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Mind, Brain and Behaviour 2 > Research Methods Modules > Flashcards

Research Methods Modules Flashcards

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1
Q

difference

A

is one group of people different to another in some way?

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2
Q

association

A

is one construct related to another?

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3
Q

prediction

A

does one construct influence another?

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4
Q

goal of psychological research

A

to make inferences about a population (inferring that what is typical for sample is typical for population)

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5
Q

population

A

everyone of interest to a research question

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6
Q

distributions of data can be described according to their

A

central tendency (eg. Mean) and variability (eg. Standard deviation)

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7
Q

the normal distribution

A

majority of observations in the middle, observations reduce in frequency towards the tails, the distribution is symmetrical

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8
Q

in a normal distribution, most observations are closed to m; these scores occur more

A

frequently; typical

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9
Q

the 2s Rule of Thumb

A

in a distribution with a normal shape, 95% of scores fall within approximately 2 standard deviations from the mean. These scores are typical

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10
Q

typical scores

A

are expected and occur frequently

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11
Q

extreme scores

A

are not expected and occur infrequently

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12
Q

distribution of sample means

A

made up of the sample means from all of the random samples of a certain size (n) that could possibly be obtained from a population

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13
Q

Central Limit Theorem tell us

A

the precise characteristics of a distribution of sample means for samples of any size (n). The distribution of sample means has equal mean to the population mean, for large sample sizes, the distribution of sample means will be normal, details of standard error, as sample size increases, standard error decreases and estimation of population mean becomes more precise

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14
Q

standard error

A

standard deviation of the distribution of sample means

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15
Q

when sample is large enough, it provides

A

a reliable estimate of the population mean

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16
Q

z-test standard error formula

A

standard error = standard deviation of population/(number of people in sample)^1/2

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17
Q

we can use 2s Rule of Thumb to test if our SAMPLE MEAN

A

is typical or extreme

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18
Q

hypothesis

A

a statement that predicts that something is going to happen

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19
Q

experimental hypothesis/alternative hypothesis

A

a statement that predicts an effect (one of difference or association)

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20
Q

null hypothesis

A

predicts that nothing is happening; a hypothesis of no effect (no difference, no association)

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21
Q

only one of null/experimental hypothesis can be

A

supported by research data at any one time

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22
Q

null hypothesis statistical notation

A

H0

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23
Q

experimental hypothesis statistical notation

A

H1

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24
Q

null hypothesis significance testing

A

propose a null hypothesis that a population parameter (mean) has a particular value. Proceed assuming the null hypothesis is true. Determine the probability of the sample mean occurring if the null hypothesis is true. If the probability of the sample mean occurring is small, reject the null hypothesis. If the probability is large, do not reject the null hypothesis

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25
Q

if the probability of the sample mean occurring is small,

A

reject the null hypothesis. Evidence for a difference. Extreme sample mean

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26
Q

if the probability of the sample mean occurring is large,

A

do not reject the null hypothesis. No evidence for a difference. Typical sample mean

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27
Q

determine the probability of the sample mean occurring if the null hypothesis is true. In other words,

A

what is the likelihood of our sample mean occurring if the mean of the population really is the value we predicted in the null hypothesis?

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28
Q

how to determine the probability of the sample mean occurring if the null hypothesis is true

A

involves a statistical test based on a normal distribution of sample means with the mean we predicted in our null hypothesis. Calculating critical limits to determine if our sample mean is typical or extreme

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29
Q

the 5% Alpha Level

A

defines which sample means in a distribution of sample means are expected or typical, and which are unlikely or extreme, if the null hypothesis is true

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30
Q

when the comparison distribution is perfectly normal, the critical limits set by the 5% Alpha Level are precisely

A

+/- 1.96 standard errors from the mean. 95% of the scores are inside these limits

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31
Q

if our sample mean is inside the limits set by the 5% Alpha Level, the probability is

A

greater than 5%, and therefore high (do not reject the null hypothesis)

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32
Q

if our sample mean is outside the limits set by the 5% Alpha Level, the probability is

A

less than 5%, and therefore low (reject the null hypothesis)

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33
Q

single sample z-test

A

how we determine the probability of our sample mean occurring after setting an Alpha Level of 5%

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34
Q

z-score

A

how many standard errors our sample mean is away from the null hypothesis

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35
Q

single sample z-test formula

A

z-score for sample mean = (sample mean - population mean)/(z-test standard error)

