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Psychology 277 > Midterm 1 > Flashcards

Midterm 1 Flashcards

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1
Q

explain the scientific method research process

A

1) ask a question
2) assume a natural cause for the phenomenon
3) consult past research
4) state a testable hypothesis
5) make a conclusion
6) submit report to peer-reviewed journal

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2
Q

explain the method for testing a hypothesis

A

1) design study to test hypothesis
2) seek ethical approval
3) collect data
4) analyze data
5) revise hypothesis
6) repeat 1-5 (usually a few times)

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3
Q

hypothesis

A

a possible answer (may be true or untrue) to the question asked

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4
Q

falsifiable hypothesis

A
  • must be specific
  • must take risks
  • must be testable (genuine test tries to refute rather than confirm)
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5
Q

what is the purpose of the concept of falsifiability?

A

evaluates the scientific status of a theory

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6
Q

what theory talked about in class is falsifiable?

A

einstein’s relativity

  • took risks
  • opportunity to test
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7
Q

what theory talked about in class is not falsifiable?

A

freud’s psychoanalysis

- no way to revise cause confidence

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8
Q

a hypothesis that is consistent with every possible outcome is essentially useless (should be incompatible with some), what is an example of this and why?

A

predict that weather tomorrow can be sunny, cloudy, rainy, or snowy –> not specific, takes no risks, always correct

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9
Q

what is the scientific status of a theory based on?

A
  • falsifiability
  • refutability
  • testability
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10
Q

hypothesis generate models of the world to help us:

A

1) predict phenomena
2) determine causes of phenomena
3) explain phenomena
4) control phenomena

NOT DESCRIBE

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11
Q

what is important about new data for a hypothesis?

A

must account for everything that old data does, and provide additional info

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12
Q

how long do hypotheses survive for?

A

until data which it can’t account for in uncovered

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13
Q

when is a hypothesis unfalsifiable?

A
  • when no empirical evidence in obtainable
  • when it’s predictions are irrefutable
  • when additional assumptions are introduced after refuted by data
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14
Q

falsifiability in practice

A

some theories not immediately discarded after contrary evidence is obtained

  • revised to improve experimental methods
  • useful but not testable (hope that will be testable one day) –> ex: string theory
  • can’t be as specific cause of lack of knowledge (ex: neuroscience more complex than physics)
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15
Q

operational definition

A

a specific description of how a concept will be measured

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16
Q

operationalization

A

links concepts to data collection

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17
Q

operational variables

A

quantities of interest that serve as substitutes for measuring concept of interest
- ex: number of smiles to show happiness

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18
Q

what is the purpose of operational definitions?

A
  • allow us to consistently quantify and measure concepts

- communicate ideas to others

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19
Q

what makes a good operational definition?

A
  • reliability
  • validity
  • absence of bias (ex: external factors)
  • cost (ex: low cost)
  • practicality (ex: easy to measure)
  • objectivity (ex: physical measurement not subjective)
  • high acceptance (ex: many others have used)
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20
Q

bias

A

difference between the measurement made and the “true” value of that variable

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21
Q

reliability and bias must be determined be over ___ measurements.

A

many

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22
Q

reliability

A
  • reproducibility of repeated measurements
  • must be based on concrete observable behaviours
  • facilitates consistency across measurements
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23
Q

what is the same as bias

A

systematic error

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24
Q

what is the same as reliability

A

precision, consistency

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25
Q

what is the opposite of bias

A

accuracy, validity

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26
Q

what is the opposite of reliability

A

variability, random error, noise

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27
Q

theory to prediction path

A

theory –> hypothesis (maybe many) –> operational definition –> prediction (based on OD)

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28
Q

hypothesis vs. prediction

A

hypothesis:

  • framed as a statement about something (phenomenon) that may or may not be true
  • often present tense
  • derived from broader theory

prediction:

  • conclusion related to specific methodological details of the study
  • often future tense
  • derived from a more general hypothesis
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29
Q

what is the same as validity

A

accuracy

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30
Q

validity

A
  • whether measures whats intended to measure (“true value”)
  • must be based on relevant behaviours
  • facilitates accuracy of measurements
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31
Q

what is the opposite of validity

A

bias, systematic error

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32
Q

what is the same as variability

A

random error, noise

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33
Q

what is the opposite of variability

A

reliability, precision, consistency

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34
Q

variability

A

how spread out the difference of measurements are

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35
Q

measurement = ?

