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2250 Research > Quantitative > Flashcards

Quantitative Flashcards

1
Q

Search Strategies

A
  • Population/situation
  • Intervention/exposure
  • Counter intervention/comparison
  • Outcome
  • Time
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2
Q

Clues

A
  • Compare
  • Test effectiveness
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3
Q

Deductive Reasoning

A
  • Theory testing
  • Start general, get specific
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4
Q

Etic Perspective

A

Outsider

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5
Q

Methodology

A
  • Predominant biomedical focus
  • Positivist/post-positivist perspective
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6
Q

Descriptive

A

Structured observations, survey, or both are used to describe phenomenon, situation, characteristic, group

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7
Q

Exploratory

A
  • Gain new insights
  • Discover new ideas
  • Increase knowledge about phenomenon
  • Used when little known about topic
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8
Q

Casual

A

Experimenting to assess cause and effect

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9
Q

Post-Positivism Assumptions

A
  • Reality can be studied and known
  • Objectivity is the ultimate goal
  • Research bias held in check
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10
Q

Control

A

Measures that the researcher uses to hold conditions of study uniform and avoid basis on the dependent variable or outcome

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11
Q

Goals of Control

A
  • Establish specific sample criteria
  • Decrease error & influence of unwanted extraneous variables
  • Increase probability that study findings accurately represent relationship among IVs & DVs
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12
Q

Internal Validity

A

Changes in the outcome (DV) due to a change in exposure/intervention (IV)

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13
Q

Internal Validity Goal

A

Rule out other explanations

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14
Q

External Validity

A

Findings generalizable to other populations/settings

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15
Q

External Validity Goal

A

Useful beyond participant setting

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16
Q

Threats to Internal Validity

A

Names of potential, common types of extraneous variables

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17
Q

Selection Bias

A
  • Internal validity threat
  • Characteristics of participants raise possible alternate explanation
  • Small sample size
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18
Q

Instrumentation

A
  • Internal validity threat
  • Inter-rater differences
  • Failure of instrument
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19
Q

History

A
  • Internal validity threat
  • Event occurs simultaneously with intervention/situation that affects outcome
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20
Q

Maturation

A
  • Internal validity threat
  • Natural changes during study affect outcome (DV)
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21
Q

Testing

A
  • Internal validity threat
  • Act of being tested once affects outcome of next test
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22
Q

Mortality (Attrition)

A
  • Internal validity threat
  • Differential loss from groups
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23
Q

External Validity Threats

A

Compromise confidence in stating whether the study results are applicable to other groups

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24
Q

Selection Effects

A
  • External validity threat
  • Study sample doesn’t represent population of interest
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25
Reactivity
- External validity threat - Natural reactions to being studied - Hawthorne effect
26
Hawthorne Effect
Participants modify aspect(s) of behaviour in response to awareness of being observed
27
Experimental (RTC) Validity
- Gold standard - Controls most validity threats
28
Quasi-Experimental Validity
Controls some validity threats
29
Non-Experimental Validity
May control some validity threats
30
Descriptive Validity
May control some validity threats
31
Experimental Design
- Intervention that is controlled/delivered - Experimental (intervention) & control group - Random assignment to groups
32
Experimental Design Strengths
- Establish causality/casual direction - Control
33
Experimental Design Limitations
- Difficult to implement - Generalizability (external validity) low - Not ethical for some conditions
34
Quasi-Experimental Design
- Controlled/delivered intervention - Experimental group with/without control group - No random group assignment
35
Quasi-Experimental Strengths
- Establish causality/casual direction - Control (some) - Practical (real world adaptability) - Increased acceptability (people not always willing to be randomized)
36
Quasi-Experimental Limitations
- Cannot make clear cause/effect statements - Generalizability (external validity) low - Not ethical for some conditions
37
Sampling
Process of selecting subjects to represent population
38
Sampling Planning
- Determine sample characteristics - Determine sample size - Feasibility/recruitment strategies - Ethical considerations
39
Probability Sampling
- Equal & independent probability of selection - Rarely used - Need to know all elements/people in population
40
Non-Probability Sampling
- Elements chosen non-randomly - Most common
41
Simple Random Sampling
- Need a sampling framework - Each element has equal & independent probability of selection - Use of random number generator etc
42
Systematic Sampling
- Needs sampling frame - Uses frame's order to locate element - Faster than simple random - May introduce bias (frame arranged to coincide with sampling occurrence)
43
Convenience Sampling
- Non-probability - Use elements available at time & place of study - Doesn't require sampling frame - Can be fast & efficient - May not provide representative sample (atypical of population) - If study doesn't list sample strategy high chance convenience sampling was used
44
Non-Experimental Design
- An effect (outcome/DV) observed in present is linked to potential cause that occurred in past - Account of events as they naturally occur
45
Non-Experimental Strengths
- Fewer participants (known outcome) - Large number of variables
46
Non-Experimental Limitations
- Difficult to find adequate control group - Beware of alternative hypothesis as reason for documented relationship (causality) - Validity threats (recall bias, selection bias/effects)
47
Relationship/Difference Studies
- Non-experimental design - Explore relationship or differences between variables to provide deeper insight into phenomena
48
Sample Size Justification
- Power calculations - Eligibility criteria
49
Type I Error (False Positive)
- Accept study hypothesis - Intervention didn't work
50
Type II Error (False Negative)
- Non-significant result due to too few observations - Intervention not effective/no relationship between IV & DV
51
Power
- Ability to detect difference in effect between groups/subjects - Many studies may be underpowered - Effect size unimportant - Power increases with larger sample size
52
Large Power
- More chance to see if an intervention actually works - 0.8 = 80% chance to see if intervention works
53
Use of Power Analysis
Determine sample size need to minimize risk of type II error (false negative)
54
Effect Size
Measure of the magnitude of effect
55
Reliability
- How stable or consistent is the measurement - How repeatable is the measurement
56
Validity
Is the instrument measuring what it's supposed to measure
57
High Validity
- High appropriateness of test - Difficult to determine - Equals high reliability
58
High Reliability
- Test measures what it is supposed to measure - No warrantee to be highly valid - Test could measure the wrong thing and be invalid
59
Mean
- Average value from data set - Sensitive to extreme scores - Single number summary for mass of data points - Allows for easy comparison between groups
60
Standard Deviation (SD)
- Most common measure of variance - How far values stray from mean
61
Small Standard Deviation
Scores closer to mean
62
Large Standard Deviation
Scores scattered over a wide range round the mean
63
P Value
- Probability that results were due to chance and not based on intervention - Range from 0-1
64
Alpha Level
- Critical probability value determined ahead of time - Usually set to 0.05 or 0.01
65
P-Value Results
Less than alpha value concludes that the difference observed is statistically signifiant
66
P = 0.01
- Statistically significant - Results are not due to chance alone but due to a true effect

2250 Research (7 decks)

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