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3340 Data Analysis > Week 1-6 (Qualitative) > Flashcards

Week 1-6 (Qualitative) Flashcards

1
Q

Ontology

A

Assumptions about the nature of reality

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2
Q

Epistemology

A

Set of assumptions about the relationship between the knower & known

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3
Q

Methodology

A
  • Theoretically informed approach to how we gain knowledge about world
  • Reflect rationale & philosophical assumptions underlying study
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4
Q

Induction

A
  • Begins with concrete details
  • Works toward abstract
    Theory building
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5
Q

Deduction

A
  • Begins with abstract ideas
  • Works toward concrete -details
  • Theory testing & refinement
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6
Q

Exploration

A
  • Uncover phenomena of interest
  • What kinds of things are present here
  • How are things related
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7
Q

Description

A
  • Describing phenomena of interest
  • Precise & trustworthy
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8
Q

Comparison

A
  • Shared features
  • Not shared features
  • Experiential vs consensual knowledges
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9
Q

Descriptive Variable

A
  • Do not emphasize quantity of information
  • Number may be used for data coding
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10
Q

Indirect Observation

A
  • Studying traces of human behavior
  • Analyzing archival data
  • Secondary analysis
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11
Q

Disadvantages of Archival/Secondary Data

A
  • Possible lack of authenticity
  • Lack of representativeness
  • Measurement error
  • Recall bias
  • Can’t confirm interpretations
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12
Q

Advantages of Archival/Secondary Data

A
  • Reducing cost
  • Avoids duplication of primary studies
  • Elaboration of earlier findings
  • Avoids overburdening informants
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13
Q

Direct Observation

A
  • Watch people
  • Record their behaviors
  • Continuous monitoring
  • Spot observation/time sampling
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14
Q

Elicitation

A
  • Interviewing
  • Structured/unstructured
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15
Q

Purposive Sampling

A
  • Maximum variation
  • Snowball - hard to find cases
  • Stop when we hit theoretical saturation
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16
Q

Thematic Analysis

A
  1. Preliminary observations
  2. Identifying themes
  3. Developing a coding scheme
  4. Coding the data
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17
Q

Narrative Analysis

A
  • Story telling - 1st person
  • Signature story
  • Rich data
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18
Q

Credibility

A

Credible/believable from the perspective of participant in research

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19
Q

Transferability

A

Generalized/transferred to other contexts/settings

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20
Q

Dependability

A
  • Would the same results be obtained if we could observe the same thing twice
  • Reliability
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21
Q

Confirmability

A

Results could be confirmed/corroborated by others

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22
Q

Selection Effects

A

Studied sample does not represent population of interest

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23
Q

Reactive Effects

A

Participant behaves different in the study then they would normally

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24
Q

Methodological Effects

A

Changes in results from different data collection procedures

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25
Simultaneity
Analysis can begin once we start sorting out data
26
Metacoding
Sorting chunks of data according to theoretical concepts
27
Content Analysis
- Studies documents & communication artifacts - Quantifies codes & themes
28
Content Analysis Key Concepts
- Systematic observation of data - Codes assigned to indicate meaningful patterns - Determine major common themes in content - Show relationship between major themes
29
Discourse Analysis
- Studies language used to reveal values & beliefs - Examines underlying significance of language within larger social context
30
Discourse Analysis Key Concepts
- A belief, practice, knowledge - How language is used in a specific context
31
Rigour
- Validity of analyses & results in research - No consensus on standards - difficult to demonstrate - Needs justification to not be seen as personal opinion
32
Selection Bias
- Sampling people, time, place - Choice of questions - Data selection
33
Wording Bias
Encouraging particular response
34
Confirmation Bias
- Selecting participants data to confirm a belief - Researcher's personal bias
35
Halo Effect
Something is considered positive due to a single positive attribute
36
Triangulation
Learn more by observing multiple perspectives
37
Population
All members of a particular group of interest
38
Sample
Subset of population that is studied
39
Variable
Anything that has quantity/quality that varies
40
Independent Variable
Manipulated or chosen my researcher
41
Dependent Variable
Outcome variable
42
Control Variable
Held constant to test relationship between dep & indep
43
Nominal
Attributes are merely different from each other
44
Ordinal
Categories you can rank order along some dimension
45
Interval & Ratio
Attributes are rank ordered and have equal distances between adjacent attributes
46
Sensitivity
How effective is tool in detecting what you are looking for
47
High Sensitivity
- Low false negative rate - Correctly identifies people with a condition
48
Specificity
How accurately does the tool give you a negative result when what you are looking for is absent
49
High Specificity
- Low false positive rate - Correctly identifies people without a condition
50
Missing Completely at Random (MCAR)
- No pattern in the missing data on any variables - Best case scenario
51
Missing at Random (MAR)
- There is a pattern in the missing data - Not based on your primary dependent variable
52
Not Missing at Random (NMAR)
- Missing value depends on variable that is missing - Worst case scenario
53
Validity
Are you measuring what you intended to measure
54
Reliability
Are you consistently getting the same response
55
Normality
- Main assumption of many statistical tests - Check before testing research hypothesis - Symmetric bell curved shape - over bar graph
56
Linearity
- Whether a relationship between 2 variables forms a straight line - Can affect type of statistical tests used
57
Frequency Distribution
How many times each value appears for a given variable
58
Quartiles
Observations into 4 equal parts
59
Bar Charts
- Display nominal data on x-axis - Spaces between bars to show distinct categories
60
Histograms
- Bar graph where vertical bars touch - X-axis - continuous variables - Y-axis frequencies/% - Highest bar is the mode
61
Line Graphs
- Show how variable changes over time - X-axis - time - Y-axis - quantity of variable
62
Scatterplots
- Each dot represents indep & dep of a single case - Number of dots is sample size - Dots indicate direction & magnitude of relationship

3340 Data Analysis (3 decks)

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