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PSYC 2140 > final exam > Flashcards

final exam Flashcards

1
Q

Why can’t we say that we have “proven” anything?

A

because they use that term to refer to the result of a logical deduction. In this rigorous sense, scientific theories can never be proven; they can only be confirmed. (weight of evidence)

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2
Q

can research explain all cases?

A

no, because research is probabilistic, but can explain a portion of the cases

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3
Q

research is done on a sample, so… there is always some error

A

sampling error

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4
Q

small chance that we made error, so we set probability to p

A

statistics

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5
Q

something that varies/changes

A

variable

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6
Q

factor in an experiment that researchers manipulate so that they can determine its effect, factor in a controlled experiment that is deliberately changed; also called manipulated variable

A

independent variable

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7
Q

the reaction to the independent variable changing

A

dependent variable

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8
Q

does not change (only has one level) or is kept the same

A

constant

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9
Q

carefully define concept at theoretical level (conceptual definition)

A

concept variable = construct

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10
Q

a variable of interest, stated at an abstract level, usually defined as part of a formal statement of a psychological theory

A

construct

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11
Q

to turn a conceptual definition of a variable into a specific measured variable or manipulated variable in order to conduct a research study

A

operationalize

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12
Q

reasonable, accurate, justifiable

A

validity

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13
Q

how consistent your results are

A

reliability

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14
Q

you are ONLY able to make casual claims with a…

A

true experiment

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15
Q

what 3 criteria must you adhere to have a true experiment?

A

random sample, random assignment, and an IV with 2 levels at least

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16
Q

why do researchers use random assignment to treatment groups?

A

increases internal validity

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17
Q

used only in experimental designs to assign participants to groups at random (increases internal validity)

A

random assignment

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18
Q

everybody has equal chance of being chosen (increases external validity)

A

random selection

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19
Q

A “false positive” result from a statistical inference process, in which researchers conclude that there is an effect in a population when there really is none

A

Type I error

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20
Q

a “miss” in the statistical inference process, in which researchers conclude that there is no effect in a population when there really is one

A

Type II error

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21
Q

a variable of interest, stated at an abstract, or conversational, level

A

conceptual variable

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22
Q

what are three common measures?

A
  • self-report
  • observational measures
  • physiological measures
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23
Q

concepts can be operationalized in lots of different ways so…

A

it is a good idea to use more than one concept to see if they correlate

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24
Q

why are scatterplots used?

A

compile data after test (ex. IQ)

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25
Q

how can we use correlation coefficient “r” to quantify reliability?

A

slope direction, strength of relationship

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26
Q

how do we interpret “r”?

A

r = +- 1.0

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27
Q

how do validity and reliability relate to one another?

A

something can be reliable and not valid - however, you can not have something valid that is unreliable too

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28
Q

what are the different types of validity?

A

face, content, criterion- known groups evidence; convergent and discriminant

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29
Q

how can question order impact someone’s answers? what can be done about it?

A

questions asked earlier in the survey may influence the way a person answers a question later in the survey

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30
Q

how can you get people to respond honestly and/or accurately?

A

conduct pilot tests or focus groups early in the survey

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31
Q

a shortcut respondents may use to answer items in a long survey, rather than responding to the content of each item

A

response set

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32
Q

examples of response sets

A

non differentiation, acquiescence or yea sayers, nay sayers, fence sitting

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33
Q

observer bias- how can it be addressed?

A

systematic errors in observation that occur because of an observer’s expectations

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34
Q

what are observer effects? (also known as expectancy effects)?

A

bright vs. dull maze rats, clever Hans-researcher subtly communicated to participants “how they should behave”

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35
Q

How do we prevent observer bias/expectancy?

A

use masked or blind designs, video or audio record, use more than one observer to assess inter-rater reliability, make sure coding/observing system is well thought out, observers well trained

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36
Q

What can be done about reactivity?