A

true score + measurement error (systematic + random)

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36
Q

what are some factors that may contribute to measurement error?

A
  • specificity of operational definition not good enough

- internal noise of measurement device (living or nonliving)

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37
Q

interrater reliability

A

use multiple raters and compare the extent to which measurements agree

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38
Q

test-retest reliability

A

test that measures specific quantity (ex: IQ test)

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39
Q

2 types of test-retest reliability

A

1) same test

2) alternate forms

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40
Q

limitation of test-retest reliability

A

memory can affect results so won’t reliably measure changes

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41
Q

3 types of internal consistency reliability

A

1) split-half reliability
2) Cronbach’s alpha
3) item-total

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42
Q

split half reliability

A

randomly select half of subject and compare with other half –> test if halves are consistent

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43
Q

Cronbach’s alpha

A

measure how closely related set of items are as a group

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44
Q

item-total

A

correlate if each item relates to rest of tests/groups –> look at items individually

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45
Q

correlation coefficient (r)

A

one of the best ways to quantify relationship bw 2 coders

  • r > 0 = positive
  • r = 0 = no relationship (OD not specific enough)
  • r < 0 = negative (something wrong with coders)
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46
Q

indicators of construct validity (how well constructed OD its)

A

1) face validity
2) content validity
3) predictive validity
4) concurrent validity
5) convergent validity
6) discriminant validity

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47
Q

face validity + example

A
  • degree at which test subjectively (based in what individuals think) covers concept its suppose to measure (looks like measure what should)
  • ex: face memory test
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48
Q

content validity + example

A
  • degree at which test measures all things relevant to what its suppose to measure
  • ex: autism spectrum quotient (items corresponding to social skills, communication skills, imagination, attention to detail, attention switching)
  • almost opp. of face validity
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49
Q

predictive validity + example

A
  • degree at which data accurately predicts a future event based on criterion
  • ex: SAT indicate gpa in uni
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50
Q

concurrent validity + example

A
  • degree at which data for a present event correlates to previously validated criterion
  • ex: course grade
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51
Q

convergent validity + example

A
  • degree at which two measurements that should be related are
  • ex: new data agrees with other(s) in literature with same hypothesis
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52
Q

discriminant validity + example

A
  • degree at which measure is related to another concept it shouldn’t be related to
  • ex: new data agrees with other(s) in literature of different hypothesis
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53
Q

predictive/concurrent vs convergent/discriminant

A

predictive/concurrent
- based on gold standard (well known & agreed upon by many)

convergent/discriminant
- based on other measures (in literature, etc)

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54
Q

variable

A

any event, situation, behaviour, or individual characteristic that can take more than one value (can change)

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55
Q

divisions on variables

A
  • quantitative

- categorical

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56
Q

quantitative variables + example

A
  • have specific numbers

- ex: measure magnitude

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57
Q

categorical variables + example

A
  • have different levels, not numbers on defined scale

- ex: eye color

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58
Q

how to distinguish between quantitative and categorical variables?

A

subtraction test –> subtract lower level from higher level

  • if differences all have same meaning = quantitative
  • if differences have diff meaning = categorical
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59
Q

types of quantitative variables

A
  • discrete

- continuous

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60
Q

how to distinguish between discrete and continuous quantitative variables?

A

midway test –> take 2 levels and go midway between

  • have no meaning = discrete
  • still have meaning = continuous
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61
Q

discrete example

A

number of siblings

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62
Q

continuous example

A

time

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63
Q

divisions of quantitative variables

A
  • interval

- ratio

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64
Q

interval scale + example

A
  • have equal intervals but no meaningful zero

- ex: IQ

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65
Q

ratio scale + example

A
  • have equal intervals and a meaningful zero (means lack of something)
  • ex: speed
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66
Q

divisions of categorical variables

A
  • ordinal

- nominal

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67
Q

ordinal scale + example

A
  • has order
  • rank differences don’t need to reflect constant change
  • ex: military rank
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68
Q

nominal scale + example

A
  • no particular order

- ex: eye color

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69
Q

Likert scale –> interval or ratio?

