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SOC EXAMFINAL > Week 13 > Flashcards

Week 13 Flashcards

1
Q

what are the types of quantitative analysis, univariate

A

one variable

  1. Frequency Distributions
    - percentage
  2. Measures of Central Tendency
    - Mode, median, mean
  3. Measures of Variations
    - range, percentiles-standard deviation (the average difference among individuals)
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2
Q

what are the types of quantitative analysis, bivariate

A

(1) The Scattergram
(2) Bivariate Crosstabs
(3) Multivariate Crosstabs (three or more variables)

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3
Q

explain scattergram

A

A diagram to display the statistical relationship between two variables based on plotting each case’s values for both of the variables

  • A linear relationship (upward or downward), Positive or negative
  • A curvilinear relationship (up and down or vice versa)
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4
Q

explain bivariate crosstabs

A

(two variables)

Measure of Association-strength of association between two variables

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5
Q

explain Multivariate Crosstabs

A

(three or more variables)
Statistical control -adding one or more control variable into the statistical model-building casual model (e.g., antecedent, intervening)

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6
Q

explain quantitative statistical significance

A

Inferential statistics-Chi square test-Apply to univariate, bi-variate & multi-variate analysis
Generalizing the findings from the sample to the target population (external validity)

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7
Q

what does qualitative analysis involve?

A

The Process of Knowledge Production

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8
Q

what is the process of knowledge production?

A
  • By developing themes, new concepts or abstract theories rather than testing theories (quantitative analysis)
  • Conceptualization & Theory Building (after data collection), grounded in data (in comparison with Quantitative R)
  • ideas supported by qualitative evidence
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9
Q

what is the process of knowledge production?

A
  • By developing themes, new concepts or abstract theories rather than testing theories (quantitative analysis)
  • Conceptualization & Theory Building (after data collection), grounded in data (in comparison with Quantitative R)
  • ideas supported by qualitative evidence
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10
Q

what are the elements of knowledge production?

A
  • Building materials: themes, codes, categories, classification
  • Building methods: coding, memo writing, analytic induction, theory/concept building
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11
Q

what is a code?

A

-a word or short phrase that symbolically assigns a summative, salient, essence-capturing and/or evocative attribute for a portion of language-based or visual data

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12
Q

what is the purpose of coding?

A

-bring themes to the surface from deep inside the data

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13
Q

Creating codes ?

A

-making sense of the data -assigning raw data with specific meaning

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14
Q

Organizing codes

A

-creating different thematic drawers

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15
Q

Types of Coding?

A
  1. Open Coding
  2. Axial Coding
  3. Focused Coding
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16
Q

explain Open Coding

A

(Line-by-line coding) – the initial rough stage

Read through your data to identify preliminary themes

17
Q

explain axial coding

A

linking codes
Understand the relationship between codes and derive new categories from codes
Compare codes to find consistencies and differences

18
Q

explain focused coding

A

Re-read your data to better organize these themes (group similar themes into broader ones, drop some that no longer seem important)

19
Q

explain analytic memo writing?

A
  • Record of thoughts and ideas about the coding process
  • Creates the link between raw data or evidence and formal theorizing and thesis statement creation
  • “Theoretical Reflection”
20
Q

4 Strategies of Conceptual/Theoretical Development

A

Narrative approach – story telling
Ideal types
Successive approximation
Illustrative method

21
Q

explain the Narrative Approach?

A

Rely on codes to build the narrative
Concrete details presented in chronological order as if the product of a natural sequence of events
Data speak for themselves (grounded theory)
Valuing study participant’s voice and experiences

22
Q

explain Ideal Types – Theoretical Concept

A

,

23
Q

explain Ideal Types – Theoretical Concept

A
  • Models or abstractions of social relations or processes (Weber)
  • Help us to explain the social world (possible patterns)
  • Compare ideal forms suggested by theory to empirical observations
  • Note the ideal-reality difference
  • Used for comparison, because no reality ever fits an ideal type.
24
Q

what are two aspects of ideal types?

A
  1. Contrast contexts: Researchers with an interpretive approach use ideal types to interpret data in a way that is sensitive to the context and cultural meanings of members. The ideal type brings out the specifics of each case and can be used to emphasize the impact of the context. Thus, cases that dramatically contrast or that have distinctive features are often chosen.
  2. Analogies: Used to organize qualitative data and facilitate logical comparisons. They make it easier to compare social processes across settings or cases. Do not provide a test of explanation but, instead, they guide the conceptual reconstruction of vast amounts of information and details into a systematic and sensible format.
25
Q

Successive Approximation – Theoretical Refinement

A
  • Move back and forth between theory and data, until theory (or generalization) is maturely developed-a non-linear research process
  • Abstract concepts are rooted in concrete evidence and reflect the context
  • As the analysis progresses, the researcher refines generalizations and linkages to reflect the evidence better
  • Additional data is gathered during the theory building process and, over time, the evidence and theory begin to shape one another
26
Q

Illustrative Method

A
  • Find empirical examples in the data (e.g., specific interview quotes) to support a theory
  • Theory provides empty boxes, and the researcher sees if the evidence can fill them
  • The evidence is used to confirm or reject theories
27
Q

Analytic Induction

A
  • Look at an event and develop a thesis statement of what happened (the process of theory development)
  • Look at another similar event and see if it fits the thesis statement, If it doesn’t, revise the thesis statement
  • Look for exceptions to the thesis statement
  • When found, revise hypothesis to fit all examples encountered
  • Eventually, will develop a statement that accounts for all observed cases (context specific)
28
Q

similarities in quantitative and qualitative analysis?

A

Make inferences from data
Faithful to the data
Based on comparison

29
Q

differences of qualitative and quantitative analysis?

A

Less standardized
Analysis begins earlier (often while in the field)
Theory development, rather than testing
More abstract
Conceptualization Process (after data collection)
Results of early analysis guides further data collection
Because qualitative data takes the form of words rather than number, the data is less precise, context-specific, and can have multiple meanings and interpretations

SOC EXAMFINAL (8 decks)

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