involves school buildings, films, books. labs act

we cannot just look at one factor, because a school system might have a high;y desirable student-teacher ratio but inadequate facilities and learning materials

58
Q

If we are to achieve validity, we must

A

always strive to construct measures that are both appropriate and complete!

59
Q

how can we create measures that are complete and appropriate?

A

begins with the OPERATIONALIZATION PROCESS

we can define validity as the extent to which differences in scores on a measure reflect only differences int he distribution of values on the variable we intend to measure

since we can probably never achieve e complete and total validity, our goal should be to select measures that are susceptible to as few influences as possible other than differences in our target variable

this requires that we think carefully through the process that surround our measures in search of possible causes of variations in scores

if we want to measure the relative influence of different interest groups in a state legislature, we may think of using newspaper reports interest group appearance before legislative committees as our indicator. There are so many other means of exercising influence that a measure that relies exclusively on the giving of testimony as an indicator of influence is incomplete

achieving appropriate and relatively complete operationalizations depends both unknowing a good deal about the subject of our study and on conducting a careful logical analysis of at

60
Q

how can we tell whether we have succeeded in creating measures that are complete and appropriate?

A

we can check the validity of our measures I order to determine whether or not to determine whether or not we have developed sound measures only after we have collected

the process of evaluating the validity of our measures is referred to as validation

61
Q

Pragmatic validation

A

involves assessing the validity of a measure from evidence of how well it works in allowing us to predict behaviors and events

requires that there be some alternative indicator of variables that we feel fairly certain is a valid reflection of them

We check our measures against this alternative as we might check the accuracy of verbal reports of age against birth certificates.

62
Q

construct validation

A

achieved by INFERRING the validity of a measure from evidence of the extent to which actual relationships between scores of various measures are consistent with what we expect from the theory that has led us to use a given indicator

63
Q

Example of construct validation

A

consider a study of international alliances. We might create a measure of the strength of an alliance based on a content analysis of newspaper articles from countries involved. Is what the newspaper of one nation say about another nation a valid indicator or the strength of the alliance between the two countries?

We might get an idea of whether it is by reasoning as follows: our theory tells us that the stronger an alliance between two nations is, the more often they will vote together in the United Nations and the fewer restrictions they will place on trade with each other.

Therefore scores on a valid measure of strength of alliance will be positively related to scores on measures of number of trade barriers

we then proceed to do the data analysis necessary to see whether this expectation is supported by our observations

if the relationships are as expected, we will have greater confidence in the validity of our measure of strength of alliance

if they are not as we have expected, we will question whether we have a sound measure of this concept

64
Q

External Validation

A

involves comparing scores on the measure being validated with scores on measures of other variables

Efforts at external validation will produce convincing evidence about the validity of our measure of one variable only if we can have a high degree of confidence in the validity of the measures we use for the other variables

65
Q

Internal (convergent) validation

A

the of validation involves devising several measures of the same variable and comparing scores on these various measures

We reason that if each of the indicators provides a valid measure of the concept in question, the scores individual cases receive on the measure should be closely related

If A, B and C are all valid measures of X, then any individuals scores on A, B and C should be highly similar

Cause and effect relationship

66
Q

Discriminant validation

A

asks whether using a measure as an indicator of a given concept allows us to distinguish that concept from other concepts

we might want to measure the concept “trust in public officials” through a series of questions in a survey. If we also have on the questionnaire a series of questions designed to measure trust in people (in general) we can compare the scores not he two measures to ask whether our first set of questions actually reflects simply another way of measuring trust in people.

if scores are highly similar we say that the political trust measure does not have discriminant validity because it does not permit us to distinguish the concept of trust in political officials from he concepts of trust in people

67
Q

sub-sample method

A

drawing one sample of cases and divide it into several subsamples in such a way that each is highly similar to the other s in composition

we then apply the same measure to all subsamples and use the similarity or difference of responses from subsample to subsample as an indicator of the reliability of the measure

68
Q

data is

A

transient and ever changing

not absolute reality but manifestations of reality

must meet certain criteria to be admitted to study

69
Q

primary and secondary data

A

primary: closest layer to the the truth

secondary

70
Q

4 questions about the planning and collection of data collection

A

what data is needed?
where is the data located?
how will the data be obtained?
how will the data be interpreted?

71
Q

how do you identify appropriate measurement instruments?

