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HSCI 207 > Measurement and Sampling > Flashcards

Measurement and Sampling Flashcards

1
Q

what is measurement

A

when a researcher measures, he or she takes a concept, idea, or construct and develops a measure (i.e. a technique, a process, a procedure) by which he or she can observe the idea empirically

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2
Q

two types of measurement process

A
  1. conceptualisation
  2. operationalisation
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3
Q

conceptualisation

A
  • refinement of abstract concepts
  • process of thinking through the various meanings of the concept
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4
Q

operationalisation

A
  • development of specific research procedures that will result in empirical observations
  • important in research as they are measurable and allow for replication
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5
Q

Example of conceptualisation and operationalisation definitions for “criminality”

A

conceptual: non-sanctioned acts of violence against other members of society or their property
operational: counting number of criminal arrests, or number of times a person has spent in prison, or asking people whether they have committed crimes

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6
Q

levels of measurement

A

Categorical
- the average it has NO MEANING
- example: average smoker
Continuous
- the average HAS MEANING
- example: average age or blood pressure
Discrete variables
- a variable that can only take on a certain number of values
- categorical variables are considered to be discrete variables

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7
Q

nominal measurement

A

used to categorise data into mutually exclusive categories or groups
e.g., Faculty (Beedie, FASS), eye colour
- categorical level of measurement
- least precise level of measurement

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8
Q

ordinal measurement

A

used to measure variables in a natural order, such as rating or ranking
e.g., socioeconomic status
- categorical level of measurement
- mutually exclusive

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9
Q

interval measurement

A

used to measure variables with equal intervals between values. But these variables have no TRUE zero
e.g., temperature. A temperature of 0 does not mean no temperature
- continuous level of measurement

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10
Q

ratio measurement

A

allows for comparisons and computations such as ratios, percentages, and averages
e.g., height, weight
- continuous level of measurement

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11
Q

Distinguishing levels of measurement example: John is 10 years old and Sam is 20 years old (which is nominal, ordinal, interval, and ratio?)

A

Nominal: john is young and Sam is old
Ordinal: John is younger than Sam
Interval: John is 10 years younger than Sam
Ratio: Sam is twice as old as John

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12
Q

what is reliability?

A

the ability of a measuring instrument to produce consistent results under consistent conditions

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13
Q

Types of reliability

A

Measurement
- test-retest
- reliability across time

Inter-rater
- independent evaluations conducted by different individuals

Parallel forms reliability
- reliability across indicators

Internal consistency
- whether different items on the same test correlate

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14
Q

How to improve reliability

A
  • conceptualization
  • increase level of measurement (use ratio instead of an ordinal variable)
  • multiple indicators
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15
Q

What is validity?

A

the degree of confidence we can place on the inferences we make about people based on their scores from that scale

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16
Q

construct validity

A

how well a test measures the concept it was designed to evaluate

17
Q

face validity

A

a type of measurement validity in which an indicator “makes sense” as a measure of a construct in the judgement of others

degree of effectiveness

18
Q

content validity

A

evaluates how well an instrument (like a test) covers all relevant parts of the construct it aims to measure.

19
Q

what are the two main types of construct validity?

A

convergent: based on the idea that indicators of one construct will act alike or converge

discriminant: based on the idea that indicators of different constructs diverge

20
Q

practice question: validity is best defined as?

A

Seeing if the scale measures what it says it does

21
Q

what is sampling?

A

a process that selects part of a population for observation

sample: a group of individuals chosen to represent a larger population

22
Q

sampling qualitative and quantitative perspective

A

Qualitative
- less focus on representativeness
- focus on relevance
- non-probability samples

Quantitative
- representativeness
- produce generalizations
- probability samples

23
Q

non-probabilistic sampling

A

individuals are selected based on non-random criteria, and not every individual has a chance of being included
- samples selected based on their relevance to research topic rather than their representativeness

issue with non-probabilisitic sampling: generalisability

examples:
- convenience
- voluntary response
- purposive
- snowball
- quota

24
Q

quota sampling

A
  • non-probability sampling
  • interviewers told they need to go out and get a given quota of subjects
    e.g., an interviewer may be told to select 20 male smokers and 20 female smokers so they can interview them about smoking behaviours.
25
Q

purposive sampling

A
  • non-probability sampling
  • Participants selected for reasons linked to the research study
  • Typically difficult-to-reach populations
26
Q

snowball sampling

A
  • non-probability sampling
  • Identify a few key individuals
  • Ask them to distribute
    questionnaire to/recruit others
  • Goal is to capture an already- existing network
27
Q

probabilisitic sampling

A
  • involves randomly selecting a sample, or a part of the population that you want to research
    2 primary criteria: individuals must be randomly selected and must have a non-zero chance of being selected
  • generalizable
  • parameter: true characteristic of the population
  • statistic: information from the sample to estimate a population parameter

examples:
- simple random
- systemaic
- stratified
- cluster

28
Q

strengths of probability sampling

A
  • reliable inferences can be made about the population
  • avoids selection bias and so, representative of the population in a probabilisitic sense
29
Q

limitations of probability sampling

A
  • complex
  • time consuming
  • costly

HSCI 207 (3 decks)

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