A
  • treated as interval when analyzing data (technically ordinal)
  • 5-point or 7-point –> but usually self reported
  • ex: can’t have zero happiness
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70
Q

positive linear relationship

A

2 variables change in same direction by set amounts (x up, y up)

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71
Q

negative linear relationship

A

2 variables change in different direction by set amounts (x up, y down)

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72
Q

curvilinear relationship

A

still have certain relationship at any given time but direction of relationship not monotonous (positive from 1-5 seconds, negative from 6 - 10 seconds)

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73
Q

no relationship

A

usually somewhat circular shape, no relation on one variable change to another

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74
Q

linear relationship

A

variables change by set amount each time

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75
Q

non-linear relationship

A

variables do not change by set amount each time

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76
Q

monotonic relationship

A
  • overall relationship curve move in same direction (doesn’t matter if linear or not)
  • ex: positive non-linear
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77
Q

non-monotonic relationship

A
  • overall relationship changes direction is some places

- ex: curvilinear

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78
Q

non-experimental method

A
  • observations only –> both variables measured

- aka correlational method

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79
Q

experimental method

A
  • at least one variable manipulated (independent), one variable measured (dependent)
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80
Q

non-experimental vs. experimental method –> which one prefer?

A

experimental method

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81
Q

how to interpret correlation data?

A

1) correlation is spurious
2) A causes B
3) B causes A
4) third variable causes A and B –> A & B not correlated directly

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82
Q

limitation of non-experimental method

A
  • correlation doesn’t imply causation –> spurious or third variable problem
  • directionality problem (A –> B or B –> A?)
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83
Q

spurious correlation

A
  • just a coincidence

- seems to happen when looking at many things –> at least some will happen to have similar patterns

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84
Q

confounding variables

A

variables intertwined with another independent variable so can’t determine which is operating given situation (alternate explanation)

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85
Q

types of confounding variables

A
  • operational definitions –> ex: poor validity
  • participant factors –> ex: social status (based on individuals personal situation)
  • order effect –> ex: fatigue, practice (treatment effects)
  • group factors –> ex: self-selection
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86
Q

how to minimize confounding variables?

A

random assignment to conditions –> potential confound likely affect one group as other

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87
Q

internal validity

A

degree to which all confounding variables have been controlled (how confident can cause be inferred)

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88
Q

limitations of experimental method

A

plausible alternative explanations need to be eliminated

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89
Q

random assignment

A

each participant has equal change of being placed into any experimental group/condition

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90
Q

random sampling

A

randomly choose portion of larger group for doing experiment

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91
Q

descriptive statistics

A

help us organize, summarize, and describe data –> usually based on samples

92
Q

inferential statistics

A

help us generalize from the sample to the population –> based on descriptive stats

93
Q

what to look at in descriptive stats / frequency distributions?

A
  • shape
  • spread (variability)
  • outliers (variability)
  • central tendency
94
Q

graphical representations

A
  • pie chart
  • bar graph
  • histogram
  • frequency polygon
95
Q

benefits of pie chart

A
  • simple and easy to make

- can quickly see data results

96
Q

limitations of pie charts

A

hard to include measures of variation –> error bars (therefore not used in research)

97
Q

the law of large numbers

A
  • as sample size increases, sample statistics become less variable and more closely related to estimate population values
  • random phenomena are unpredictable in short term but more predictable in long term
  • ex: coin toss (4 tosses vs. 100 tosses); the casino example
98
Q

plots to use for categorical variables

A
  • pie charts

- bar graph (especially nominal)

99
Q

plots to use for quantitative variables

A
  • histogram

- frequency polygons

100
Q

histogram

A
  • shows frequency of occurrence of each score (distribution)

- x axis = each score; y axis = frequency of occurrence

101
Q

advantage of frequency polygons

A

easier to visualize 2 series of data compared to histograms

102
Q

frequency polygons special feature

A

have connecting lines indicating things are happening between the points

103
Q

shape of frequency distributions

A
  • symmetry –> symmetric, skewed

- modality –> unimodal, bimodal, multimodal, uniform

104
Q

symmetric distribution

A
  • can be divided intro two halves that are mirror images of each other
  • common
105
Q

positively skewed distribution

A
  • has score values with low frequencies trailing off towards positive numbers
  • central tendency early –> often when values bound by something
  • ex: people come in bank when just open
106
Q

negatively skewed distributions

A
  • has score values with low frequencies that trail off towards negative numbers
  • central tendency late
  • ex: investment returns
107
Q

unimodal distribution

A

has one peak

108
Q

bimodal distribution

A

has two peaks (ex: 2 distinct populations)

