E1: Introduction Flashcards

Conceptual Definitions, Operationalization, Levels of Analysis, Data Types, Coding (21 cards)

1
Q

Key to minimizing bias and utilizing science?

A

Systematic, specific observations

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2
Q

Empirical Data

A

concrete, observable phenomena that is testable

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3
Q

Normative Data

A

Data based on values or personal philosophies

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4
Q

Case

A

Each observation we make

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5
Q

What must a case be?

A

specific

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6
Q

Level of analysis

A

what does each case represent for each variable?

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7
Q

Individual Level of Analysis

A

1 person gives 1 variable/response

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8
Q

Aggregate level of Analysis

A

group of individuals gives 1 variable/response

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9
Q

Ecological Fallacy

A

When we fail to distinguish levels of analysis.
ie: making observation about individuals based on aggregate data.

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10
Q

Concept

A

vague or unrefined question that will lead into research

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11
Q

Conceptual Definition

A

Broad idea of what you’re studying.

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12
Q

What must a conceptual definition include?

A
  1. measurable
  2. Variation of variables
  3. Specify units of analysis
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13
Q

Operational Definition

A

What specific measure will be used?

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14
Q

Types of operationalization

A
  1. Self-reported
  2. Questions concerning characteristics of what you’re measuring
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15
Q

Nominal Data

A

non-mathematical information. Names or values of variable.

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16
Q

Ordinal Data

A

Logical order to a scale.

17
Q

Interval Data

A

Mathematical data where each observation is a real number.

18
Q

Coding

A

Taking information and assigning it a category

19
Q

Codes

A

number given to a value in the variable

20
Q

Dummy variable

A

Coding for nominal data. 0 = not having, 1 = having

21
Q

Why do we weight certain variables?

A

to correct for over/under representation of populations in a sample.