A

unobstrusive observations and deception

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37
Q

extent to which research results apply to a range of individuals not included in the study

A

generalizability

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38
Q

extent to which we can generalize findings to real-world settings

A

external validity

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39
Q

why are generalizability and external validity important for frequency claims?

A

frequency claims need random sample; when a random sample can’t be used, assess whether potential bias will have impact on results; casual and association claims make external validity a lower priority

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40
Q

population vs. sample

A

whole set vs part of population

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41
Q

simple random sample, cluster samples, multistage samples, stratified random samples, oversampling, systematic sampling, weighting - combo of sampling techniques

A

probability sampling

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42
Q

convenience sampling, purposive sampling, snowball, and quota

A

non-probability sampling

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43
Q

what are the three criteria for a casual claim?

A
  1. covariance
  2. temporal precedence
  3. internal validity
44
Q

casual claims can only be made or based on what?

A

true experiments

45
Q

bivariate correlation

A

calculates correlation

46
Q

argues that one level of a variable is likely to be associated with a particular level of another variable

A

association claim

47
Q

random assignment to treatment, random selection of a sample, and an IV with at least 2 levels

A

criteria to be considered an experiment

48
Q

why can’t we talk about cause and effect with casual claims?

A

can only be made or based on experiments

49
Q

how do experiments differ from non-experimental designs we have talked about?

A

experiments are more powerful than non-experimental designs because they have two group designs (one receives IV and other group doesn’t or IV has two levels - level 1 = IV level and level 2 = another level of IV)

50
Q

experiment with one independent variable with two levels

A

simple experiment

51
Q

know how to pick out IV and DV

A

example experiment

52
Q

what is a control variable?

A

independent variable (you control it)

53
Q

why are comparison groups so important?

A

don’t know if your experiment is working or not

54
Q

level of IV that is intended to represent no treatment or a neutral condition

A

control groups

55
Q

group that receives the IV

A

treatment groups

56
Q

when control group is exposed to an inert treatment such as a sugar pill

A

placebo groups

57
Q

experimenters control…

A

temporal precedence

58
Q

threats to internal validity but don’t know what is causing the change

A

confounds

59
Q

experimenter’s mistake in designing IV; second variable happens to vary systematically along with intended IV and is an alternative explanation for results

A

design confound

60
Q

systematic variability impact on an experiment?

A

levels coincide in a predictable way with experimental group membership that creates a potential confound; seriously jeopardize internal validity

61
Q

unsystematic variability impact on an experiment?

A

levels of a variable fluctuate independently of experimental group membership that contribute to variability within groups (no confound)

62
Q

selection effects - how can you control for it?

A

happens when participants in one level of IV are systematically different from those in the other or when experimenters let participants choose their groups; use random assignment (desystemize types of participants who end up in each group) or matched groups

63
Q

match participants on a variable (other than the IV) that might otherwise impact how they behave in your experiment (your DV)

A

matched groups

64
Q

independent groups design = between subjects design = random assignment design

A

synonyms; randomly assign participants to groups (or treatment levels); independent because there are no connections/ties between subjects; two basic forms: posttest only design and pretest/posttest design

65
Q

assigned to IV groups are tested on DV once; testing after experiment

A

posttest only design

66
Q

DV is measured more than one time (before and after treatment on DV), but there is only one administration of the IV whereas a within subjects design, the participants get exposed to multiple levels of an IV

A

pretest posttest only design

67
Q

within groups design = repeated measures = within subjects or correlated groups design

A

synonymous; test the same participants in both treatment conditions (IV and DV); very powerful

68
Q

strengths/weaknesses of within groups design

A

weaknesses: order effects, might not be possible or practical, people see all levels of IV and then change the way they would normally act.
Strengths: ensures participants in the two groups will be equivalent

69
Q

what is meant by power?

A

probability that a study will show a statistically significant result when an IV truly has an effect in the population

70
Q

with within groups design there are order effects. what are examples of order effects?