109
Q

uniform distribution

A
  • does not have well-defined mode (straight box)
  • all value on x axis equally likely to occur
  • ex: rolling dice
110
Q

central tendency

A
  • score of a value that corresponds to the centre of the distribution
  • a typical or representative score
111
Q

purpose of central tendency

A
  • summarize distribution
  • allows comparison to other distributions
  • used in inferential stat procedures
112
Q

3 main measures of central tendency

A
  • mean
  • median
  • mode
113
Q

central tendency measures: nominal

A
  • mode
114
Q

central tendency measures: ordinal

A
  • median

- mode

115
Q

central tendency measures: interval

A
  • mean
  • median
  • mode
116
Q

central tendency measures: ratio

A
  • mean
  • median
  • mode
117
Q

the mode

A

the score with the highest frequency

118
Q

advantages of the mode

A

can be used with any type of data (including nominal)

119
Q

limitations of the mode

A

ignores much of data available

120
Q

the median

A
  • the middle score with half the measurements before and have the measurements after
  • 50th percentile of a distribution
121
Q

advantages of median

A
  • robust against outliers
  • better summary of skewed data
  • can be used with ordinal (and everything except nominal)
122
Q

disadvantages of median

A

limits use of many statistical tests

123
Q

the mean

A

numerical average obtained by summing all scored in distribution and dividing by number of scores

124
Q

advantages of mean

A
  • easy to obtain & use

- good estimator to represent normal distribution (out of the 3 central tendency measures)

125
Q

disadvantages of mean

A
  • sensitive to outliers –> poor measure of “central tendency” for highly skewed distributions
  • not suitable for nominal or ordinal data
126
Q

central tendency for unimodal symmetric distribution

A

mean = median = mode

127
Q

central tendency for unimodal positively skewed distribution

A

mode, then median, then mean

128
Q

mean vs. median vs. mode –> which is preferred

A

mean

129
Q

mean vs median vs mode

A

mean

  • interval and ratio data (quantitative)
  • symmetric distribution
  • enables use of sophisticated statistical tests

median

  • ordinal, interval, ratio data
  • skewed distribution
  • outliers have little effect (so good representation of entire data)
  • limits the applicability of statistical tests

mode

  • ordinal and nominal data (categorical) –> all data
  • multimodal distributions
130
Q

survey

A

self-report measure administered through an interview or questionnaire

131
Q

categories of survey questions

A
  • attitudes & beliefs
  • facts & demographic
  • behaviours
132
Q

who created and what was the first survey

A
  • Charles Darwin (1860s)

- questionnaire on emotional expressions to determine if facial expressions are universal

133
Q

types of surveys

A
  • open-ended

- close-ended

134
Q

purpose of surveys

A
  • provides method for asking people to tell about themselves
  • can be used to study relationships bw variables
  • can serve as an important complement to experimental research findings
135
Q

pros to open-ended surveys

A

give opportunity to freely say everything individual’s thinking without bias from others

136
Q

cons to open-ended surveys

A

difficult to categorize data

137
Q

pros to closed-ended surveys

A

easier to code & analyze data

138
Q

cons to closed-ended surveys

A

may not give an option that an individual want as an answer –> not super accurate representation

139
Q

problems with survey question wording

A
  • unnecessary complexity
  • vague questions / statements
  • loaded / leading questions
  • double-barreled questions
  • negative wording
  • yea-saying and nay-saying
140
Q

surveys: unnecessary complexity

A
  • unfamiliar technical terms

- phrasing that overloads working memory

141
Q

surveys: vague questions / statements

A
  • imprecise terms

- ungrammatical sentence structure

142
Q

surveys: loaded / leading questions

A
  • embedding question with misleading info

- written to bias responses

143
Q

surveys: double-barreled questions

A
  • asking about two or more things at once
144
Q

surveys: negative wording

A
  • negative wording (direction in question) –> influence answers a bit
145
Q

surveys: yea-saying and nay-saying

A
  • hard to distinguish responses to several items in a row asked in same direction from one where participant agrees/disagrees with every item
  • pseudo question can help
146
Q

rating scale types

A
  • likert scale

- graphical scale (happy faces)

