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RESIT research & statistics Flashcards

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1
Q

inference

A

making decisions and predictions based on the data for answering the statistical question

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2
Q

inferential statistics

A

methods of making decisions or predictions about a population, based on data obtained from a sample of that population

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3
Q

parameter

A

numerical summary of the population

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4
Q

categorical variable

A

if each observation belongs to one of a set of categories

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5
Q

quantitative

A

if observations on it take numerical values that represent different magnitudes of the variable

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6
Q

discrete quantitative variable

A

0,1,2,3,4, if its possible values form a set of separate numbers

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7
Q

continuous quantitative variable

A

if its possible values form an interval

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8
Q

modal category

A

category with the highest frequency (for a categorical variable)

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9
Q

mode

A

(for quantitative variable), the numerical value that occurs most frequently

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10
Q

unimodal

A

data has single mount

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11
Q

bimodal

A

data has two distinct mounds

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12
Q

median

A

middle value of the observations when the observations are ordered from smallest to the largest(or other way around)

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13
Q

resistent

A

if extreme observations have little if any influence on its value

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14
Q

deviation

A

of an observation x from the mean , the difference between the observation and the sample mean

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15
Q

variance

A

average of the squared deviations

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16
Q

pth percentile

A

value such that p percent of the observations fall below or at that value

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17
Q

z-score

A

for an observation is the number of standard deviations that it falls from the mean. positive z score indicates the observation is above the mean, negative below the mean

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18
Q

empiricism

A

involves using evidence from the senses or from instruments that assist the senses as the basis for conclusions.

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19
Q

parsimony

A

theories are supposed to be simple. mid two theories explain the at a equally well, most scientists will opt for the ampler more parsimonious theory

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20
Q

applied research

A

conducting in real world context

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21
Q

probabilistic

A

behaviour research findings are not expected to explain all cases all of the time.

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22
Q

availabiliity heuristic

A

things pop up easily in our mind and to guide our thinking

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23
Q

present bias

A

name for our failure to consider appropriate comparison groups

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24
Q

confirmation bias

A

tendency to look only at info that agrees with what we already believe

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25
Q

bias blind spot

A

the belief that we are uniquely to fall prey to the other biases previously described

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26
Q

reliability

A

how consistent the results of a measure are

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27
Q

validity

A

concerns the operationaliztion measuring what is it supposed to measure

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28
Q

internal reliability

A

a study participant gives a consistent pattern of answers, no matter how the researcher has phrased the question

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29
Q

criterion validity

A

evaluates whether the measure under consideration is associated with a concrete behavioural outcome that it should be associated with, according to conceptual definition

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30
Q

known groups paradigm

A

in which researchers see whether scores on the measure can discriminate among two or more groups whose behaviour is already confirmed

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31
Q

convergent validity

A

the pattern of correlations with measures of theoretically similar or dissimilar constructs

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32
Q

discriminant validity

A

something should not correlate with measures of construct that are very different

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33
Q

independent trials

A

if the outcome of any one trial is not affected by the outcome of any other trial

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34
Q

subjective definition of probabilit

A

the probability of an outcome is defined by personal probability, your degree of belief that the outcome will occur based on available information

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35
Q

sample space

A

for a random phenomenon, the sample space is the set of all possible outcomes

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36
Q

event

A

a subset of the sample space

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37
Q

good story bias

A

people tend to believe convincing stories

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38
Q

deduction

A

the process of formulating a prediction that follows from your theory

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39
Q

demand characteristics

A

a participant wants to be a ‘good’ participant

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40
Q

self perception

A

people do not necessarily have a correct self image

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41
Q

social desireability

A

participants want to give a good first impression about themselves

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42
Q

primacy effect

A

the effect of being the first to be observed

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43
Q

recency effects

A

the effect of being the last to be observed

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44
Q

test retest reliability

A

strength of an association between test and retest gives an indication of reliability

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45
Q

cronbachs alpha

A

a measure of internal reliability

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46
Q

external validity

A

if it can be generalized

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47
Q

covariance

A

do the two variables go together

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48
Q

temporal precedence

A

did the cause occur before the effect

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49
Q

cluster sampling

A

sample random schools, use children in each school

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50
Q

multistage sampling

A

a random sample of clusters, then a random sample of people within those clusters

