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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
when are larger sample sizes needed
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
5 rules for determining sample size
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
what is a metric
a measure that is quantifiable
27
what will the metric determine
the statistical analyses we can perform
28
4 levels of measurement
nominal interval ordinal ratio
29
what is a nominal measurement
Not a quantity, but rather a discrete quality that something can have something which is purely categorical information eg. religion
30
what is an interval measurement
a true number in the sense that there are equal intervals implied, but no true zero point eg. temperature in degrees
31
what is ordinal measurement
a rank order
32
what is a ratio measurement
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
what is validity
how well the results among the study participants represent true findings among similar individuals outside the study
34
types of validity
face validity content validity criterion validity - concurrent validity - predictive validity construct validity - convergent validity - divergent validity
35
what is face validity
the degree to which the study appears effective in terms of its stated aim eg. measures what it said it would
36
what do measures that lack face validity have the potential to do
alienate research participants
37
what is content validity
Consider what should go into a measure, and what should stay out - define the boundaries
38
whats the difference between face and content validity
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
what is criterion-related validity
it involves checking the performance of your measure against some external criterion
40
types of criterion-related validity
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
what is concurrent validity
does our measure agree with pre-existing gold standard measures
42
what is predictive criterion validity
does our measure agree with theoretically future behaviour?
43
what is construct validity
How well the measures align with the theory
44
types of construct validity
convergent divergent
45
what is convergent construct validity
demonstrating that the measure relates to measures of similar and related theory
46
what is divergent construct validity
demonstrating that the measures does not relate to unrelated constructs
47
what is reliability
the consistency or repeatability of your measurement
48
types of reliability
stability of the measure (test-retest) internal consistency of the measure (split-half, cronbachs alpha) agreement or consistency across raters (inter-rater)
49
main problems with test-retest
memory effect practice effect (practice improves because of practice in test taking)
50
what is test-retest reliability
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
what is split half reliability
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
strengths and limitations to split-half reliability
strength - eliminates memory and practice effects limitations - are the 2 halves equivalent
53
what is inter-item reliability
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
inter-rater/ inter-observer reliability
checking the match between 2 or more raters/judges eg. research investigating the relationship between communication and family functioning
55
calculation of inter-rater reliability for nominal or ordinal scale
the percentage of times difference raters agree
56
calculation of inter-rater reliability for interval or ratio scale
correlation coefficient
57
reliabilities coefficient scores for testretest, internal consistency and rating consistency
test-retest coefficient >.70 internal consistency >.70 but aim for more rating consistency >.90
58
Can a measure be reliable but not valid?
yes you could have a consistent measure that does not actually measure the construct
59
Can a measure be valid but not reliable?
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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