147
Q

problems with surveys

A
  • tendency to answer all questions in particular manner
  • “faking good” –> social pressure
  • topics too sensitive to talk
148
Q

solving problems that arise from surveys

A

assuring privacy, anonymity, confidentiality, etc

149
Q

features questionnaire should have

A
  • appear attractive and professional
  • neatly typed
  • error free
  • points scales consistent
  • ask interesting questions first
  • keep as short as possible
150
Q

how to administer questionnaires

A
  • in person –> groups or individuals
  • mail surveys
  • internet surveys
  • other technologies
151
Q

benefits of questionnaires

A
  • less costly than interviews
  • ensures anonymity
  • can reach out to a large number of people
152
Q

limitations of questionnaires

A
  • no in person clarification of questions

- boredom / distraction can occur

153
Q

population

A

a set of individuals of interest to the researcher

154
Q

confidence interval

A
  • a range of plausible values for the population value
  • help allow a more accurate generalization of entire population
  • aka margin of error
155
Q

sampling

A
  • smaller group to rely on when can’t survey everyone
156
Q

non-probability sampling

A
  • phenomena that are expected to be relatively similar across the population –> will occur for most individuals
  • convenience sampling for this
157
Q

convenience sampling

A

anyone will do

158
Q

probability

A
  • phenomena that are expected to vary across the population

- probability sampling for this

159
Q

probability sampling

A

sample “representative” of the population in question

160
Q

types of probability sampling

A
  • simple random sampling
  • stratified random sampling
  • cluster sampling
161
Q

simple random sampling

A
  • every member of the population has an equal probability of being selected
  • ex: randomly pick 10 winners
162
Q

stratified random sampling

A
  • population divided into subgroups (strata) and random samples taken from each strata
  • prevent small percentages from not getting represented
  • ex: pick 7 winners from contact lens wearers and 3 from non-wearers
163
Q

cluster sampling

A
  • when don’t know ALL individuals relating to the wanted phenomenon, so randomly select some clusters and study all the individuals in belonging to those clusters
  • ex: randomly pick a group from 2 decks of cards, randomly pick either spades/hearts/diamonds/clubs, all individual in that groups wins
164
Q

types of non-probability sampling

A
  • convenience sampling
  • purposive sampling
  • quota sampling
165
Q

convenience sampling

A
  • sampling whoever in most convenient
  • aka haphazard sampling
  • ex: everyone in front
166
Q

purposive sampling

A
  • sample meets predetermined criterion

- ex: every girl in second row

167
Q

quota sampling

A
  • sample reflects the numerical composition of various subgroups in the population
  • ex: 7 people with glasses and 3 without in third row wins
168
Q

sample size effect on confidence

A
  • larger sample size reduces size of the confidence interval (increase confidence)
  • don’t need to continue increasing sample size as population size increase in order to keep precision
169
Q

limitations to sample size effects on confidence

A

must consider cost / benefit of increasing sample size and find a balance

170
Q

independent groups (“between subjects”) experimental design

A
  • the different participants experience different levels of the independent variable
  • each person = only one treatment ever
171
Q

benefit of independent group experimental design

A
  • avoid order effects
  • avoid demand characteristics
  • treatments with relatively permanent effects can be done
  • similarity to “real-world” permanent effects
172
Q

limitation of independent group experimental design

A
  • any detected difference between condition may be attributed to individual group differences
  • low power –> any true differences may not be detected
173
Q

repeated measures (“within subjects”) experimental design

A
  • the same participants experience all levels of the independent variable
  • each person = try each treatment
174
Q

benefit of repeated measures experimental design

A
  • reduces measurement error by eliminating random error due to individual differences
  • differences among conditions cannot be attributed to participant differences
  • greater power (less measurement error) –> fewer participants needed, more likely to detect true differences
175
Q

limitation of repeated measured experimental design

A

order effects (can all play role at same time)

  • practice effect –> learning and memory
  • fatigue effect –> bored or tired
  • contrast effect –> what saw before affects now

demand characteristics

176
Q

within subjects not possible when…

A
  • independent variable is subject variable –> animal study (WT vs transgenic), human studies (ASD vs typical)
  • theres order effects –> relatively permanent treatment, like surgery, applied
177
Q

independent group vs repeated measures –> preference?