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51
Q

stratified random sampling

A

in which the researcher purposefully selects particular demographic categories or strata, and then randomly selects individuals within each of the categories, proportionate to their assumed membership of the population

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52
Q

oversampling

A

blood type ab only 0,5 of population, sample extra ab people to precent unreliability

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53
Q

systematic sampling

A

each fifth person in a class

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54
Q

convenience sampling

A

a sample that is easily accessible

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55
Q

purposive sampling

A

search for participants that meet requirements

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56
Q

snowball sampling

A

start with a couple of subjects, and ask these subjects whether they know more subjects

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57
Q

quota sampling

A

set a quota (50 law 50 psych students) and select non randomly up until quotas are fulfilled

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58
Q

observer bias

A

observations are influenced by your expectations

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59
Q

reactivity

A

people may behave differently when they know that they are being observed

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60
Q

spurious association

A

association due to a third variable

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61
Q

posttest design

A

random assignment multiple groups, test the difference after group assignment

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62
Q

repeated measures design

A

most important within group design, measure twice in each participant, test the difference

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63
Q

discrete variables

A

not all variables have the same probability

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64
Q

standard deviation

A

indicates the amount of dispersion in the population

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65
Q

sampling distribution

A

of a statistic is the probability distribution that specifies probabilities for the possible values the statistic can take

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66
Q

central limit theorem

A

for any large enough sample, the sampling distribution is approximately normally distributed

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67
Q

confidence interval

A

interval containing the most believable values for a parameter

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68
Q

confidence level

A

the probability that this method produces an interval that contains the parameter

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69
Q

type 1 error

A

false positive, stating that there is an association when there is no association

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70
Q

type 2 error

A

stating that there is no association when in fact there is one

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71
Q

internal validity

A

is an indication of a study’s ability to eliminate alternate explanations for the association

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72
Q

semantic differential format

A

instead of degree of agreement, respondent might be asked to rate a target object using a numeric scleras that is anchored with adjectives

73
Q

double-barrelled question

A

a question that asks two questions in one

74
Q

response sets

A

type of shortcut respondent can take when answering survey questions.

75
Q

acquiescence

A

yeah saying

76
Q

fence sitting

A

playing it safe by answering in the middle of the scale

77
Q

socially desirable responding

A

respondents give answers that make them look better than they really are.

78
Q

observer bias

A

occurs when observers expectations influence their interpretation of the participants behaviours or the outcome of the study

79
Q

observer effects

A

observers change the behaviour of those they are observing

80
Q

census

A

if you have the scores of everyone in a population

81
Q

biased sample

A

unrepresentative sample, some members of the population of interest have a much higher probability of being included in the sample compared to other members

82
Q

unbiased sample

A

representative sample

83
Q

self-selection

A

when a sample contains only people who volunteer to participate

84
Q

probability sampling

A

every member of interest has an equal and known chance of being selected for the sample, regardless of whether they are convenient or motivated to volunteer.

85
Q

nonprobability sampling

A

involve nonrandom sampling and result in a biased sample

86
Q

weighting

A

if they determine that the final sample contains fewer members of a subgroup than it should, they adjust the data so responses from members of underrepresented categories count more, and overrepresented members count less.

87
Q

purposive sampling

A

if researchers want to study only certain kinds of people, they recruit only those particular participants

88
Q

confounds

A

alternative explanations, potential threats to internal validity

89
Q

design confound

A

experimenters mistake in designing the independent variable

90
Q

selection effects

A

when the kinds of participants in one level of the independent variable are systematically different from those in the other

91
Q

independent groups design

A

in which different groups of participants are placed into different levels of the independent variable (between subjects design/ between groups design)

92
Q

within groups design

A

(within subjects design) there is only one group of participants and each person is presented with all levels of the independent variable

93
Q

post-test only design

A

participants are randomly assigned to independent variable groups and are tested on the dependent variable once.

