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Beginning research > Quantitative research > Flashcards

Quantitative research Flashcards

(54 cards)

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1
Q

What’s quantitative research

A

it is used to quantify the problem by way of generating numerical data to explain observable phenomena

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2
Q

what does quantitative research do/purpose

A

uses measure able data to formulate facts and uncover patterns in research

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3
Q

what’s a research design

A

a structured plan or blueprint which outlines how a study will be conducted. - details methods & procedures

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4
Q

why is a research design important

A
  • Consider the purpose of the study
  • allows hypothesis to be tested
  • ethical considerations
  • reduces the chance of error
  • understand the conclusion which can be drawn from the study
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5
Q

purpose of the hierarchy of scientific evidence

A

shows how strong or weak evidence is

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6
Q

common research designs

A

observational = participants are observed
experimental = effect of an intervention is assessed

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7
Q

observational study - flow chart

A

no intervention -> group comparison ->
Yes (cohort study, case control) or No (case series, case study)

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8
Q

what is an observational study

A
  • observational (non-experimental) studies
  • find a naturally occurring experiment
  • comparison of 2 or more populations that yields information about the relationship between 2 or more variables
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9
Q

why do we do observational studies

A
  • gain real world insights
  • ethical considerations
  • can provide valuable insights in chronic health conditions
  • large sample size
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10
Q

Experimental study - flow diagram

A

intervention -> experimental -> random allocation ->
Yes (randomised control trial) or No ( controlled study)

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11
Q

what is experimental research design

A
  • most common type of study
  • intervene by providing an intervention
  • manipulate IV to see what effect it has on DV
  • primary purpose is draw a conclusion about a particular procedure treatment
    involved pre and post intervention measurements
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12
Q

independent variable IV
dependent variable DV

A

IV = change, control group
DV = measure, outcome

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13
Q

Experimental designs

A
  • Parallel (stay in groups)
  • crossover (participants receive all conditions)
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14
Q

randomised control trial

A

all participants should have similar characteristics (e.g. age, sex)

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15
Q

What’s blinding

A

method used to prevent bias by keeping certain information hidden from participants, researchers, or both

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16
Q

single blind

A

participants don’t know what treatment they are receiving (active treatment, or placebo) but researchers do.
- reduces bias in participant response and behaviour

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17
Q

double blind

A

Both participants and researches do not know who is receiving the active treatment and placebo

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18
Q

control group

A
  • don’t receive any treatment
  • compare effects of a given intervention with baseline measures
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19
Q

placebo group

A
  • equivalent or inert treatment
  • shows any observed effects are caused by treatment and not the procedure of administering the treatment
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20
Q

what’s bias

A

a systematic error or tendency that distorts findings, interpretations, or conclusions

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21
Q

Types of bias

A

Cognitive, confirmation, design, selection, data collection/messurement, analysis, survivorship, publication

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22
Q

cognitive bias

Study These Flashcards
A
  • ways of thinking that predispose one to favour of a certain viewpoint
23
Q

what can bias effect

Study These Flashcards
A
  • can occur at each stage of the research process
  • can impact validity and reliability of study findings, and misinterpretation of data
24
Q

confirmation bias

Study These Flashcards
A

interpret information in a way that confirms one’s preconceptions, while ignoring information that doesn’t support preconceptions

25
selection bias
both the process of recruiting participants and study inclusion
26
survivorship bias
focus on the individuals that have survived a certain process, intervention, while ignoring those who didn’t
27
publication bias
“scientific studies are more likely to be published if reporting statistically significant findings” - positive results are more interesting
28
scale of measurement
- Nominal scale - Ordinal scale - Interval scale - Ratio scale
29
Nominal scale
Simple, variables have no numerical value, have categories e.g. gender, race, type of sport
30
Ordinal scale
Variables are in categories with an underlying order to their value, rank-order from high to low, intervals may not be equal e.g. pain ratings, RPE
31
interval scale
ordered categories and the difference bbetween two values is meaningful, no absolute 0 e.g. temperature, time
32
ration scale
ordered categories, equal intervals and a true 0 e.g. age, body weight, blood pressure
33
parametric distribution of data
normal distribution
34
non parametric of data distribution
non-normal distribution
35
assessment of normality
- need to establish what we consider as normal - achieved by assessing difference between mean and median - statistical tests: Shapiro-Wilk & Kolmogorov-Smirnov
36
Measures of Central Tendency and when to use them
Mean - average (normally) Mode - most common (not often used) Median - middle (non normally)
37
Variance
- how scattered around the average value is - small v = values on average are closer to the mean - large v = measured values vary widely from the mean
38
measures of data spread
- standard deviation - range - interquartile range
39
standard deviation
Small SD = numbers close to average Large SD = numbers are more spread out
40
choosing a measure of spread
standard deviation- normally distributed interquartile range - not normally
41
Null hypothesis
statement of no difference/ no relationship, tested using statistics
42
Null hypothesis
statement of no difference/ no relationship, tested using statistics
43
inferential statistics definition
used to analyse data that involved using different statistical tests, which allows researchers to make conclusions or inferences about a given population, based on data from a sample
44
1. descriptive statistics 2. inferential statistics 3. correlational statistics
1. provides some valuable insight into our data 2. allows u to make predictions (inferences) from that data 3. allows us to tell whether a relationship exists between two variables
45
Type l and Type ll error
Type 1 = false positive Type 2 = false negative
46
P values
P = probability of error - low probability (better), can be more confident in finding - cut off is 0.05, if P value is less then <0.05 the result is significant (reject null hypothesis)
47
Testing differences: T test
allows u to compare the means of 2 groups to determine whether there is a genuine difference between group or a product of chance
48
paired sample T-test
used to test the whether two samples means, collected from the same group on two separate occasions, are significantly different from each other
49
unpaired sample T-test
allows us to test whether sample means from different populations are significantly different from each other
50
What test? same group separate groups
same group = paired sample T-test or Wilcoxon test separate groups = unpaired sample T-test or Mann-Whitney test
51
Intention to treat ITT vs Per protocol analysis PPA
ITT - all participants that where included in the study (no matter if they didn’t adhere to the protocol) PPA - only included participants who fully complied with the study protocol
52
R value
expressed as a correlation coefficient - a number between +1 (positive relationship) and -1 (negative relationship)
53
Pearsons vs Spearman Rank
Parametric = Pearsons correlation coefficient Non-parametric = Spearman Rank Both provides R value and a P value
54
correlation analysis
shows us how 2 variables may be related but correlation does not tell us causality

Beginning research (3 decks)

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