A

repeated measures if all else equal

178
Q

demand characteristics

A

any clue within the study that gives participants an idea of what the hypothesis is

179
Q

how to counteract order effects?

A
  • counterbalancing
  • partial counterbalancing
  • time interval between conditions
  • use independent group design
180
Q

when can you use counterbalancing?

A

on any experiment with multiple conditions (multiply levels of IV) –> figure out the number of orders for a experiment

181
Q

how to use counterbalancing?

A

randomly split subjects into N! orders –> N = number of independent variables conditions

182
Q

limitations of counterbalancing

A

impractical after a certain point (ex: 4!) because NEED at least 1 participant per order

183
Q

types of partial counterbalancing

A
  • latin square
  • random order
  • reverse counter-balancing
184
Q

latin square rules

A

1) each condition occurs in each position (order) once

2) each condition precedes and follows each condition once

185
Q

purpose of partial counterbalancing

A

make things easier than counterbalancing

186
Q

random order

A

put each participant that come in in a random order

187
Q

reverse counter-balancing

A

obtain one order (ex: ABC), then reverse it (ex: CBA) to get your groups

188
Q

purpose of time interval between conditions

A

separate time of tests to allow order effects (ex: fatigue) to have less of an effect

189
Q

how to create equivalent groups

A
  • random assignment to conditions

- matched pairs

190
Q

purpose of equivalent groups

A

should always try to reduce any error

191
Q

what are matched pairs? examples?

A
  • 2 subjects that are controls for each other

- ex: twins, spouses of patients, gaze contigent displays (eye movement example)

192
Q

caviat

A

one can never be sure if matching was 100% effective in creating equivalent groups

193
Q

latin square design

A
  • N orders for even N, N = # of conditions

- 2N orders for odd N, N = # of conditions

194
Q

things to consider when conducting studies

A
  • finalizing good study design
  • controlling for participant bias
  • controlling for experimenter expectations
195
Q

conducting survey studies

A
  • construct survey (wording, format, privacy)
  • recruitment (who, how many, protocol for recruitment)
  • data (how to record)
196
Q

conducting archival studies

A
  • sources of data (from where, who to use)
  • data (how to record)
  • ex: hockey players data
197
Q

conducting observational studies

A
  • observe (who, what, where)

- data (how to record)

198
Q

conducting experimental studies

A
  • prepare experimental material
  • recruitment (who, how many, protocol for recruitment)
  • jobs of each group member (getting data, etc)
  • data (how to record
199
Q

how to counteract demand characteristics?

A
  • deception
  • disguise the dependent measure
  • ask participants what they think hypothesis is after (filter ppl)
200
Q

bistable system + example

A
  • things that can two points of view (depending on the individual) without changing
  • ex: direction cube is turning
  • not ex: crater / dome illusion (stable to an individual bc of shading)
201
Q

disadvantages of demand characteristics + example

A
  • can change data obtained
  • cube example (when show people a specific direction the cube is turning at specific way of looking at it (with pole) during training, people will most often conform to that direction when asked what they see later on even if they don’t see it as that way (think its the “right answer”); but did not see same results for sound - cause answers remained stable even without the sound cue - so could conclude that data is not solely due to demand characteristics)
202
Q

how to counteract participant bias?

A
  • placebo effects on placebo group

- alternate between easy and hard tests (for reacting to adaptive procedures)

203
Q

how to counteract bias from reacting to adaptive procedures?

A
  • right = get harder; wrong = get easier
  • tell human will get harder is right, ask to remain calm
  • alternate bw easy and hard tests, along with reward
204
Q

what are the effects of experimenter expectations?

A
  • treat participants in different condition differently

- record / interpret data in different condition differently

205
Q

how to avoid effects of experimenter expectations?

A
  • repeated measures designs
  • automated presentation of condition and recording of data (experimenter doesn’t know participant on which)
  • double-blind procedures (as opposed to single blind)
206
Q

main reasons for using multiple levels of IV?

