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hsci 207 research > week 8 quantitative methods : measurement and sampling > Flashcards

week 8 quantitative methods : measurement and sampling Flashcards

1
Q

what do measurements do?

A

link data to concepts

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2
Q

why is studying measurement important?

A

helps understand the type of data collected, understanding appropriate methods and better research

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3
Q

what is conceptualization?

A
  • understanding abstract concepts
    a definition in abstract, theoretical terms
    “the process of thinking through the various meanings of the concept”
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4
Q

EXAMPLE: “CRIMINALITY”
A study is interested in people’s “criminality”
what is conceptual definition and the operational definition

A

Conceptual definition
* “Non-sanctioned acts of violence against other members of society or their property”
Operational definition
* Counting a person’s number of criminal arrests from official records; OR
* Calculating the amount of time a person has spent in prison; OR
* Asking people whether they have committed crimes

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4
Q

what is operationalization?

A

development of research procedures that will give empirical observations

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5
Q

why are operational definitions important in research?

A
  • force us to think carefully
  • allow replication
  • measureable
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6
Q

example * What is a conceptual definition of ethnicity?
* What is an operationalized definition of ethnicity?

A

conceptual : * Ethnic identity which refers to self-identification within a particular group
* Ethnic origin which refers to classification based on the ethnic group to which the individual’s ancestors belong

operational : Ethnic identity:
* The individual’s self-identification within specific ethnic groups Common survey question:
* “Please write down the term that best describes the ethnic character of your everyday home environment”
Ethnic origin:
* Used in many national surveys (National Population Health Survey, Canadian Community Health Survey).
* Country of birth
* Language

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7
Q

categorical vs continuous variables

A

continouous : you take an average and it has meaning : eg: age or blood pressure - changing
categorical variable : you measure and it has no meaning - average smoker

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8
Q

what are discrete variables?

A
  • variable that can only take on a certain number of values
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9
Q

are categorical variables considered to be discrete variables?

A

yes! - only certain number of values

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10
Q

are the following categorical or continuous
1. age
2. achievement test : pass/fail
3. score on achievement test : 1-100

A
  1. continuous
  2. categorical
  3. continuous
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11
Q

what is the nominal level of measurement ?

A
  • allows researchers to classify characteristics of study pop into categories
  • least precise
  • no math
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12
Q

whats an example of nominal measures

A

blood type - only one category per person
what is your occupation? - choose one

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13
Q

what is the ordinal level of measurement

A
  • categories and categories can be ordered in a meaningful way
  • rank accoringly to charactertistics and object posses
  • mutally exlusive
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14
Q

what is an example of ordinal measurement?

A

SES
- low
-medium
- high
how would u describe your health?
- poor
-good
-excellent
highest level of schooling
- elementary
- high school
-university

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15
Q

what is the interval level of measuremnt?

A
  • ranked in order and actual value between values has some meaning
  • numbers have meaing but no true zero point eg:: tempature
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16
Q

what are ratio levels of measurement?

A

all the other charactertistics and also have an absolute zero point which represents absence
- fixed measuring units

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17
Q

example of ratio levels

A
  • body weight
  • zero kg = no weight
  • person who is 20kg is twice as heavy as 10kg
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18
Q

what is each of the level of measurement for : John is 10 years old and Sam is 20 years old
1. nominal
2. ordinal
3. interval
4. ratio

A
  1. john is young and sam is old
  2. john is younger than sam
    3.john is 10 years younger than sam
  3. sam is twice as old as john
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19
Q

what is reliability

A

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

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20
Q

what is test-retest reliability?

A

reliability across time

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21
Q

what is inter-rater reliability?

A

independent evaluations conducted by different individuals

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22
Q

what is parallel forms of reliability?

A

reliability across indicators (i.e., two versions of the same scale should yield nearly identical results)

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23
Q

what is internal consistency?

A

Whether different items on the same test correlate
Degree to which scales are correlated, made on the assumption that scales should measure a single construct

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24
Q

how to improve reliability?

A
  • Conceptualization
  • Increase level of measurement i.e. use a ratio instead of an ordinal variable
  • Multiple indicators
25
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”
* Is the scale actually measuring what we think it is

26
Q

what is the Construct validity

A

construct: Theoretical concept, theme or idea based on empirical observations. It’s a variable that is not directly measurable. It may consist of multiple dimensions.

