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KIN 2032 > Midterm > Flashcards

Midterm Flashcards

1
Q

Belmont Report Guidelines

A
  1. Beneficence - maximize benefits, minimize harm
  2. Respect for persons - autonomy, informed consent
  3. Justice - fair distribution of benefits and burdens
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2
Q

Primary literature

A
  • Original work that enhances or introduces knowledge
  • Includes research results, case studies, descriptive and evaluative studies
  • e.g. randomized controlled trial
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3
Q

Secondary literature

A
  • Summarize, analyze, draw conclusoin from previous work
  • e.g. reviews, meta-analysis
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4
Q

Evaluating Resources

acronym

A

Currency: published, updated, revised
Relevance: info, details, audience
Authority: credentials, peer-reviews
Accuracy: references, match others
Purpose: stated, objective or bias

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5
Q

Situational variable

A
  • Describe characteristics of a situation/environment
  • Categorical
  • e.g. temperature in gym
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6
Q

Response variable

A
  • Responses/behaviours
  • Dependent variable
  • e.g. RT
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7
Q

Participant/subject variable

A
  • Individual differences, characteristics
  • Numerical
  • Independent variabke
  • e.g. sex
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8
Q

3 Fundamental features of science

A
  1. Systematic empiricism
  2. Empirical questions
  3. Public knowledge
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9
Q

Beliefs/activities that imply science but lack 1+ of the 3 features of science

A

Pseudoscience

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10
Q

3 goals of science

A
  1. Describe - observational
  2. Predict - systematic relationship between variable
  3. Explain - mechanisms + causal rltnsp
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11
Q

Basic vs applied research

A

Basic: global understanding
Applied: address practical problems

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12
Q

PICOT

A

Patient pop. of interest
Intervention of interest
Comparison intervention/group
Outcome
Time

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13
Q

Sampling methods

simple random, systematic, cnvenience, cluster

A

Simple random: every member of pop has equal chance of being selected
Systematic: every nth participant
Convencience: nearby and willing
CLuster: divide pop into blocks, then randomly select blocks of participants

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14
Q

Stratified sampling

A

Divide pop based on characteristics, then sample is taken from strata using random, systematic, or convenienc e smapling

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15
Q

Variables other than the DV

A

Extraneous variables

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16
Q

Variable that systematically vary with DV

A

Confound variable
Provide alternative explanation

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17
Q

Difference between experimental and non-experimental rsrch

A

Manipulation of IV only in experimental
Can’t draw causal conclusions with non-exper

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18
Q

Measures of dispersion

range, standard dev, variance

A

Range: difference between highest and lowest score (outliers can mislead)

Standard deviation: avg distance between scores and mean; square root of variance
* √((⅀(x-m)²)/n)

Variance: mean of squared diffferenced (SD^2)
* calculate the variance by taking the difference between each point and the mean. Then square and average the results.

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19
Q

Descriptive stats

Examples and purpose

A

Describe/summarize data; no causal conclusions
e.g. %, central tendency, dispersion, correlation coefficients

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20
Q

Inferential stats

A

draw conclusions, determone statistical sig.
Type 1 and 2 errors

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21
Q

Type I vs Type II errors

A

Type I - false psoitive
Type II - false negative

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22
Q

What do the results of a study tell us

A
  • can’t conclude/prove based off a single study
  • Either support, refute, or modify theory
  • scientific evidence, not proof
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23
Q

Continuous vs categorical levels of measurement

A

Cont - Interval and ratio
Cat - nominal and ordinal

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24
Q

Level of measurement with a meaningful zero

A

Ratio

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25
Q

Math with levels of measurement

A

Interval: add and subtract
Ratio: +, -, /, x

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26
Q

Summarizing levels of measurement

A

Nominal: mode
Ordinal: median and mode
Interval + Ratio: all three

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27
Q

Sex is an example of what level of measurement

A

Nominal

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28
Q

Place in a race is an example of what level of measurement

A

Ordinal

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29
Q

Temp in celsius is an example of what level of measurement

A

Interval

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30
Q

Temp in Kelvin is an example of what level of measurement

A

Ratio

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31
Q

When do ordinal and interval data overlap

A
  • aggregating multiple items
  • underlying construct is continuous
  • Measurement instrument is reliable
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32
Q

Why collect as continuous data and then put into categoires?