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36
Q

in single sample z-tests, the population standard

A

deviation is known

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37
Q

once z-score has been calculated, check whether it is more extreme than

A

+/- 1.96

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38
Q

if z-score is more extreme than +/- 1.96, the probability of sample mean occurring assuming the null hypothesis is true is

A

less than the Alpha Level of 5%, so probability is low and null hypothesis is rejected

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39
Q

steps for determining whether sample mean provides evidence to support null hypothesis or not

A

set Alpha Level (5%) then calculate z-score

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40
Q

we can’t use a single sample z-test and the normal distribution when the

A

population standard deviation isn’t known

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41
Q

we use the t-test and ‘t-distribution’ when

A

the population standard deviation isn’t known

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42
Q

in single sample t-tests, we use the sample standard deviation as an

A

estimate of the population standard deviation

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43
Q

t-test standard error formula

A

standard error = standard deviation of sample/(number of people in sample)^1/2

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44
Q

t-test formula

A

t-score for sample mean = (sample mean - population mean)/(t-test standard error)

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45
Q

almost all aspects of the process are the same when conducting a

A

single sample z-test or t-test

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46
Q

in the t-distribution, critical limits corresponding to Alpha Level of 5% will not be fixed at

A

+/- 1.96 as in z-test

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47
Q

t-distributions require that we consider

A

sample size and degrees of freedom (df)

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48
Q

degrees of freedom

A

one less than our sample size for single sample t-test (n-1)

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49
Q

critical limit in single sample t-test varies

A

along with df

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50
Q

to check if t-score is more extreme than critical limit taking df into account, use

A

SPSS or look up in back of textbook

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51
Q

Alpha Level of 5% still applies in t-tests, it’s just that we can’t automatically assume

A

critical limits of +/- 1.96

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52
Q

test value in SPSS value is the

A

null hypothesis value

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53
Q

sig in SPSS output is the

A

probability of our sample mean occurring (in %)

54
Q

correlational research design

A

examines relationship between two variables

55
Q

in correlational research, each participant provides how many pieces of data?

A

2

56
Q

in correlational research, a ______ observation is made

A

simple

57
Q

in correlational research, there is no

A

control or manipulation

58
Q

correlational research examines

A

ASSOCIATION

59
Q

positive linear association

A

as scores on x increase, scores on y also increase. High scores on x are related to high scores on y

60
Q

negative linear association

A

as scores on x increase, scores on y decrease. High scores on x are related to low scores on y

61
Q

linear trends are barely or not observable if

A

weak or no association exists

62
Q

correlation does not tell us about

A

direction of effect/causation

63
Q

in correlation, there is no

A

IV or DV

64
Q

in correlational research, it is possible that another

A

variable explains an observed relationship

65
Q

Pearson’s Correlation Coefficient (r)

A

measures the strength of the linear relationship between x and y

66
Q

the value of r lies between

A

+1 and -1

67
Q

the size of r specifies how close the data is to

A

a straight line

68
Q

the sign of r (+/-) specifies the

A

direction of the association

69
Q

.10 r value

A

weak (small effect)

70
Q

.30 r value

A

moderate (medium effect)

71
Q

.50 r value

A

strong (large effect)

72
Q

r values close to 1, whether negative or positive, tell us that the correlation is

A

quite close to being a straight line

73
Q

r values of zero mean

A

no correlation, so scores closer to zero are weaker

74
Q

Pearson’s r procedure in JASP tells us

A

the strength and direction of the correlation, and if we can infer that an association observed in a sample (r) is also present in the population (‘rho’)

75
Q

if r is large enough (so that it is extreme in a distribution of sample correlation coefficients), we can

A

infer an association between two variables in a population and reject the null hypothesis

76
Q

rho (fancy p)

A

correlation in the population

77
Q

null hypothesis for Pearson’s (r) analysis

A

rho = 0

78
Q

experimental hypothesis for Pearson’s (r) analysis

A

rho is not zero

79
Q

for Pearson’s (r) analysis in JASP, if statistical significance (p) is less than 0.05, we can

A

infer correlation in population

80
Q

independent groups research design

A

participants are assigned to, or come from, two or more different groups

81
Q

research question for independent groups research design

A

is there a difference between the groups?

82
Q

independent samples t-test

A

is there a significant difference between the means of the two groups?