A

pin down data by measuring it in some way

measurement instruments provide a basis on which the entire research effort rests

a research effort employing faulty measurement tools is of little value in solving the problem under investigation

in planning the researching project, the nature of the measurement instruments should be clearly identified

instrumentation should be describe in explicit, concrete terms

72
Q

define measurement

A

limiting the data of any phenomenon- substantial or insubstantial- so that those data may be interpreted and, ultimately, compared to a particular qualitative or quantitative standard

73
Q

Substantial measurement

A

physical substance

74
Q

insubstantial measurement

A

exist only as concepts, ideas, opinions, feelings, or other intangible entities

75
Q

internal validity

A

the extent to which the design and data of a research study allow the researcher to draw accurate conclusions about the cause and effect and other relationships within the data

76
Q

external validity

A

the extent to which the results of a research study apply to situations beyond the study itself, generalized

beyond the study itself

77
Q

the goal of sampling

A

to create a sample that is identical to the population in all characteristics except size

any difference between a population and sample is defined as bias

78
Q

informed consent means

six

A

subjects are given information about the research,
its purposes,
risks/anticipated benefits,
alternative procedures (when therapy is involved),
how subjects are selected,
and the person responsible for the research.

79
Q

what is a research design

A

a plan that shows how one intends to study an empirical question

indicates what specific theory or propositions will be tested, appropriate units of analysis, measurements and observations are needed and which analytical and statistical procedures will be used

all parts of a research design should work to the same end: drawing sound conclusions supported by observable evidence

80
Q

research design continued

A

a scheme that guides the process of collecting, analyzing and interpreting data. it is a logical model of proof that allows the making of valid causal inferences

if it doesn’t make sense to you it’s not going to make sense to anyone else

81
Q

exploratory research

A

intended to provide greater familiarity with the phenomena we want to investigate so that we can formulate more precise research questions and perhaps develop hypotheses

such studies can be essential when we are investigating new phenomena or old phenomena that have not beens studied before

82
Q

descriptive research

A

closed

intended to provide an accurate representation of some phenomenon so that we can better formulate research questions and hypotheses

example: frequency, geographic distribution and sequence of events of some phenomenon need to know what other phenomenon it leads to be associated with before we can begin to theorize ab out what might have caused it

83
Q

causal hypothesis and exploratory research

A

if we can use the results of a study to argue that one thing causes another, we can begin to develop explanations of the second event. For that reason, hypothesis testing research may be described as explanatory research

84
Q

exploratory research requires

A

Opened

flexibility more than precision

needs to provide only an opportunity to observe the phenomenon in question

ensure unbiased and reliable observation and provide a basis for inferring the causal influence of one or more variables

85
Q

descriptive research requires

A

accurate measurement of phenomena

the research design must ensure unbiased and reliable observations

86
Q

what is a population?

A

any well defined set of unit of analysis
determined by the research question
consistent through all parts of a research project

87
Q

what is a sample?

A

drawn through a systematic procedure called a sampling method

88
Q

ethical issues in research

six

A
honesty with professional colleagues 
internal review board (rib)
protection from harm 
informed consent
right to privacy
professional code of ethics
89
Q

to avoid tautologies, one must have..

A

clear definitions of the concepts of interest are important if we are to develop specific hypotheses and avoid tautologies

must refer to one and ONLY ONE set of properties of some phenomenon

we must be able to know exactly what we are talking about when we use a concept to describe an object

90
Q

a research design provides

A

a basis for causal inferences when it allows us to rule out any PLAUSIBLE explanations for observed results that represent alternatives to the causal hypothesis being tested.

91
Q

6 steps (elements) of a research design

A

a statement of the purpose of the research

a statement of the hypothesis to be tested

a specification of the variables to be employed

a statement of how each variable is to be operationalized and measured

a detailed statement of how observations are to be organized and conducted

a general discussion of how the data collected will be analyzed (compare your work to others)

92
Q

Feasibility

A

the projects feasibility or practicality
some designs may be unethical, while others may be impossible to implement for lack of data or insufficient time or money

balance what is possible to accomplish against what would ideally be done to investigate a particular hypothesis

93
Q

purpose of the investigation

A

whether the research is intended to be exploratory, descriptive, or explanatory

94
Q

Verifying causal assertions

A

lets say that all those who report viewing negative commercials tell us they did not vote, whereas all those who were not aware of these ads cast ballots. we might summarize that hypothetical results in a simple table.

let x stand for whether or not people saw the campaign ads and Y for whether or not they voted. what this symbolizes is a relationship or association between X and Y

95
Q

opinion research

A

involves an investigator or observing behavior indirectly by asking people questions about what they believe and how they act.

since we do not directly observe their actions, we can only take the respondents word about whether or not they voted or saw attack ads.

