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SOC200 - Conceptualization (Chapters 4+5) Flashcards

1
Q

UNITS OF ANALYSIS

A
  • what/who being studied
  • Identifying can difficult
  • Periodically diff betw who/what we observe to get info we need + who/what generalizing about based on info
  • what characteristics interested in observing
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2
Q

UNITS OF ANALYSIS

A

•info from individuals + pooling info to find patterns
•Individuals unit of observation, but aggregates units of analysis
•Derive characteristic of social groups from those of individual members - Couples, cities, gangs, churches
Social artifacts: Concrete items – books, paintings, buildings/Social interactions – Weddings, friendship

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3
Q

UNITS OF ANALYSIS AND FAULTY REASONING: ECOLOGICAL FALLACY

A

assume observations at broader unit of analysis say something about indivs comprising unit/equally distributed to individuals in that group
Eg: Neighbourhoods with lots Protestants higher suicide rate than lots of Catholics
•couldn’t infer from aggregate level data because you don’t have individual data
•individualistic fallacy: bias, racism, prejudice
•can’t generalize characteristic of one person to whole groups

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4
Q

CONCEPTUALIZATION

A

process of agreement about what concepts mean
•Indicators & Dimensions: Sign of presence/absence of concept
•Definitions: Real, Nominal + Operational
•Conceptual Order
•If common images changes over time, then concept means something else

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5
Q

OPERATIONALIZATION

A

Specifying exact procedures involved in measuring variables represent concepts (the operational definition)
•Operationalization Choices
•Levels of Measurement

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6
Q

MEASUREMENT

A

measures use depend on operational definition + way available data collected
•Accuracy vs. Precision
•Reliability vs. Validity
•Indexes vs. Scales

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7
Q

CONCEPTUALIZATION: Interchangeability of Indicators

A

doesn’t matter if researchers disagree on specific indicators only matters if all indicators used do not behave same way in testing

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8
Q

CONCEPTUALIZATION: Interchangeability of Indicators

A

If some indicators behave differently, then possible they represent diff dimension of the concept/don’t represent that concept at all.
•Example: all indicators measure level of reckless driving, if you dropped, one you can still measure it

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9
Q

CONCEPTUALIZATION: Real Definitions

A

statement of essential nature of some entity
IMPOSSIBLE – mistakes construct for a real entity + produces vague definitions
•only exists in theory

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10
Q

CONCEPTUALIZATION: Nominal Definitions

A

arbitrary definition

based on conventions of how term commonly used everyday conceptual understanding

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11
Q

CONCEPTUALIZATION: Operational Definitions

A

specifies exactly how a concept will be measured

high clarity by limiting definition - how it is measured only means that thing

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12
Q

CONCEPTUALIZATION: Conceptual Order

A

need to refine/expand meaning, their increased understanding of the concept necessitates it
may divide up concept into diff dimensions + subdimensions
•Hermeneutic circle: cyclical process of ever deeper understanding

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13
Q

OPERATIONALIZATION: Range of Variation

A

what distance betw highest + lowest points in data
•Example from HBSC Survey: frequency distribution, maybe decide $100+
oHow much variation do you need?

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14
Q

OPERATIONALIZATION: Degree of Precision

A

degree of precision at the time of data collection will affect what you can and cannot do with the data later
you can always break it down into less precise units
•If you don’t need to know exactly how much they smoke, then put it into categories

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15
Q

Degree of Precision is Important

A

cannot collect more general data then break it down to specific later
•More precise data has more informative models
•General data limits types of models you can use
•If possible more detail, can aggreagate later
•More precise – more measurement validity
•Collapsing data is more vague, and less measurement power

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16
Q

LEVELS OF MEASUREMENT: Nominal Measure

A

Variables with attributes merely categorical, no rank, no numeric value
different from each other and have no order
Yes No

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17
Q

LEVELS OF MEASUREMENT: Ordinal Measure

A

Variables with attributes rank ordered
can’t really pin down range/distance between attributes
Never, Occasionally, Regularly, Frequently, All the time, or 0-10, 11-20, etc.
•Ordinal - percentage

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18
Q

LEVELS OF MEASUREMENT: Interval Measure

A

Variables with attributes can rank + equal distances, no true 0
IQ Score 60,70,80,90,100,110,120,130

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19
Q

LEVELS OF MEASUREMENT: Ratio Measure

A

Interval measure but with a true 0 point
income, kelvin-0,1,2,3,4,5,6,7,8,9,10,11,12,13,
•Always nice to collect data at ratio level
•Certain types of analysis requires certain level of measurement
•Conclusions have to reflect data you have
•Ratio–mean