A

Otherwise can’t get an average
Presents fewer analytic choices

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33
Q

sum of all scores divided by n

A

Mean

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34
Q

Median

A

50th percentile/middle score
First step: order scores
Next: locate middle ((n+1)/2)

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35
Q

Bimodal

A

Tie between 2 for most repeated score (mode)
2 distinct peaks in distribution shape

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36
Q

Multimodal

A

Tie between >2 for most repeated score (mode)

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37
Q

Where are central tendencies located on normal distribution

A

All in middle if perfectly normal

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38
Q

Frequency tables

A

Display distribution of a single variable
* Variable listed from highest to lowest on one side, frequency on other

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39
Q

Histograms

A

Graphical display distribution
* quantitative variables don’t have gaps between bars unless the score has frequency of 0

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40
Q

Skewed with peak on the right

A

negative skew

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41
Q

Skewed with peak on the right

A

negative skew

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42
Q

Percent of scores lower than an individual score

A

Percentile rank
Number of scores converted into %

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43
Q

Z Score

A

Difference between individual score and the mean of distribution (x-m), divided by the standard deviation √((⅀(x-m)²)/n)

44
Q

Effect size differences between means

A

Cohen’s d
d = (M₁ - M₂) / SD
Formally use pooled SD

45
Q

Pooled SD

A

The average spread of all data points about their group mean (not the overall mean)

46
Q

Graphing correlations between quantitative variables

A

Line graph if x-axis variable (IV) has small # of values

Scatterplot if x-axis variable (IV) has large # of values

47
Q

Linear vs nonlinear relationship

graphing

A

Linear: pts fit into single, relatively straight line; Pearson’s r
Nonlinear: pts fit into curved line

48
Q

Pearson’s r

Purpose, limitation, steps

A
  • For linear relationships
  • From -1.00 to +1.00
  • Limitation: restriction of range - limited range in sample relative to pop
  1. Turn scores into z scores (x and y variables seperately)
  2. Multiple x and y z-scores together for each individual
  3. Take mean of cross products
49
Q

Bar graphs

Purpose, error bars, stat sig.

A
  • Present and compare mean score of groups when IV is categorical
  • Error bars for variability (extend one standard error in each direction)
  • if difference between means is greater than 2 standard errors, there is statistical significance
50
Q

SD of group divided by √n

A

STandard error

51
Q

Line graphs

Application, error bars

A
  • When IV is continuous (e.g. time) or small # of IV lvls
  • Use when IV is quantitative
  • Error bars for standard error
52
Q

Scatterplots

A
  • Correlations between quantitative variables when x-axis (IV) has large # of lvls
  • Add regression line
53
Q

Multiple-response measures

A
  • Enter seperately, then combine using software
  • assess internal consistency of the measure using Cronbach’s alpha or Cohen’s k
54
Q

How to analyze/show difference between means

A

Bra graph and cohen’s d

55
Q

How to analyze/show correlation between quantitative variables

A

Line graph or scatterplot (check for nonlinearity and restriction of range)
Pearson’s r

56
Q

Null Hypothesis

A
  • No relationship in the pop., relationship in the sample reflects only sampling error
  • Occured by chance
  • No relationship = Cohen’s d or Pearson’s r is 0
57
Q

Probability of the sample result or a more extreme result if the H₀ were true

A

P Value

58
Q

Factors of p value

A

Strength of relationship
Size of sample

59
Q

Low p value

A

Sample or more extreme result would be unlikely if the H₀ were true
Reject the null hypothesis
Statistically significant

60
Q

Alpha .05

A

5% chance or less of a result at least as extreme as the sample result if the null hypothesis were true

Even when the H₀ is true and alpha is .05, the H₀ will be mistakenly rejected 5% of the time

Greater than 5% chance ==> retain the H₀ (fail to reject)

61
Q

Type I error

A
  • Rejecting the H₀ when it is true (false positive)
  • p value tells probability of making a type I error
  • Cause: sampling error
  • Reduce the chance of making Type I error by setting alpha to something less than .05 BUT then this raises probability of making Type II error!
62
Q

File drawer problem

A

usually only moderate to strong positive relationships are published, leading to published effects showing a stronger relationship than is really in the population

63
Q

p-hacking

A

various decisions in the research process to increase the chance of a stat. sig. result
Set alpha before!!!

