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quantitative research (Definition, characteristics, research methods & analysis) Flashcards

1
Q

what is the difference between qualitative and quantitative data?

A

qualitative;
- words, understanding
- purposive sampling
- social sciences, soft, subjective
- inquiry from the inside
- meaning of behaviours, broad focus
- discovery, gaining knowledge, understanding actions
- practitioner as human instrument to gather data, prescriptive, personal

quantitative;
- numbers, explanation
- statistical sampling
- physical sciences, hard, objective
- inquiry from the outside
- cause and effect relationships
- theory/explanation testing and development
- researcher descriptive, impersonal

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2
Q

what does quantitative research involve?

A

involves a systematic examination of phenomena through testing a hypothesis

development of statistical models to explain observable phenomena

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3
Q

what are the common types of statistics

A

descriptive
comparative
relationship/causal

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4
Q

what is descriptive statistics

A
  • wanting to understand a situation, facts
  • describe your study participants
  • when you want to describe what is going on or what exists

e.g. how many tennis coaches are working in the UK?
what is the most popular sportswear brand amongst uni students in the UK?

what is the average number of goals on a PL football game?

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5
Q

what are comparative statistics?

A

two or more things are compared with the aim of finding something about one or all of them

e.g. is water a better hydration option during long (over 60 mins) sport activities than sports drinks?

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6
Q

what is relationship statistics?

A
  • relationships or the causal associations between variables
  • understand the nature of and relationships between variables
  • most relevant when thinking about interventions

e.g. does plyometric training improve sprint speed in rugby players?

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7
Q

what is the research process?

A

specify -> design -> collect -> visualise -> build -> analyse -> report

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8
Q

what is involved in the research process?

A

specify clearly a question of interest
design a suitable means of gathering data
collect data in unambiguous and organised manner
visualise data in an appropriate form
build a statistical model
analyse data using this model
report data in simple English, the answers and use graphs where appropriate to ease interpretation

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9
Q

what are statistics?

A

science that involves collecting, summarising, analysing and interpreting data

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10
Q

what is a statistic?

A

a single number summarising a variable of interest

mean/sd = descriptive statistics

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11
Q

why do we undertake research using statistics?

A

to test a hypothesis?
are the results real?
do the results matter?

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12
Q

what is data?

A

collection of facts or information

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13
Q

what is a variable?

A

variables of interest:
what is being observed or measured?
a characteristic associated with a group being studied

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14
Q

what is an explanatory or independent variable?

A

what you are manipulating or what you think is associated with outcome

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15
Q

what is the response or dependent variable?

A

outcome variable, what you’re measuring

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16
Q

what are the data types?

A

qualitative (non numerical)
quantitative (numerical)

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17
Q

what are the two types of qualitative data?

A

nominal
ordinal

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18
Q

what is categorical (nominal) data?

A

named categories (non numeric) no order
e.g. favourite running shoe brand, countries

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19
Q

what is ordered categorical (ordinal) data?

A

numbered/named categories
natural order
e.g rate of perceived exertion

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20
Q

what are the quantitative data types?

A

discrete (interval)
continuous (ratio)

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21
Q

what is discrete (interval data)?

A

integer values (whole numbers) does not have to start at zero e.g. time of day

have units

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22
Q

what is continuous (ratio) data?

A

variables can take any value and start at zero e.g. height, BMI, age, crowd size

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23
Q

why does data type matter?

A

the type of variable will dictate what sort of research questions you ask, how you visualise them and statistical analytic method you use.

24
Q

what do hypothesis tests involve?

A

apply them to experimental data and make statistical decisions using these hypotheses.
precise criteria for rejecting the null hypothesis related to the p value and significance p < 0.05

25
what is the null hypothesis?
default position no relationship no difference
26
what is the alternative hypothesis
there is a relationship there is a difference
27
how do you do a hypothesis test?
use statistical methods and experimental data to make a decision using precise criteria
28
what is an example of a null hypothesis (H0)
there will be no difference in the uptake of smart trainers during the COVID pandemic compared to pre pandemic
29
what is the alternative hypothesis (H1 or Ha)
there will be a difference in the uptake of smart trainers during the COVID pandemic compared to pre pandemic
30
what is a population
total set of observations that can be made
31
what is a sample
a selected subgroup of a population - simple random - stratified random - convenience sample
32
what is a parameter?
- a single number that summarises a variable of interest e.g. fastest time on cricket pitch 400m dash Strava segment is 51 seconds - number 1 ranked athlete is Dan Putman
33
what are the questions for measurement of data concepts?
can you measure it? how should you measure it? is there an established definition?
34
how do you measure reliability?
stability (test-retest) - collect data same way on same person with a time difference is it going to be the same? inter-observer consistency - if two different researcher collect data same way same person are they going to get the same data?
35
what are the types of validity?
internal validity: problems due to manipulation or other causes (variables)? e.g. have people stopped smoking due to your intervention or an external one? external validity: generalisability to a wider population? comparability with other literature
36
why do we need to sample?
- human variability - sample to cover this variability? - sample likely to differ from population - confidence in generalisation - want to make claims about general population
37
what are the inferences in quantitative research?
population: - every member of the population has the same chance of being selected in the sample -> take a sample -> random sample -> statistics (might be more than one - mean, sd, t value or f value) using stats make an estimation and inferences about the population
38
what are inferential statistics?
"allows one to draw conclusions or inferences from data. This means coming to conclusions (such as estimates, generalisations, decisions, or predictions) about a population on the basis of data describing a sample."
39
what is a population?
distinct group of individuals, from which a statistical sample is drawn for a study. A population is built up of elementary units which cannot be further decomposed.
40
what is a population total?
sum of all the elements in the sample frame
41
what is a population mean?
average of all elements in a sample frame or population (usually only estimated)
42
what is a sample?
a subset of the population
43
what is a sampling frame?
a list of all the units in the population form which teh sample is selected (telephone directory)
44
what is a representative sample?
a sample that accurately reflects the population
45
what is a probability sample?
random selection procedure, each unit has equal chance of selection
46
what is a non-probability sample?
sample not selected using random selection method
47
why and how to sample?
select to reduce bias select to have enough statistical power to test hypotheses (sample size) select to be logistically feasible select to cover all relevant groups adequately (representative)
48
what is a sampling error?
aka random error the error that occurs when you analyse a sample instead of a population
49
how do we select a sample?
population characteristics must be clearly defined a poorly defined population or its parameters lead to weak sample data
50
what is the error triangle?
sampling variability sampling error non-sampling error - mistakes in data collection unrelated to sampling
51
what is sampling variability?
different samples from the same population do not always produce the same mean and SD e.g. class heights
52
what is a sampling error
the mean of a sample will not be the same as the mean of a population; can be minimised but not eliminated using good selection criteria
53
what is a non-sampling error?
errors not connected with the sampling methods e.g. questions asked in a bad or leading way measurement error errors made in coding or recording data
54
what is a random sample?
- everybody in the population has the same chance of being selected - allocate everybody a number - random number generator
55
what is stratified random sample?
- if we want to select members of the population with specific characteristics we may want to stratify the sample take a sample from specific groups in the population
56
how large should samples be?
- populations with greater variability need a larger sample size - greater precision requires larger sample size but not proportional increase in precision with sample size though - needs to fit budget and resources
57

academic, research and vocational skills (6 decks)

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