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Reminders! > Research > Flashcards

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
Q

p value is what

A

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

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26
Q

If p is less than 0.5 you should

A

Reject the null (significance)

27
Q

If p is greater than 0.5 you should

A

Retain the null (no significance)

28
Q

Power =

A

the probability of rejecting a false null

Power = 1 minus beta

29
Q

Rather than z scores, in health research we are more likely to see what

A

t test which is sample variance instead of population variance

30
Q

T distribution

A

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
Q

Reliability can be quantified with

A 
T test
ANOVA
Pearson r
R (true score variance/total variance) 
ICC
SEM - this is the most useful for clinicians
32
Q

Reliability

A

Precision
Consistency
Repeatable

33
Q

Confidence interval - ex 95% CI means

A

95% of the time your measure will fall in this range

34
Q

Validity

A

Accuracy

Measure what it says it measures

35
Q

Criterion validity

A

How does it compare to the gold standard

36
Q

Construct validity

A

How well can inferences be made from it

Validity of the measurement tool

37
Q

Internal vs. External validity

A

Internal - how well study was done

External - how well it can be generalized to other people and other situations

38
Q

Case control study

A

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
Q

Cross sectional study

A

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
Q

Prevalence

A

Proportion of population with a disease at a specific point in time - existing cases

41
Q

Incidence

A

Number of new cases over a specific period

42
Q

Relative risk ratio

A

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
Q

Odds ratio

A

Odds of exposure for cases/Odds of exposure for control

44
Q

Sensitivity

A

SnOUT
High sensitivity = can rule it out
True Positives
How well a test detects those with disease

45
Q

Specificity

A

SpIN
High specificity = rule in
True negatives
How well a test detects those without disease

46
Q

Positive predictive value

A

Proportion of people with pos test who actually have disease

TRUE POSITIVE

47
Q

Negative predictive value

A

Proportion of people with neg test who do not have disease

TRUE NEGATIVE

48
Q

Correlation (r) =

A

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
Q

Pearson r =

A

Correlation coefficient

Test to see if r is large enough that it is unlikely to have occurred by chance

50
Q

Regression

A

Used to explain changes in DV
Uses the line of best fit
Predicts the Y (DV) from X (IV)

51
Q

Regression (R^2)

A

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
Q

T test is used to

A

Compare 2+ levels of 1 IV

Ex = IV gender (M and F) DV (BP)

53
Q

Ind vs. Dep t test

A 
Ind = variables don't depend on each other (M and F)
Dep = they do (pre and post)
54
Q

one tailed vs two tailed t test

A 
Two = has no direction in hypothesis
One = has direction
55
Q

ANOVA is used to compare

A

2+ levels/means

Uses an F statistic

56
Q

One way ANOVA

A

2+ levels of 1 IV on 1 DV (so like independent t test)

If F stat is greater than critical value = significant diff

57
Q

Repeated measures ANOVA

A

Used when subjects are tested more than once

Extension of dependent t test

58
Q

Two way ANOVA

A

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
Q

Chi square =

A

Analyzes freq of responses that are nominal
“ranked” - will be ordinal or nominal data
Non parametric test because not normal distribution

60
Q

ANCOVA is used when

A

1+ IVs
1 covariate
1 DV

61
Q

Discriminant analysis

A 
1 IV (2+ levels) 
2+ DVs
62
Q

MANOVA

A

2+ IVs
2+ DVs

Tests for patterns (vs. ANOVA is testing for effects on an ind variable)

63
Q

MANCOVA

A

1+ IVs
1+ covariate
2+ DVs

64
Q

Levels of evidence

A 
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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