94
Q

pretest posttest design

A

participants are randomly assigned to at least two different groups and are tested on the key dependent variable twice

95
Q

repeated measures design

A

a type of within groups design in which participants are measured on a dependent variable more than once, after exposure to each level of the independent variable

96
Q

concurrent-measures design

A

participants are exposed to all the levels of an independent variable at roughly the same time, and a single attitudinal or behavioural preference is the dependent variable

97
Q

power

A

refers to the probability that a study will show a statistically significant result when an independent variable truly has an effect in the population

98
Q

order effects

A

happens when exposure to one level of the independent variable influences responses to the next level

99
Q

practice effects

A

in which a long sequence might lead participants to get better at the task or to get tired or bored

100
Q

carryover effects

A

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

101
Q

counterbalancing

A

they present the levels of the independent variable to participants in different sequences

102
Q

full counterbalancing

A

in which all possible condition orders are represented

103
Q

partial counterbalancing

A

in which only some of the possible condition orders are represented

104
Q

latin square

A

a formal system to ensure that every condition appears in each position at least once

105
Q

demand characteristic

A

a cue that can lead participants to guess an experiments hypothesis

106
Q

manipulation check

A

an extra dependent variable the researchers can insert into an experiment to convince them that tier experimental manipulation worked

107
Q

interaction effects

A

the effect of one variable depends on another variable

108
Q

maturation threat

A

patient improvement without a specific cause, sometimes called spontaneous remission, can be prevented by : comparison group and pretest post-test

109
Q

history threat

A

an external factor that influences the dependent measure, can be prevented by comparison group

110
Q

regression to the mean

A

participants with extreme scores will have less extreme results later

111
Q

non-specific effects

A

any other effect that causes a change in condition that is unrelated to the treatment

112
Q

scientific hypothesis

A

hypothesis about how the world works

113
Q

statistical hypothesis

A

hypothesis about a population parameter

114
Q

p-value

A

probability of these or extremer results if nulhypothesis is true

115
Q

level of significance

A

you conclude that the nulhypothesis should be rejected in favour of the alternative hypothesis. commonly smaller than 0.05 means that the nulhypothesis is rejected

116
Q

attrition threat

A

when a certain kind of participant drops out , to prevent this : remove scores from the participant from the pretest too

117
Q

testing threat

A

refers to a change in the participants as a result of taking a test (dependent measure) more than once

118
Q

instrumentation threat

A

when a measuring instrument changes over time

119
Q

selection-history threat

A

an outside event or factor affects only those at one level of the independent variable

120
Q

selection attrition threat

A

only one of the experimental groups experiences attrition

121
Q

null effect

A

if the independent variable did not make a difference in the dependent variable -> there is no significant covariance between the two

122
Q

ceiling effect

A

all the scores are squeezed together at the high end

123
Q

floor effect

A

all the scores cluster at the low end

124
Q

manipulation check

A

separate dependent variable that experimenters include in a study , to make sure manipulation worked

125
Q

measurement error

A

a reason for high within group variability, its a human or instrument factor that can inflate or deflate a persons true score on the dependent variable

126
Q

weighted average

A

used when each x value is not equally likely

127
Q

factorial design

A

one in which there are two or more independent variables

128
Q

participant variable

A

a variable whose levels are selected (measured), not manipulated

129
Q

marginal means

A

are the arithmetic means for each level of an independent variable averaging over levels of the other independent variable

130
Q

file drawer problem

A

researchers tend to not write papers about insignificant results

131
Q

publication bias

A

journals publish insignificant results less often than significant results

132
Q

data dredging

A

doing as many statistical tests up until you find a significant resul

133
Q

harking

A

hypothesising after the results are known

134
Q

explorative

A

no specific hypothesis

135
Q

confirmatory

A

you test a specific hypothesis

136
Q

cross validation

A

after exploration, conduct a new confirmatory study

137
Q

stable baseline design

A

study in which a practitioner or researcher observes behaviour for an extended baseline period before beginning a treatment or other intervention, multiple pretests and posttests, helps to prevent regression to the mean, maturation and nonspecific effect

