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pyb210: research design and data analysis > week 2 > Flashcards

week 2 Flashcards

measuring variables, sampling, validity and reliability

1
Q

what are generalisable results

A

results that are deemed to reflect the true state of affairs in the population of interest

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2
Q

how do you claim generalisability?

A

your sample needs to be as representative of the population as you can make it

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3
Q

types of sampling procedures

A

non-probability sampling
probability sampling

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4
Q

types of non-probability sampling

A

convenience
snowball
purposive

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5
Q

types of probability sampling

A

simple random sample
systematic random sample
stratified random sampling
multi-stage cluster sampling

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6
Q

how do you ensure your sample is representative of the population

A

a sample will be representative if all members of the population have an equal chance of being selected in the sample

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7
Q

what does probability sampling allow

A

the researchers to calculate the relationship between the sample and the population

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8
Q

what is a simple random sample

A

each member has an equal and independent chance of being selected

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9
Q

how do you do simple random sample

A

define the population
list all members
assign numbers
eg.
- use a table of random numbers to select
- use a lottery method
- use a computer program to randomly select

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10
Q

what is systematic random sampling

A

selects a random starting point from the population, then a sample is taken from regular fixed intervals of the population depending on its size
eg. to select a sample of 1000 people from a list of 10,000, randomly select the first person and then select every 10th person from the list

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11
Q

what is stratified sampling

A

researchers divide subjects into subgroups called strata based on characteristics that they share
eg. age, ethnicity, location

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12
Q

why do we use stratified sampling

A

can reduce sampling error by ensuring ratios reflect subpopulations
to ensure that small subpopulations are included in the sample

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13
Q

what is multi-stage cluster sampling

A

you draw a sample from a population using smaller and smaller groups at each stage

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14
Q

what is non-probability sampling

A

not every member of the population has an equal chance of being part of the sample

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14
Q

difference between stratified sampling and multi-stage cluster sampling

A

it is not the same as stratified sampling as each cluster doesnt need to be sampled

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15
Q

why would we use non-probability sampling

A

because not every member of the population has an equal chance of being part of samples
eg. homeless people

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16
Q

what is convenience sampling

A

choosing people who are easy for the researcher to reach and get in touch with
eg. students enrolled in a particular course

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17
Q

pros and cons to convenience sampling

A

pros
- easy
- cheap
cons
- no control over representativeness
- bias

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18
Q

what is snowball sampling

A

Involves collecting
data with members of
the population that
can be located and
then asks those
members to provide
information/contacts
for other members of
the population

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19
Q

why is it used

A

to study hard to reach populations
eg. homeless youth
QUT students who use the library at night

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20
Q

what is quota sampling

A

a non-probability sampling method in which researchers create a convenience sample involving individuals that represent a population. Researchers choose these individuals according to specific traits or qualities.

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21
Q

what is purposive/judgment sampling

A

Selecting a sample based on knowledge of the population, its elements, and the purpose of the study

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22
Q

why do we use purposive/judgement sampling

A

it is often used to:
- select cases that might be especially informative
- select cases in a difficult to reach population
select cases for in-depth investigation

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23
Q

for quantitative research which type of sampling should be used

A

probability sampling

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24
Q

when are larger sample sizes needed

A

when the sample is heterogeneous
when you want to breakdown the samples into subcategories
if you want to obtain a narrow or more precise confidence interval
when you expect a small effect or weak relationship
for some statistical techniques

25
Q

5 rules for determining sample size

A
  1. if less than 100 use entire population
  2. larger sample sizes make it easier to detect an effect or relationship in the population
  3. compare to other research studies in area by doing a lit review
  4. use a power table for a rough estimate
  5. use a sample size calculator eg. g power
26
Q

what is a metric

A

a measure that is quantifiable

27
Q

what will the metric determine

A

the statistical analyses we can perform

28
Q

4 levels of measurement

A

nominal
interval
ordinal
ratio

29
Q

what is a nominal measurement

A

Not a quantity, but rather
a discrete quality that
something can have
something which is purely categorical information
eg. religion