20
Q

Reliability

A
  • Consistent = reliable
  • We don’t know whether 1 time measurement affected by other factors
  • Replication important
21
Q

Validity

A

-Exists when measures accurately reflect the concept it is intended to measure
•Measure that accurately reflects concept it is intended to measure

22
Q

Reliable, not valid

A

consistent, but don’t hit target

oConcepts require rethinking

23
Q

Valid, not reliable

A

measures scattered, one point, not consistent in measurement

24
Q

Neither valid, nor reliable

A

no point in target, not very well clustered

25
Both valid + reliable
points clustered around target, Consistent and close to concept
26
Methods for Creating Reliable Measures: 1.Test-Retest Method
Make the same measurement more than once
27
Methods for Creating Reliable Measures: Split-Half Method
Good for measuring complex concepts comprised of several indicators (good health, religiosity, prejudice). Statistical method to see how correlated indicators are: Randomly assign half the indicators one set + other indicators as another set If highly positively correlated, measuring same thing + are reliable measures of your concept.
28
Methods for Creating Reliable Measures: Using Established Measures
•Using established measures: previously used + known reliable scales Rely instead on measures that have been proven in previous research
29
Methods for Creating Reliable Measures: Reliability of Research Workers
Ensuring research workers (interviewers, coders, transcribers, translators, analysts) performing as consistently as possible trained workers as coding the same way
30
Methods for Creating Reliable Measures: Reliability of Research Workers
calling subsample of respondents to verify certain info have same piece of info independently coded by more than one worker or plenty of training •Clarity, specificity, training, and practice
31
GUIDES FOR VALID MEASUREMENT: Face Validity
Indicator seems sensible measure of the concept | checking if they attend class
32
GUIDES FOR VALID MEASUREMENT: Criterion-related (predictive)Validity
Based on an external criterion | employee hiring, sats
33
GUIDES FOR VALID MEASUREMENT: Content Validity
How much does measure cover range of meanings in concept? | includes passive receiving on sexual activity
34
GUIDES FOR VALID MEASUREMENT: Construct Validity
How does the variable relate theoretically to other variables? created a measure using marks + associate with hours of study •measure should relate in some logical fashion to other measures
35
COMPOSITE MEASURES: INDEXES
measures based on more than 1 data item | summarizing + rank scores assigned to individual indicators Usually represents a more general dimension
36
INDEXES: Must have face validity
item should measure what you intend it to measure | 13 HBSC items all appear to offer some indication of the person’s health
37
INDEXES: Unidimensionality
Each item only represent 1 dimension of concept | items measuring physical health should not be included with items measuring mental health
38
INDEXES: Decide on Specificity of the Variable
only want to measure certain aspect of health, then only use items representing that aspect If measuring overall health, then include more balanced set of items
39
INDEXES: Look at Amount of Variance each Item Provides
If your item has 100% of people answering the same way, the item will have no variance and will not be a useful indicator for your measure
40
COMPOSITE MEASURES: SCALES
assigns scores to patterns of responses, some items suggest weak degree of the variable + others reflect stronger degrees of the variable Takes advantage of diff in intensity among indicators •more variation
41
Scales and Indexes – Similarities
▫Both ordinal measures of variables ▫Both composite measures of variables •Both rank order units of analysis using specific variables
42
Scales and Indexes – Differences
▫Index-accumulating the scores assigned to individual items •index more cumulative oSimply accumulating scores assigned to individual indicators
43
Scales and Indexes – Differences
▫Scale assigns scores to patterns of responses suggesting diff in intensity among indicators of the variable
44
Conceptualizing/Operationalizing “Good Health”
* Indicators: eats well, smiling, fit * Working definition: no physical/mental conditions that proclude enjoyment of life * Agreed ideas on what concepts mean come from everyday observation * Observations make up conceptions * Agreed upon conceptions make up definition * Going from mental images (conceptions) to indicators and dimensions
45
Ambiguity of Meaning in a Concept
* drawing on everyday meaning of sexual relationship * got off on lewinsky case * paula jones – lawyers pins down with declarative (written) definion * Clinton’s answers were legally correct * Ambuiguity researchers can face with definitions * Can sabotage judicial procedures * Left a loophole for Clinton
46
Construct
theoretical creations based on observations but cannot be observed directly or indirectly •Concepts are constructs derived by mutual agreement from mental images •Concept have no real, true, or objective meanings help us organize, communicate about, and understand things that are real

SOC200 (11 decks)

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