138
Q

multiple baseline design

A

goal is to exclude effect of external factors

139
Q

direct replication

A

exactly the same experimental conditions/operationalizations

140
Q

systematic repication

A

change some aspects of the study

141
Q

conceptual replicatio

A

the same research question, but a complete different experimental setting

142
Q

principle of beneficence

A

researchers must take precautions to protect participants from harm and to ensure their well being

143
Q

bivariate correlation

A

association that involves exactly two variables

144
Q

construct validity

A

how well was each variable measured

145
Q

statistical validity

A

how well do the data support the conclusion

146
Q

effect size

A

describes the strength of a relationship between two or more variables

147
Q

statistical significance

A

refers to the conclusion a researcher reaches regarding the likelihood of getting a correlation of that size just by chance, assuming there is no correlation in the real world.

148
Q

p value means

A

the probability that the samples association came from a population in which the association is zero

149
Q

restriction of range

A

if there is not a full range of scores on one of the variables in the association it can make the correlation appear smaller than it really is.

150
Q

curvilinear association

A

in which the relationship between two variables is not a straight line, it might be positive up to a point and then become negative

151
Q

directionality problem

A

sometimes called temporal precedence, when you don’t know which variable comes first

152
Q

spurious association

A

the bivariate correlation is there but only because of some third variable.

153
Q

moderator

A

when the relationship between two variables changes depending on the level of another variable

154
Q

quasi experiment

A

differs from a true experiment in that the researchers do not have full experimental control

155
Q

nonequivalent control group design

A

different participants at each level of the independent variable. it has at least one treatment group and one comparison group, but participants have not been randomly assigned

156
Q

nonequivalent control group pretest/posttest design

A

the participants were not randomly assigned to groups, and were tested both before and after some intervention.

157
Q

interrupted time-series design

A

a quasi experimental study that measures participants repeatedly on a dependent variable before during and after the ‘interruption’ caused by some event.

158
Q

nonequivalent control group interrupted time series design

A

it combines non equivalent control group design dnt he interrupted time series design

159
Q

multiple baseline design

A

researchers stagger their introduction of an intervention across a variety of individuals, times or situations to rule out alternative explanations

160
Q

reversal design

A

a researcher observes a problem behaviour both with and without treatment but takes the treatment away for a while to see whether the problem behaviour returns

161
Q

which non parametric statistic is appropriate for a research design that compares a quantitative response variable for two independent groups?

A

wilcoxon test

162
Q

which non parametric statistic is appropriate for a research design that compares a quantitative response variable for two independent groups?

A

wilcoxon test

163
Q

interquartile range

A

is the distance between the third and first quartiles

164
Q

interquartile range

A

is the distance between the third and first quartiles

165
Q

direct replication

A

researchers repeat an original study as closely as they can to see whether the effect is the same in the newly collected data.

166
Q

conceptual replication

A

researchers explore the same research question but use different procedures

167
Q

replication plus extension

A

researchers replicate their original experiment and add variables to test additional questions

168
Q

p hacking

A

the goal is to find a p value of just under 0.05, the traditional blue for significance testing and researchers may try to make their results work

169
Q

preregistration

A

scientists can preregister their study’s method, hypotheses or statistical analyses online, in advance of data collection

170
Q

preregistration

A

scientists can preregister their study’s method, hypotheses or statistical analyses online, in advance of data collection

171
Q

ecological validity

A

study’s similarity to real world contexts

172
Q

theory testing mode

A

researchers are then designing correlational or experimental research to investigate support for a theory

173
Q

generalization mode

A

when researchers want to generalise the findings from the sample in a previous study to a larger population

174
Q

experimental realism

A

lab experiments create situations in which people experience authentic emotions, motivations and behaviours

175
Q

assumptions for a confidence interval that have to be satisfied

A

random sample and distribution has to be normal

176
Q

specifity

A

says negative when it is indeed negative

177
Q

sensitivity

A

says positive when it is indeed positive

178
Q

model

A

a simple approximation for how variables relate in a population

179
Q

power depends on ;

A

size of the effect that you want to detect, dispersion in the population, level of confidence, sample size

Psychology (10 decks)

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