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Research Flashcards

1
Q

Internal validity

A

How well a study is done - whether it avoids confounding variables

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2
Q

Factors that influence internal validity -

A 
Baseline effect
Low statistical power
Testing effect
History effect
Instrumentation effect
Stat regression to mean
Maturation effect
Experimental attrition
Diffusion of tx
Contamination
Assignment of groups
Selection bias 
Compensatory rivalry
Compensatory equalization
Ambiguity of direction of causal influence
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3
Q

Construct validity

A

How well can inferences be made from it

Validity of the measurement tool

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4
Q

Factors that influence construct validity

A 
Multiple tx interferance
Order effect
Hawthorne effect
Experimenter effect
Novelty and disruption effect
Pre or post test sensitization
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5
Q

IV vs. DV

A 
IV = the thing that is changed or controlled
DV = depends on the IV (usually a measure)
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6
Q

Ex - Measure mile run times in Jim while drunk - Ind and Dep =

A 
Ind = alcohol
Dep = mile time
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7
Q

Scales of measurement - Nominal

A 
Identity or classification
Yes/No
M/F
Race
Type of arthritis
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8
Q

Scales of measurement - Ordinal

A

Rank without equal distance between items
Scale 0-10
Rank favorite foods
MMT

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9
Q

Scales of measurement - Interval

A

Equal distance between numbers, NO absolute zero

Temp

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10
Q

Scales of measurement - Ratio

A

Equal distance between numbers, YES absolute zero
Weight
Height

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11
Q

Quartiles - Q1, Q2, Q3

A 
Q1 = 25th (below median)
Q2 = 50th (median)
Q3 = 75th (above median)
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12
Q

Variance

A

Variability for the average squared distance that scores deviate from their mean
(sum of squared SD/# of scores - 1)

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13
Q

Standard deviation

A 
Square root of variance 
1 SD = 68%
2 SD = 95%
3 SD = 99% 
Summarizes variability in a set of data
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14
Q

Negatively skewed

A

Mean is less than median and mode

Bigger hump to the R

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15
Q

Positively skewed

A

Mean is greater than median and mode

Bigger hump to the L

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16
Q

Null hypothesis

A

No difference between the control and the experimental groups

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17
Q

Alternative hypothesis (H1)

A

Contradicts the null

Can be non directional (2 tailed) or Unidirectional (1 tailed)

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18
Q

Level of significance is usually set at

A

5%

P < 0.05

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19
Q

Z score is what

A

Standard score

Z of -1.0 means that the raw score is 1 SD below the mean

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20
Q

Z score and p value

A

Inversely related
If the z score is high, indicates that the test statistic is outside 2 SDs - so need to reject null
If the z score is low, it is within 2 SDs so we retain the null

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21
Q

Beta =

A

Probability of type II error

Retain the null (fail to reject it) when it is false

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22
Q

Alpha =

A

Probability of type I error

Reject the null when the null is true

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23
Q

Relationship of Beta and Alpha

A

The smaller you make alpha, the bigger beta is

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24
Q

If p value is less than alpha - what should you do

A

reject the null (there is a stat sig diff)

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25
p value is what
Probability of getting data as extreme as you did by chance alone (if null is true) P value of less than 0.5 means that there was less than 5% chance that the observed result was a fluke
26
If p is less than 0.5 you should
Reject the null (significance)
27
If p is greater than 0.5 you should
Retain the null (no significance)
28
Power =
the probability of rejecting a false null | Power = 1 minus beta
29
Rather than z scores, in health research we are more likely to see what
t test which is sample variance instead of population variance
30
T distribution
Like normal distribution, but greater variability in the tails df = n minus 1 If the t value exceeds the critical t value, then you need to reject the null (p and t are inversely related)
31
Reliability can be quantified with
``` T test ANOVA Pearson r R (true score variance/total variance) ICC SEM - this is the most useful for clinicians ```
32
Reliability
Precision Consistency Repeatable
33
Confidence interval - ex 95% CI means
95% of the time your measure will fall in this range
34
Validity
Accuracy | Measure what it says it measures
35
Criterion validity
How does it compare to the gold standard
36
Construct validity
How well can inferences be made from it | Validity of the measurement tool
37
Internal vs. External validity
Internal - how well study was done | External - how well it can be generalized to other people and other situations
38
Case control study
Select a control group that represents base population and then select a case group that has disease Retrospectively look back and see differences in exposure
39
Cross sectional study
Sample a random group and look for disease and exposure status right then and there (like random survey) Collect data on exposure and disease at a single point in time
40
Prevalence
Proportion of population with a disease at a specific point in time - existing cases
41
Incidence
Number of new cases over a specific period
42
Relative risk ratio
Risk of an outcome in exposed group/risk of an outcome in a non exposed group If RR greater than 1, exposure is a risk factor
43
Odds ratio
Odds of exposure for cases/Odds of exposure for control
44
Sensitivity
SnOUT High sensitivity = can rule it out True Positives How well a test detects those with disease
45
Specificity
SpIN High specificity = rule in True negatives How well a test detects those without disease
46
Positive predictive value
Proportion of people with pos test who actually have disease | TRUE POSITIVE
47
Negative predictive value
Proportion of people with neg test who do not have disease | TRUE NEGATIVE
48
Correlation (r) =
Describes the relationship between 2 levels of an IV Does NOT indicate causation r = -1.0 to 1.0 0 = no correlation - means one IV increases while other decreases + means both increase or both decrease
49
Pearson r =
Correlation coefficient | Test to see if r is large enough that it is unlikely to have occurred by chance
50
Regression
Used to explain changes in DV Uses the line of best fit Predicts the Y (DV) from X (IV)
51
Regression (R^2)
Coefficient of determination Portion of total variance on measure that can be explained by variance in another measure If r = 0.50, r^2 = 0.25 SO.... we can say that 25% of the variance in Y is accounted for by X
52
T test is used to
Compare 2+ levels of 1 IV | Ex = IV gender (M and F) DV (BP)
53
Ind vs. Dep t test
``` Ind = variables don't depend on each other (M and F) Dep = they do (pre and post) ```
54
one tailed vs two tailed t test
``` Two = has no direction in hypothesis One = has direction ```
55
ANOVA is used to compare
2+ levels/means | Uses an F statistic
56
One way ANOVA
2+ levels of 1 IV on 1 DV (so like independent t test) If F stat is greater than critical value = significant diff
57
Repeated measures ANOVA
Used when subjects are tested more than once | Extension of dependent t test
58
Two way ANOVA
Compares 2+ levels of 2 IV on 1 DV Factorial = both IVs are ind Mixed model = one IV is ind, other is dep Repeated measures = both IVs are dep
59
Chi square =
Analyzes freq of responses that are nominal "ranked" - will be ordinal or nominal data Non parametric test because not normal distribution
60
ANCOVA is used when
1+ IVs 1 covariate 1 DV
61
Discriminant analysis
``` 1 IV (2+ levels) 2+ DVs ```
62
MANOVA
2+ IVs 2+ DVs Tests for patterns (vs. ANOVA is testing for effects on an ind variable)
63
MANCOVA
1+ IVs 1+ covariate 2+ DVs
64
Levels of evidence
``` Systematic reviews and Meta analyses RCTs Cohort studies Case control studies Cross sectional studies Case series Case reports Ideas, opinions ```

Reminders! (22 decks)

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