83
Q

null hypothesis for independent samples t-test

A

no effect, no difference between means of two groups. The population mean of each group would be equivalent

84
Q

experimental hypothesis for independent samples t-test

A

of effect. The population mean of each group would be different (one bigger than the other)

85
Q

the experimental hypothesis in an independent samples t-test is actually predicting

A

that the two groups come from different populations

86
Q

in an independent samples t-test, if the p-value is <0.05,

A

reject the null hypothesis, a statistically significant difference has been found

87
Q

Cohen’s d

A

effect size

88
Q

little overlap between groups (independent samples t-test)

A

evidence of different populations

89
Q

lots of overlap between groups (independent samples t-test)

A

evidence of same population

90
Q

independent samples t-tests assess if

A

the difference between two sample means is different to zero OR if one sample mean is < or > than another

91
Q

the JASP output for independent samples t-test gives

A

descriptives, t-score, p-value, effect size, and more

92
Q

the JASP output for independent samples/repeated measures t-tests gives

A

descriptives, t-score, p-value, effect size, and more

93
Q

control groups

A

do not receive the experimental treatment

94
Q

types of t-tests

A

single sample t-test, independent samples t-test, repeated measures/paired samples/related samples t-test

94
Q

repeated measures research design

A

each participant is measured on two or more different occasions

95
Q

research question for repeated measures research design

A

is there a change across time?

96
Q

in a repeated measures research design, we use the same measurement/test

A

at times 1 and 2 to ask ourselves if the mean scores of the two samples of data are statistically significantly/meaningfully different

97
Q

repeated measures t-tests are also known as

A

paired samples or related samples t-tests

98
Q

repeated measures t-tests

A

is there a significant difference between the means at time 1 (before) and time 2 (after)? Whether the difference between the T1 and T2 means (on the same measure) is different to zero OR if one sample mean is < or > than the other

99
Q

null hypothesis for repeated measures t-test

A

mean of population at T1 is same at T2; mean of population at T2 subtracted from T1 equals zero

100
Q

experimental hypothesis for repeated measures t-test

A

mean of population at T1 minus mean of population at T2 will not equal zero

101
Q

for repeated measures t-test JASP data, if the middle score for the second boxplot is higher/lower,

A

the median has increased

102
Q

in a repeated measures t-test, if median difference in difference scores is

A

higher than zero, the mean of different scores is different to zero; the treatment works

103
Q

when we find a significant difference with regard to the construct in a repeated measures t-test,

A

there is evidence of different populations (less overlap)

104
Q

when we don’t find a significant difference with regard to the construct in a repeated measures t-test,

A

there is not evidence of different populations (more overlap)

105
Q

number of sample groups in single sample research design

A

one

106
Q

number of measurements taken from each group in single sample research design

A

one

107
Q

single sample research design measures/compares what?

A

sample to population mean

108
Q

repeated measures research design measures/compares what?

A

difference between T1 and T2

109
Q

number of sample groups in repeated measures research design

A

one

110
Q

number of measurements taken from each group in repeated measures research design

A

two

111
Q

independent groups research design measures/compares what?

A

group 1 and group 2

112
Q

research question example for independent groups

A

does G1 differ from G2?

113
Q

research question example for repeated measures

A

does T1 differ from T2?

114
Q

research question example for single sample research design

A

does sample differ from population mean?

115
Q

number of sample groups in independent groups research design

A

two

116
Q

number of measurements taken from each group in independent groups research design

A

one

117
Q

correlational research design measures/compares what?

A

extent to which two variables co-occur

118
Q

research question example for correlational research design

A

is V1 associated with V2?

119
Q

number of sample groups for correlational research design

A

one

120
Q

number of measurements taken from each group correlational research design

A

two

121
Q

reliability

A

does the measurement yield consistent, dependable, and error-free information

122
Q

validity

A

does the measurement assess what it is intended to assess and is it useful

123
Q

internal consistency (part of reliability)

A

do the components of the test all cohere? All test items should correlate with each other

124
Q

inter-rater reliability

A

does the test give the same information about the person when different people administer it?

125
Q

re-test reliability

A

does the test yield similar scores when it is administered to the same person on different occasions?

126
Q

high reliability =

A

high consistency = low measurement error

127
Q

does the test measure what it is intended to measure?

A

content, convergent, and discriminant validity

128
Q

does the test provide practically useful information?

A

predictive validity

129
Q

if reliability is low,

A

validity cannot be high

130
Q

unreliability exists when

A

there is inconsistency in what the test measures

131
Q

invalidity exists when

A

the test does not measure what it should

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