30
Q

what is an interval measurement

A

a true number in the sense that there are equal intervals implied, but no true zero point
eg. temperature in degrees

31
Q

what is ordinal measurement

A

a rank order

32
Q

what is a ratio measurement

A

a true number. the distinguishing feature of a ratio scale variable is that it has a meaningful zero point, that participants could use to indicate the
quantity is completely absent

33
Q

what is validity

A

how well the results among the study participants represent true findings among similar individuals outside the study

34
Q

types of validity

A

face validity
content validity
criterion validity
- concurrent validity
- predictive validity
construct validity
- convergent validity
- divergent validity

35
Q

what is face validity

A

the degree to which the study appears effective in terms of its stated aim eg. measures what it said it would

36
Q

what do measures that lack face validity have the potential to do

A

alienate research participants

37
Q

what is content validity

A

Consider what should go into a measure, and what should stay out
- define the boundaries

38
Q

whats the difference between face and content validity

A

Face validity is an informal review of a questionnaire by non-experts, who assess its clarity, comprehensibility, and appropriateness for the target group
content validity involves a formal assessment to determine the appropriateness of content

39
Q

what is criterion-related validity

A

it involves checking the performance of your measure against some external criterion

40
Q

types of criterion-related validity

A

concurrent: does it relate to a known criterion
eg. an alternative gold standard measure of the same construct
predictive: does the measure predict/relate to some criterion that you would expect it to predict

41
Q

what is concurrent validity

A

does our measure agree with pre-existing gold standard measures

42
Q

what is predictive criterion validity

A

does our measure agree with theoretically future behaviour?

43
Q

what is construct validity

A

How well the measures align with the theory

44
Q

types of construct validity

A

convergent
divergent

45
Q

what is convergent construct validity

A

demonstrating that the measure relates to measures of similar and related theory

46
Q

what is divergent construct validity

A

demonstrating that the measures does not relate to unrelated constructs

47
Q

what is reliability

A

the consistency or repeatability of your measurement

48
Q

types of reliability

A

stability of the measure (test-retest)
internal consistency of the measure (split-half, cronbachs alpha)
agreement or consistency across raters (inter-rater)

49
Q

main problems with test-retest

A

memory effect
practice effect (practice improves because of practice in test taking)

50
Q

what is test-retest reliability

A

you administer the measure at one point in time. you then give the same measure to the same participants at a later point in time. you correlate the scores on the two measures

51
Q

what is split half reliability

A

administer questionnaires and split the measures into 2 halves. correlate the scores on the 2 halves of the measure. higher correlation means greater reliability

52
Q

strengths and limitations to split-half reliability

A

strength
- eliminates memory and practice effects
limitations
- are the 2 halves equivalent

53
Q

what is inter-item reliability

A

assesses the internal consistency of your measure
eg. tells you how well the questions in your measure appear to reflect the same underlying construct

54
Q

inter-rater/ inter-observer reliability

A

checking the match between 2 or more raters/judges
eg. research investigating the relationship between communication and family functioning

55
Q

calculation of inter-rater reliability for nominal or ordinal scale

A

the percentage of times difference raters agree

56
Q

calculation of inter-rater reliability for interval or ratio scale

A

correlation coefficient

57
Q

reliabilities coefficient scores for testretest, internal consistency and rating consistency

A

test-retest coefficient >.70
internal consistency >.70 but aim for more
rating consistency >.90

58
Q

Can a measure be reliable but not valid?

A

yes
you could have a consistent measure that does not actually measure the construct

59
Q

Can a measure be valid but not reliable?

A

yes
eg. something
that is difficult to implement (e.g., Skin fold tests –require technical skill) – may be unreliable across multiple administrators

pyb210: research design and data analysis (2 decks)

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