Psych 101 Exam 1

Front 1

statistics

Back 1

the science of collecting, analyzing, and interpreting data

Front 2

statistic

Back 2

a measure of some attribute of a sample

Front 3

case study

Back 3

an analysis of statistics of one element or a small sample of elements

Front 4

Non-experimental (observational) studies

Back 4

analyses that compare or measure similarities/differences within a group that we do not manipulate

Front 5

experimental studies

Back 5

analyses that allow us to compare or measure similarities/differences between groups that we manipulate

Front 6

true experimental studies

Back 6

studies comparing properties in groups that were randomly assigned

Front 7

quasi-experimental studies

Back 7

studies that compare properties of groups where assignment wasn’t possible (ex. Earthquake)

Front 8

random assignment

Back 8

every element in a group has an equal chance of being assigned to the different groups in the study

Front 9

Single blind studies

Back 9

experiments where the elements are unaware of the groups they are in

Front 10

double blind studies

Back 10

experiments where the elements and experimenters are unaware of the groups that they are in

Front 11

descriptive statistics

Back 11

numerical

Front 12

inferential statistics

Back 12

interpretations. Look at sample, make assumption about population.

Front 13

variables

Back 13

general characteristics, usually quantified, that VARY and can be used to compare or describe

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variability

Back 14

the fact that variables obtained often differ from one another

Front 15

good variability

Back 15

individual differences (due to the participants themselves)

Front 16

bad variability

Back 16

measurement error (due to the inability to measure something accurately) or unreliability (due to differences in responses to the same situation)

Front 17

construct

Back 17

a hypothetical or theoretical entity that is being explored in research (eg. emotions, learning)

Front 18

operational definition

Back 18

the systematic process of obtaining or measuring a construct

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levels

Back 19

the values that a construct can take on

Front 20

validity

Back 20

accuracy of measurement with respect to intent

Front 21

construct validity

Back 21

is it measuring what we’re interested in?

Front 22

predictive validity

Back 22

does the measure predict related behaviors/measures?

Front 23

concurrent validity

Back 23

does it relate to other measures that are supposed to be measuring the same thing?

Front 24

reliability

Back 24

consistency in measurement

Front 25

Internal reliability

Back 25

parallel forms and similar items

Front 26

continuous variables

Back 26

variables that can assume an infinite number of values

Front 27

discrete variables

Back 27

variables that have a finite set of values that they can be

Front 28

categorical (nominal) variables

Back 28

variables that have no numerical meaning

Front 29

quantitative variables

Back 29

numerical

Front 30

ordinal

Back 30

variables that have a natural order, but the precise distance between values is not defined

Front 31

interval

Back 31

variables that have values where the distance btwn them is meaningful and consistent

Front 32

ratio

Back 32

variables where there is a true zero and where ratios of values make sense

Front 33

independent variables

Back 33

All variables are independent in observational studies. Typically discrete.

Front 34

dependent variables

Back 34

caused by or changed by the independent variable

Front 35

advantages of mode

Back 35

-easy to calculate

-can be used with nominal, ordinal, interval, and ratio data

Front 36

disadvantages of mode

Back 36

-gives little info about the entire distribution

-sampling variability

Front 37

advantages of median

Back 37

-good for distributions that are skewed/have extreme outliers

-less sampling variability

Front 38

disadvantages of median

Back 38

-does not represent all of the scores in the distribution

-doesn't always tell much about discrete data

Front 39

advantages of mean

Back 39

-lowest sampling variability

-takes all scores in distribution into account

-used in higher-order calculations

Front 40

disadvantages of mean

Back 40

-the only measure of central tendency that is sensitive to outliers

-does not work with ordinal or nominal data

Front 41

population

Back 41

a complete set of people, events, or scores that we’re interested in

Front 42

parameter

Back 42

the measurable characteristic of the population that is of interest

Front 43

sample

Back 43

a subset or portion of that population

Front 44

statistic

Back 44

the measurable characteristic of the sample of the population that we’re interested in

Front 45

convenience sampling

Back 45

taking a select sample from the population that is easily accessible

Front 46

volunteer sampling

Back 46

sampling that is obtained through the willing participation of particular individuals

Front 47

true random sampling

Back 47

taking the entire population and selecting a sample randomly from that population

Front 48

cluster sampling

Back 48

random sampling of organized groups of individuals from the population

Front 49

stratified (representative) sampling

Back 49

identifying some major characteristics of interest in the population and generating a sample that is proportionally equivalent to the population

Front 50

pseudo-random sampling

Back 50

taking everyone that is accessible from a population and selecting a sample randomly from that group

Front 51

random assignment

Back 51

creating groups by giving each participant an equal chance of being in the experimental conditions/levels

Front 52

randomized block design (stratified assignment)

Back 52

creating equivalent groups based on important characteristics

Front 53

convenient assignment

Back 53

assignment of individuals based on experimenter discretion

Front 54

sample statistics

Back 54

quantities that characterize samples of raw scores

Front 55

sampling statistics

Back 55

quantities that characterize sampling distributions of statistics

Front 56

Subjective probability

Back 56

probability based on an individual’s opinion of the likelihood that an event will occur, or that an event or relationship is due to more than chance

Front 57

expected probability

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a measure of the actual probability of an outcome if the outcomes were random and repeated many times

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hypothesis testing

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statistically verifying that the probability of an outcome is significantly different from chance

Front 59

null hypothesis

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a statement that implies no effect, differences, or similarities on or between variables w/in a population of interest (results obtained were merely due to chance)

Front 60

alternative hypothesis

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a statement that implies that the null hypothesis is false

Front 61

type 1 error

Back 61

null hypothesis is TRUE, but you reject it

Front 62

type 2 error

Back 62

null hypothesis is FALSE, but you retain it