96
Q

spurious relationship

A

arises because two things are both affected by a third factor and thus appear to be related. two things are affected by one. once this additional factor has been identified and controlled for, the original relationship weakens or disappears altogether

97
Q

Feasibility

A

the projects feasibility or practicality
some designs may be unethical, while others may be impossible to implement for lack of data or insufficient time or money

balance what is possible to accomplish against what would ideally be done to investigate a particular hypothesis

98
Q

three things to a valid causal design

A

1) covariation
2) time order
3) elimination of possible alternative causes, sometimes termed “confounding factors”

99
Q

covariation

A

demonstrates that the alleged cause (call it x) does in fact covary with the supposed effect (y)

covariational relationship indicate that two or more concepts tend to change together: as one increases (or decreases) the other increases (or decreases)

covariational relationships tell us nothing about what causes the tow concepts to change together

100
Q

Verifying causal assertions

A

lets say that all those who report viewing negative commercials tell us they did not vote, whereas all those who were not aware of these ads cast ballots. we might summarize that hypothetical results in a simple table.

let x stand for whether or not people saw the campaign ads and Y for whether or not they voted. what this symbolizes is a relationship or association between X and Y

101
Q

distinguishing real causal relations from spurious ones

A

distinguishing real causal relations from spurious ones in an important part of scientific research. to explain phenomena fully, we must know how and why two things are connected not simply that they are associated. thus, one of the major goals in designing research is to come up with a way to make valid causal inferences.

102
Q

three things to a valid causal design

A

1) covariation
2) time order
3) elimination of possible alternative causes, sometimes termed “confounding factors”

103
Q

three scales

A

likkert
molken
goodman

104
Q

likert scale

A

a multi item measure in which the items are selected based on their ability to discriminate between those scoring high and those scoring low on the measure.

105
Q

Goodman scale

A

a multi item measure in which respondents are presented with increasing difficult measures of approval for an attitude

106
Q

Mokken scale

A

a type of scaling procedure that assesses the extent to which there is order in the responses of respondents to multiple items

107
Q

focus group

A

10 to 20 individuals
often used in market research to probe reactions to stimuli such as commercials
meet in a single location and discuss with a leader

108
Q

comparative study

A

more likely to have explanatory power than a single case study
provides the opportunity for replication

109
Q

policy evaluation

A

sometimes called policy analysis

simply means objectively analyzing the economic political cultural and social impacts of public policies

110
Q

external validity

A

the extent to which the results of a study can be generalized across populations times and settings is the touchstone for natural and social scientists alike.

111
Q

an experiment allows the researcher too…

A

control exposure to an experimental variable (often called a test stimulus, test factor, or independent variable) the assignment

112
Q

an experimenter starts by establishing what two groups

A

experimental group and control group

113
Q

a subset of a population is a

A

sample

114
Q

Opinion Research

A

involves an investigator or observing behavior indirectly by asking people questions about what they believe and how they act.

115
Q

Causal Relationships

A

exist when changes in one or more concepts lead to or cause changes in one or more other concepts.

116
Q

Causal Relationships – Conditions

A

First – the postulated cause and effect must change together, or Covary
Second – the cause must precede the effect •
Third – we must be able to identify a Causal Linkage between the supposed cause and effect (meaning, we must be able to identify the process by which changes in one factor cause changes in another) •
Fourth – the covariance of the cause-and-effect phenomena must not be due to their simultaneous relationship to some third factor

117
Q

Probability Sampling

A

Probability samples: each element in the population has a known probability of inclusion in the sample.

Probability samples are a better choice than non-probability samples when possible because they are more likely to be representative and unbiased.

118
Q

Non-probability sampling

A

Non-probability samples: samples for which each element in the population has an unknown probability of inclusion in the sample.
These sampling techniques, while less representative, are used to collect data when probability samples are not feasible.

119
Q

Where we would like sampling to lead

A

The goal of statistical inference is to make supportable conclusions about the unknown characteristics, or parameters, of a population based on the known characteristics of a sample measured through sample statistics.

120
Q

five steps to theory

A
development of idea
hypothesis
data collection 
interpretation and decision
modification and extension (repeat)
121
Q

three things that could effect internal validity

A

history: events other than the experimental stimulus that occur between the pretest and the post test
maturation: the development or change of a subject over time
test subject interaction

122
Q

experimental mortality

A

the differential loss of participants from comparison groups

when subjects selectively drop out of a study

123
Q

selection bias

A

when subjects are picked, intentionally or not, according to some criterion and not randomly

happens when people volunteer

124
Q

demand characteristics

A

aspects of the research situation that cause participants to guess that the investigators goals and adjust their behavior or opinions accordingly