64
Q

Type II Error

A
  • retaining the H₀ when it is false (false negative)
  • Cause: relationship lacks adequate statistical power to detect relationship (e.g. sample is too small)
  • Reduce the chance of making Type II error by setting alpha to something more than .05 BUT then this raises probability of making Type I error!
65
Q

Statistical Power

A
  • probability of rejecting the null hypothesis given the sample size and expected relationship strength (pearson’s r)
  • Complement of the probability of committing a type II error
  • Power of .80 is adequate - means there’s an 80% chance of rejecting the null hypothesis for the expected relationship strength
  • To increase statistical power, increase the strength of the relationship or increase the sample size
    *
66
Q

Calculate probability of committing a TYpe II error given statistical power of .59

A

1 - .59 = .41

67
Q

4 Moral Principles in scientific rsrch

A
  1. Balance risks vs benefits
  2. Act responsibly and with integrity (maintain trust + transperency)
  3. Seek justice (fair treatment)
  4. Respect right and dignity (autonomy + consent)
68
Q

TCPS rsrch agencies

A
  • CAN institute of health rsrch
  • Nat sci and Eng. Rsrch council of CAN
  • Soc sci & humanities Rsrch council of CAN
69
Q

3 Levels of Risk

Federal Policy for the Protection of Human Subjects

A
  1. Exempt rsrch - nonsensitive, standard, public info
    * Maintain confidentiality
    * Once approved, exempt from regular, continuous review
  2. Expedited Rsrch - no greater than minimal risk
    * Reviewed by 1 member of IRB, or appointed subcommittee
  3. Greater than minimal risk - needs full IRB review
70
Q

Z score

define, distribution

A

Standard measure of the distance between a single point in the data and the overall mean for that variable
z-distribution:
* ranges from negative infinity to positive infinity
* has a mean of 0
* has a standard deviation of 1

71
Q

T score

A
  • Standard distribution with a mean of 50, and a SD of 10
  • without negative values
  • can use this to scale any variable (e.g. IQ)
72
Q

Percentiles

A

Use to express z-scores more intuitively
Refers to the proportion scoring less than a particular value
Obtained from z-table

73
Q

Parameters

A

Values describing population distribution

74
Q

Population distribution

centre, dispersion

A

Centre at mu
Dispersion indicated by sigma

75
Q

Sample distribution

A

x-bar = centre of the distribution
s = standard deviation/dispersion of the distribution

76
Q

Decision Matrix

A

state of reality vs decision reached in inferential testing
* Type I error
* Type II error (beta)
* Power

77
Q

Power

A
  • Study’s ability to find a difference if there is one
  • Correct rejection of the H₀
  • 1-beta (type II error) = power
  • ↑ Power by …
    1. ↑ n
    2. ↑ critical alpha
    3. ↑ effect size (∆)
78
Q

Null Hypothesis Sig. Testing

A
  1. Directional - upper/lower tailed (X… more/lower than Y) or two-tailed test (X different than Y)
  2. Establish rejection regions
    * two-tailed tests: split the alpha value in half (0.025) so non-rejection region is 0.5 - 0.025 = 0.475
    * single-tailed tests: rejection alpha value (0.05) on distribution graph
  3. If sample falls within rejection region, reject the null hypothesis and conclude that the alt hypoth. is likely correct
79
Q

Limitations of p value

A
  • Only relevant to specific sample stats
  • Conditioned on the null hypothesis being true
  • the false positive rate associated with a p value of .05 is usually around 30%, but can be much higher
  • silent on the magnitude and range of an effect
  • Even the most miniscule effect can be statistically significant if the sample size is large enough
80
Q

Limitation of significance testing

A
  • Null hypothesis is rarely true
  • ST provides a binary decision (yes or no) and a direction of the effect
  • Mostly interested in the size of the effect
  • Statistical vs practical significance
81
Q

Statistical Significance ⍺

define, stat significance

A
  • probability of results due to chance
  • Represents chance of making Type I error
  • smaller value = more “unusual” (e.g. sample is different than pop. that its being compared to)
  • Fail to reject H₀ if p > ⍺
  • Reject H₀ if p < ⍺
82
Q

Power Analysis

A

Calculate expected power before conducting a study based on estimated n, critical ⍺, expected or minimum effect size (from related rsrch)
Avoid post-hoc power analysis!!!!!!

83
Q

Effect size

A

Measure of the strength of a relationship
Unrelated to n and statistical significance
* Can be statistically significant but trivial effect
* Could be statistically insignificant, but notable effects (increase n to gain significance)
Cohen’s d = mean difference / SD
* d = 0.2, small effect
* d = 0.5, medium effect
* d = 0.8, large effect

84
Q

P value vs alpha

A

Alpha is arbitrary number/threshold (e.g. finish line)
P-value is the actual measurement (e.g. race time)

85
Q

Bayes’ Theorem

A

allows you to calculate exact percentages (conditional probability based on the occurrence of previous outcomes of similar circumstances)

86
Q

Converging operations

A

Mulitple operational definitions for same construct
allows for more general conclusion if multiple measures have consistent scores

87
Q

Levels of measurement with category labels

A

All of them!

