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RPE > exam > Flashcards

exam Flashcards

1
Q

rationalism

A

using logic to derive knowledge

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2
Q

empiricism

A

using observation to gain new knowledge

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3
Q

basic ethical principles

A

approval from irb
voluntary participation
no coercion
informed consent
debrief
confidentiality

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4
Q

central tendency

A

statistical measure to determine a single score that defines the center of a distribution

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5
Q

types of central tendency

A

mode: score or tendency w/ greatest frequency

median: score that divideds distribution in half

mean: sum of scores divided by the number of scores

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6
Q

measures of variance (define)

A

range: diff between largest and smallest score in a distribution

standard deviation: describes typical distance of scores from the mean (average distance)

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7
Q

reliability

A

consistency of a measurement

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8
Q

test-retest reliabilty and how to measure it

A

consistency of a participants response on a measure over time
measure it by: having participants take test multiple times and find correlation between responses

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9
Q

inter-item reliability

A

degree of consistency among the items on scale
measured by
- split half: correlation between each half of a scale
- cronbach’s alpha: average correlation between all items on scale

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10
Q

inter-rater reliability

A

consistency among two or more rates/ observers
measured by: 2 experimentors using the same rating and testing to find a positive relationship between both

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11
Q

validity

A

soundness of a measurement / measuring what is supposed to be measured

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12
Q

face validity

A

extent to which a measure appears to measure what its supposed to measure
(ex: are you sad? being used to measure depression)

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13
Q

content validity

A

extent to which measure adequaetly covers all aspects of the construct
(ex: depression construct meausred by are you sad? unmotivated? fatigued? )

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14
Q

construct validity & 2 types

A

how well a test measures the concept it was designed to evaluate.

  1. convergent validity: correlates with other measures as it should
  2. discriminant validity: does not correlate with other measures it should not correlate with
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15
Q

criterion related validity & 2 types

A

the extent to which a measure allows us to distinguish among participants on a behavioral criterion

  1. concurrent: measure and behavioral criterion are at the same time
  2. predictive: behavioral criterion is in the future
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16
Q

internal validity vs external validity

A

internal: degree to which researcher can draw accurate conclusions about the effects of IV

external: degree to which results obtained in one study can be replicated or generalized

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17
Q

relationship between reliability and validity

A

reliability sets upper limit for validity
- for a measurement to be valid, it has to be reliable
- high reliability does not mean high validity

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18
Q

representative sampling techniques

A

sample for which researcher knows the mathematical probability that any individual in pop. is included in sample

simple random: every possible sample has same chance of being selected
stratified: subset of population that shares a particular characteristic
cluster: used for large populations, group participants into clusters

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19
Q

nonrepresentative sampling techniques

A

no way of knowing probability that a particular case will be chosen for the sample

convenience: use anyone available
quota: get a certain # of specific types of people
snowball: find one member of population then ask person to locate others

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20
Q

how to interpret correlation coefficient

A

ranges from -1 to +1, 0 in middle
positive correlation if both variables increase
negative if one IV decreases as other increases

21
Q

types of correlations and when they are used

A

pearson: to measure the strength and direction of a linear relationship between two quantitative variables
spearman rank order: ordinal (ranked) variables
point biseral: 1 dichotomous (2 level) variable, one continuous
phi coefficient: two dichotomous variables

22
Q

partial correlations and what they are used for

A

correlation between 2 variables w/ the influence of another variable removed
- option1: correlation between x and y does not change after controlling for z
- option 2: correlation for x and y disappears or decreases after controlling for variable z

23
Q

correlations vs regressions (when they should be used)

A

correlations quanitify strength of linear relationship (describe)

regressions express relationship in form of equation (prediction)

24
Q

standardized vs unstandardized coefficients for regression

A

standardized when measurement scales of independent variables are different

25
Q

effect sizes

A

measure of the magnitude of a treatment effect

26
Q

meta analyses

A

method that combines the results of multiple studies to answer a research question