27
Q

what are examples of construct validity?

A

Example: social anxiety

Psychological dimension: intense fear and anxiety Physiologic dimension: physical stress response Behavioural dimension: avoidance of social settings

28
Q

what is the construct of validity

A
  • how well a test measures the concept it was designed to evaluate
  • If the measure is valid, do the various indicators operate in a consistent manner?
29
Q

what is Convergent validity:

A

Used for multiple indicators based on the idea that indicators of one construct will act alike or converge.

30
Q

discriminant validity

A

Used for multiple indicators based on the idea that indicators of different constructs diverge.

31
Q

Can a test truly be valid if it is not reliable?

A

No. A test CANNOT be truly valid if it’s unreliable.
* If the measure is not reliable, that means it gives different results every time it is
assessed.
* If it keeps giving different results, it cannot be measuring what you think it is.

32
Q

what is a sample

A

group of indvdiuals chosen to represent a larger population

33
Q

qualitative sampling

A
  • less focus on representativeness
  • focus on relevance of sample to research topic
  • cases that will enhance what researchers learn
  • non- probability samples
34
Q

quantitative samples

A
  • representativeness
    -produce accurate generalizations about larger group
  • probability samples
35
Q

what is non-probability sampling?

A

participants selected based on their relevance to the research topic rather than their representativeness
issue : not generalizable

36
Q

why use non-probability samples?

A
  • less $$
  • Participant engagement can be challenging with stigmatized behaviours
  • Difficult to obtain large sample of rare groups
  • Difficult to capture “hard-to-reach” populations
37
Q

haphazard sampling

A

convience sampling

38
Q

quota sampling

A

Interviewers told they need to go out and get a given “quota” of subjects

39
Q

purposive sampling

A
  • Participants selected for reasons linked to the research study
  • Typically difficult-to-reach populations
40
Q

snowball sampling

A
  • Identify a few key individuals
  • Ask them to distribute
    questionnaire to/recruit others
  • Goal is to capture an already- existing network
41
Q

judgement sampling

A

— Purposeful selection of a “representative” sample

42
Q

Target population: (N)

A

Target population: (N)
The concretely specified large group of many cases from which a researcher draws a sample and to which results from a sample are generalized
The population of interest about which inferences are desired

43
Q

sampling frame

A

list of all avaible sampling units in the target population- phone list, school, drivers licence

44
Q

sampling ratio

A

The ratio of the size of the sample to the size of the target population

45
Q

what is probability sampling

A

A method of sampling that allows inferences to be made about the population based on observations from a sample

46
Q

two criteria of probability sampling

A
  1. random
  2. must have a non-zero chance of being selected
47
Q

parameter def

A

True characteristic of the population

48
Q

statistic def

A

Information from the sample to estimate a population parameter

what you actually observe in the data

49
Q

benefit of probability sampling

A

avoids selection bias
allows generalizability

50
Q

sampling error def

A

the degree that sample deviates from a population

51
Q

what do we mean by random?

A

Random refers to a selection process that gives each element/unit in a population an equal probability of being selected

52
Q

types of probability sampling

A

Simple random sampling
* Systematic sampling
* Cluster sampling

53
Q

SRS simple random sampling pros and cons

A

Advantages
* Simple to conduct
* Disadvantages
* Requires list prior to sampling
* Can be expensive

54
Q

what is systematic sampling?

A
  • a gap or interval between each selection eg: every third
55
Q

systematic sampling interval

A

Tells the researcher how many elements to skip in the sampling frame before you pick one of your sample.
* To calculate you need: 1) the sample size
2) population size
* Example: If a systematic sample of 300 patients were to be carried out in a family practice with a total practice population of 3,000, the sampling interval will be:
* N/n = 3,000/300 = 10

56
Q

what is stratified sampling

A

divided pop into groups that differ in important ways
select random sample within each group
each group is mutally exclusive

57
Q

what is cluster sampling?

A

The entire population is divided into clusters or groups and a random sample of these clusters is selected
* Typically used when the researcher cannot get a complete list of the members of the population they would like to study, but they can get a list of groups

58
Q

stratification vs clustering

A

Stratification
* Divide population into groups (“strata”) that are different from each other
* Randomly sample from each group

Clustering
* Divide population into comparable groups
* Randomly sample some of the groups

59
Q

does a large sample guarantee a representative sample

A

no

hsci 207 research (12 decks)

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