88
Q

Levels of measurement that can rank order

A

Ordinal, interval, ratio

89
Q

Levels of measurement with = intervals

A

interval and ratio

90
Q

Test-retest reliability

A

COnsistency over time
For consturcts that are assumed to be consistent over time

91
Q

Internal Consistency

what? why? how?

A

Responses across the items on a multiple-item measure
* if all items represent the underlying construct, then people’s scores should be correlated which each other
* assessed by collecting and analyzing data
* Split-half correlation: split items into two sets, compute score for each set of items, examine relationship between the two sets using Cronbach’s alpha

92
Q

Cronbach’s alpha

what? why?

A

mean of all possible split-half correlations for a set of items
Used to measure internal consistency

93
Q

What does Cronbach’s alpha assess

A

quantitative judgements

94
Q

What does Cohen’s k assess

A

Categorical judgements

95
Q

Content validity

A

extent that measure covers the construct
check measurement method against coceptual def

96
Q

Criterion Validity

what? concurrent? predicitive? convergent?

A

Criterion Validity: extent that peoples scores are correlated with other variables (criteria) that are expected to be correlated

  • Criterion: any varibale that is expected to be correlated with the construct in question
  • Concurrent validity: when the criterion is measured at the same time as the construct
  • Predicitive validity: when the criterion is measured at some point in the future (after the construct has been measured)
  • Convergent validity: other measures of same construct
97
Q

Discriminant Validity

A

Discriminant Validity: extent to which scores on a measure are NOT correlated with measures of variables that are conceptually distinct (e.g. self-esteem and mood)
low correlations = evudence of conceptually distinct construct

98
Q

Utility

efficiency and generality

A

Is the data precise and reliable, at the lowest possible cost (Efficiency)?
Can the method be applied successfully to a wide range of phenomena (Generality)?

99
Q

Measurement errors

parallax, calibration, zero, damage

A

Parallax error (incorrectly sighting the measurement).
* e.g. don’t read the measurement right, copy it down wrong

Calibration error (if the scale is not accurately drawn)
* e.g. scale on a map is wrong

Zero error (if the device doesn’t have a zero or isn’t correctly set to zero)

Damage (if the device is damaged or faulty).
* e.g. bent level

100
Q

Types of error

gross, systematic (3), random

A

Gross errors: human mistakes in reading instruments and recording and calculating measurement results
Systematic Errors
* Instrumental: shortcoming, misuse, measurement accuracy
* Environmental: external and envrmtl factors
* Observational: inaccurate readings, conversion error
Random Errors: disturbance about which we are unaware

101
Q

5 causes of type I error

A

Measurement error
Lack of random sample
Alpha value too liberal
Investigator bias
Improper use of one-tailed test

102
Q

Causes of type II error

A

Measurement error
Lack of sufficient power (n too small)
Alpha value too conservative
Treatment effect not properly applied

103
Q

Social Desirable Responding

A

saying/doing socially appropriate thing

104
Q

Demand characteristics

A

subtle cues in the measure that reveal how the researchers expect participants to behave

105
Q

Meaurement Bias

Confirmation, recording, halo, social desirability

A

Confirmation bias
* find what u look for

Recording bias
* recall
* Rely on imperfect record (e.g. memory)
* Availability heuristic
* Primacy/recency effect

Halo effect
* extraneous variables affect measures
* common in subjective appraisal of ind diffs

Social desirability bias
* impression management
* participants more likely to report positive info to the experimenter

106
Q

Expectancy effects

participant, pygmalion, hathorne, halo, placebo, biosoc, psysoc

A
  • Participant types (good, bad, faithful, apprehensive)
  • Pygmalion - self-fulfilling prophecy; use doubl-blind
  • Hawthorne - alter bhvr (pierce!)
  • Halo - short-term improvement bc of novelt of treatment
  • Placebo - natural improvement, generalized effect of ‘being in treatment’, reinteropretation of outcome measures
  • Biosocial experimenter cues - age, sex, attraction
  • Psychosocial experimenter cues - warmth, status, etc
107
Q

Reducing expectancy effects

A
  • Standardize experimenter-participant interaction
  • Use blinding techniques
  • Use deception (active vs passive)
  • Convince participant that you can detect lying

KIN 2032 (1 decks)

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