A

1) detect non-linear relationship between the IV and DV

2) rule out alternative explanations –> eliminate confounds

207
Q

examples of detecting non-linear relationships between the IV and DV

A
  • the mozart effect –> hypothesis that listening to classical music improves intelligence (memory performance)
  • randomly assign subjects to one of three conditions (no sound, rain (“placebo”), classical music) with latin square design
  • participants told that experiment tested relaxation on recall
  • did reversed digit span task
  • classical was actually not as good as silence (a little better than rain)
  • showed order effect has a say (practice effect)
208
Q

what are complex experimental designs

A
  • IV with 2 or more levels (more than 2 on slides)
    OR/and
  • designs with more than one IV
209
Q

example of a 2 levels of IV

A
  • caffeine study –> hypothesis that caffeine improves level of alertness
  • randomly assign subject to one of two conditions (doses of caffeine)
  • alertness increased initially with some caffeine but decreased with more caffeine
210
Q

factorial design

A

when a study included more than one single independent variable

211
Q

what is the simplest factorial design?

A
  • 2x2 –> 4 conditions total
  • 2 independent variables
  • 2 levels for each independent variable
212
Q

ways of representing 2x2 factorial design

A
  • chart (4 boxes; factor A vertical and factor B horizontal)
  • graph (can choose whats x axis; factor A is x axis then factor B is plotted; steeper = more of an effect of that factor if there is one)
213
Q

examples of 2x2 factorial design

A
  • candy experiment –> test how peoples eating habits are affected by others
  • 2 vs. 30 candies eaten by companion (same companion)
  • thin vs. obese companion (same companion)
214
Q

what do you interpret in factorial designs?

A

whether or not there is:

  • main effects of an independent variable (test both)
  • interaction between the independent variables
215
Q

how to test for main effect of an independent variable?

A
  • calculate marginal means (should get 4 total for 2x2)
  • ex: A vertical –> if the vertical marginal means (averages of horizontal lines) are different, theres a main effect of factor A
  • ex: B horizontal –> if the horizontal marginal means (averages of vertical lines) are different, theres a main effect of factor B
216
Q

how to test for interaction between the independent variables?

A
  • find difference of the points of the same column (if A is x axis; use same rows if B is x axis)
    - NOTE: difference can be (+) or (-) –> subtract
    bottom from top, or right from left
  • difference same for both (line on graph parallel or same) = no interaction
  • difference different (line on graph non parallel or not same) = interaction
217
Q

how many combinations of main effects and interaction outcomes can a 2x2 factorial design yield?

A

8

218
Q

independent variable by participant variable (IV x PV) factorial design

A
  • factorial designs with manipulated and non-manipulated variables
  • a mixed factorial design
219
Q

example of a IV x PV design

A
  • hypothesis that boys are better at math
  • 2x2 experiment –> gender (IV) x group (minority or same sex - PV)
  • show that boys performed better (main effect of A)
  • found that gender stereotype actually influences girl’s performance (main effect of B)
220
Q

why look for main effect AND interaction?

A
  • the main effect is either qualified or explained by the interaction
  • just main effect on its own is not as valuable
221
Q

consider a 2x3x4 factorial design: how many…

  • IVs
  • levels of each IV
  • DVs
  • main effects
  • interactions
A
  • IVs = 3 –> 2, 3, 4
  • levels of each IV = 2, 3, 4 respectively –> ex: gender, drug doses, quarter of day
  • DVs = 1 –> always unless measuring smth else separately
  • main effects = 3 –> factor 2, factor 3, factor 4
  • interactions = 4 –> 2/3, 2/4, 3/4, 2/3/4
222
Q

consider a 2x3x4 factorial study: how many conditions does each subject go through for…

  • repeated measures
  • independent groups
  • mixed-factorial
A
  • repeated measures = all 24 conditions –> so 1 group of subjects
  • independent groups = 1 condition –> so 24 groups of subjects
  • mixed-factorial = 12 conditions –> so 2 groups of subjects
223
Q

mixed factorial design

A

have both within subjects (repeated measures) and between subjects (independent) variables

224
Q

how many orders = ?, how many conditions = ?

A

factorial of # of conditions (for counterbalancing); multiply the # of levels of IV

225
Q

confound variable vs. third variable vs. mediating variable

A
  • confound: A or C –> B
  • third: C –> A and B
  • mediating: A –> C –> B

Psychology 277 (3 decks)

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