A

practice or testing effects and carryover effects

71
Q

long sequence might lead participants to get better at the task

A

practice or testing effects

72
Q

some form of contamination carries over from one condition to the next

A

carryover effect

73
Q

how can order effects be avoided?

A

counterbalancing

74
Q

used to deal with order effects; sample is divided in half, with one half completing the two conditions in one order and the other half completing the conditions in the reverse order

A

counterbalancing

75
Q

all possible condition orders are represented

A

full counterbalancing

76
Q

only some of the possible condition order are represented

A

partial counterbalancing

77
Q

technique for partial counterbalancing; a formal system to ensure that every condition appears in each position at least once

A

Latin square

78
Q

create an alternative explanation for a study’s results

A

demand characteristics

79
Q

how do we use statistics?

A

to interpret data (graphs)

80
Q

what does it mean if something is statistically significant?

A

unlikely to have been obtained by chance from a population in which nothing is happening

81
Q

what are threats to internal validity?

A 
maturation 
history
regression 
attrition 
testing 
instrumentation
82
Q

why is a one group pretest posttest only design a bad research design?

A

not enough between-groups difference

83
Q

any study can suffer from…

A

observer bias; demand characteristics; good participant effect - prevent with masked, single or double blind

84
Q

other problems that can make or break an experiment

A

not enough difference between groups - weak manipulations, insensitive measures, ceiling and floor effects

85
Q

does too much within group variability make it harder to detect group differences?

A

yes, the less within-group variability, the less likely it is to obscure a true group difference

86
Q

reason for high within-group variability; human or instrument factor that can inflate or deflate a person’s true score on DV

A

measurement error

87
Q

how does adding more participants help?

A

reduces the influence of individual differences within groups which enhances the study’s ability to detect differences between groups

88
Q

outcome if the IV did not make a difference in the DV; there is not covariance between the two

A

null effect

89
Q

experiments with two or more IV

A

complex/factorial designs

90
Q

why would a researcher opt to use a complex design? benefits?

A

levels not manipulated; to test whether an IV affects different kinds of people in the same way

91
Q

how do we interpret results from a complex design?

A

collect data on a variety of DV- graphs

92
Q

how are marginal means calculated?

A

sample sizes are equal = simple average.

sample sizes unequal = computed using the weighted average counting larger sample more; hep you eye ball data to determine if there is a main effect (need to know inferential statistics to verify - ANOVA)

93
Q

how are factorial designs used to test theories?

A

study how variables interact by combining them in a factorial and measure whether the results are consistent with the theory

94
Q

one IV is manipulated as independent-groups and the other is manipulated as within-groups

A

mixed factorial

95
Q

there are more complicated factorial designs…

A

ex. 2 x 2 x 2

96
Q

what is the difference between a true experiment and a quasi-experimental design?

A

quasi-participants cannot randomly be assigned to an IV

97
Q

what are three types of small N designs?

A
  • stable baseline design
  • multiple baseline design
  • reversal design
98
Q

what are the benefits of quasi-experiments?

A

real world applicability and external validity and excellent for things you can’t ethically vary an IV

99
Q

change in behavior that emerges more/less over time

A

maturation threats

100
Q

affects most members of treatment group at same time as treatment itself, making it unclear whether the change is caused by treatment received

A

history threats

101
Q

group mean is usually extreme at one time then next time it is measured, it is less likely to be extreme

A

regression threats

102
Q

when attrition is systematic, certain kind of participant drops out

A

attrition threats

103
Q

change in participants as a result of taking a test more than once

A

testing threats

104
Q

measuring instrument changes over time

A

instrumentation threats

105
Q

a preexisting variable that is often a characteristic inherent to an individual, which differentiates the groups or conditions being compared in a research study. Because the levels of the variable are preexisting, it is not possible to randomly assign participants to groups

A

quasi-independent variable

PSYC 2140 (3 decks)

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