27
Q

define z scores and formula

A

score that represents the number of standard deviations an individual score falls below/above the mean for a distribution
- good at comparing scores across different distributions
- identifies and describes the exact location of an original score in a distribution

28
Q

experimental research

A

manipulating 2 or more variables to study cause-and-effect relationships

29
Q

correlational research

A

two or more variables are measured and the relationship between them is assessed

30
Q

define p value and statistical significance

A

p-value: number that measures the likelihood of a data set occurring if the null hypothesis is true
Statistical significance: used to describe when a p-value is small enough to reject the null hypothesis

31
Q

type I error, alpha

A

occurs when a researcher rejects a null hypothesis that is actually true
- alpha level: probability that a test wil lead to type 1 error

32
Q

Type II, beta

A

occurs when a researcher fails to reject a null hypothesis that is really false
- beta: probability of making a type 2 error

33
Q

confound (definition and types)

A

confound: third variable that differs between groups and isn’t accounted for
- classical
- environmental
- design
- temporal
- operational

34
Q

classic confound and example

A

A variable related to both the independent and dependent variables, affecting the results

ex: study measuring if drinking coffee improves concentration, a confound could be the amount of sleep participants get. If people who drink more coffee also tend to sleep less, it’s unclear if the improvement in concentration is due to coffee or lack of sleep.

35
Q

Environmental Confound and example

A

Variations in the environment that can influence the outcome, independent of the experimental manipulation

ex: room temp

36
Q

Design Confound and example

A

Systematic differences introduced by the study design that affect the outcome

ex: Does a new diet improve weight loss compared to a standard diet?

Confound: Different exercise routines.

37
Q

Temporal Confound and example

A

Changes over time unrelated to the experiment that impact the results.

ex: Does taking a vitamin supplement improve mood?

Confound: The time of day when the mood is measured.

38
Q

Operational Confound and example

A

Variations in how variables are measured that affect the outcome.

ex: Does a new cooking technique improve the taste of food?

Confound: The method used to rate food taste

39
Q

four scales of measurement

A

nominal: observations are labeled and characterized
- ex: college major, eye color
- diff kinds, not diff amounts

ordinal: consists of seperate categories that are arranged in rank order
- higher numbers = more of a quality
- ex: class ranking, customer satisfaction survey

interval: ordered categories where all of same categories are intervals of exactly the same size
- ex: temp in degrees fahrenheit
- no absolute zero

ratio: number reflect ratios of magnitude
- interval scales with absolute zero
- height, credit hours, time

40
Q

factorial design, nomenclature

A

a research technique that examines how multiple factors affect a dependent variable

nomenclature: number of factors, and the number of levels of each factor (ex: 2x2x3 means there are 3 factors, 2 with 2 levels and 1 with 3 levels)

41
Q

between, mixed, and within subject designs

A

between subjects design: each participant is tested under only one condition/level

within subjects design: each participant is tested under every condition / level of IV (repeated measures)

mixed: 1 IV is within and other is between

42
Q

counterbalancing (latin square design, all possible orders)

A

counterbalancing: diff participants complete levels of IV in diff orders

latin square design: method of counterbalancing where all possible orders are used

43
Q

independent vs subject variables (be able to identify iv and dv)

A

independent variable: researcher can manipulate or control in an experiment to observe its effect on another variable,
subject variable: characteristic inherent to the participants that cannot be manipulated by the researcher like gender or race

44
Q

why would anova be run vs a t test

A

ANOVA is used to compare the means among three or more groups while t test only compares 2 groups

45
Q

error (define and types)

A

the difference between the observed value and the true value of what is being measured
- instrumental
- observer/researcher
- participant

46
Q

instrumental error

A

errors due to imperfections in the tools or instruments used for measurement

ex: thermometer consistently off by 2 degrees

47
Q

observer/ researcher error

A

occurs due to the researchers influence or mistakes in data collection, recording, or interpretation

ex: accidentally leading participant to answering in a certain way during an interview

48
Q

participant error

A

participants providing socially desirable response rather than honest ones in a survey

RPE (1 decks)

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