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EPPP > Research Design & Statistics > Flashcards

Research Design & Statistics Flashcards

1
Q

aim of science

A

discover systematic explanations for and/or rules governing natural phenomena

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2
Q

research

A

conduct systematic investigations and inquiries into the phenomenon (or phenomena) in question

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3
Q

research design

A

plan that specifies the research strategy — how subjects will be selected, how variables will be defined and measured, the conditions under which the research will be conducted, etc.

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4
Q

basic sequence of a scientific inquiry

A

1) hypothesis (or proposition) regarding the relationship between 2+ variables, is formulated
2) hypothesis is operationally defined (specify what exactly we should observe if the hypothesis is true)
3) collect and analyze data to test the hypothesis

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5
Q

variable

A

simply anything that varies;
not consistent or having a fixed pattern; liable to change

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6
Q

constant

A

something that does not vary;
factors that do not change during the experiment

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7
Q

independent variable (IV)

A

input variable — the event or treatment manipulated by the researcher

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8
Q

other names for IV

A

the treatment variable or experimental variable

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9
Q

dependent variable (DV)

A

is the outcome variable;
what is hypothesized to change as a result of manipulations of the independent variable;
measured to determine if they change as the result of the experimental manipulations

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10
Q

correlational research

A

investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them;
variables are measured not manipulated;
finding an association, not causation;
can be used to predict status on another variable

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11
Q

predictor variable

A

variable that is suspected to predict or correlate with an outcome variable

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12
Q

criterion variable

A

the outcome, result, or effect that researchers try to predict or explain in a study

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13
Q

levels

A

when applied to a variable, refers to the values it could take

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14
Q

factor design

A

statistical method used in experimental research that helps you study the effects of multiple factors simultaneously;
each level of one independent variable is combined with each level of the others to produce all possible combinations

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15
Q

internal validity

A

possible to determine whether a causal relationship exists between the IV and DV;
reasonably sure that the IV, rather than an extraneous (irrelevant) variable, is causally responsible for any observed change in the DV

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16
Q

one-group, pretest/post-test design

A

the dependent variable is measured once before the treatment is implemented and once after it is implemented;
subjects in one group are measured before and after they receive a treatment;
poor internal validity

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17
Q

extraneous variable

A

any variable not being investigated that has the potential to affect the outcome of a research study;
any factor not considered an independent variable that can affect the dependent variables or controlled conditions

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18
Q

confounded

A

experiment that is contaminated by an extraneous variable

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19
Q

equivalence

A

ensure that all the groups involved in a study are equivalent in every respect, except for their status on the IV

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20
Q

Threats to Internal Validity

A

history, maturation, testing, instrumentation, statistical regression, selection, differential mortality, experimental bias
TISSDEMH

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21
Q

history

A

any external event, besides the experimental treatment, that affects scores or status on the dependent variable

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22
Q

maturation

A

any internal (biological or psychological) change that occurs in the subjects while the experiment is in progress and exerts a systematic effect on the DV;
fatigue, boredom, hunger, physical or intellectual development

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23
Q

testing

A

testing is always a threat to internal validity in the one-group pretest/post-test design;
when the pretest and post-test are similar, subjects may show improvement on the post-test simply from their experience with the pretest

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24
Q

instrumentation

A

when the nature of the measuring instrument has changed;
raters’ assessment abilities have improved over time;
one way to control for this threat is to use highly reliable (dependable and consistent) measuring instruments

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25
Q

statistical regression

A

tendency of extreme (very high or very low) scores to fall closer to the mean (average) upon re-testing;
6YO child scored 180 IQ, will likely have a lower score 3 years later;
can threaten internal validity whenever extreme scorers are used as research subjects (v depressed individuals)

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26
Q

selection

A

pre-existing subject factors that account for scores on a DV
motivation, intelligence, self-esteem, etc.

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27
Q

differential mortality

A

when people who drop-out of one of the groups differ in systematic ways from people who remain in the study;
when a study involves 2+ groups

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28
Q

experimenter bias

A

behavior of subjects changes as a result of experimenter expectancies, rather than as a result of the independent variable;
ex: researcher may unconsciously communicate expectations to the subjects; researcher, consciously or unconsciously, makes errors in the direction of the research hypothesis when scoring or reporting the results

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29
Q

Rosenthal and Jacobson (1968) “Pygmalion in the Classroom”

A

teacher’s preconceived notions of a student’s ability resulted in the student’s grades and even IQ scores moving in the expected direction, even though the students themselves hadn’t changed

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30
Q

experimenter expectancy

A

AKA Rosenthal effect and the Pygmalion effect;
how the perceived expectations of an observer can influence the people being observed

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31
Q

how to overcome experimenter bias effects

A

using the “double-blind” technique, in which neither the subjects nor the experimenter know which group (experimental or control) subjects have been assigned to

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32
Q

double-blind technique

A

neither the subjects nor the experimenter know which group (experimental or control) subjects have been assigned to

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33
Q

random assignment (or randomization)

A

for all subjects in the experiment, the probability of being assigned to a particular group is the same;
considered the most “powerful” method for controlling extraneous variables;
all extraneous characteristics (including ones the researcher has not measured or even thought of) should be distributed to the groups equally

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34
Q

Random Assignment vs. Random Selection

A

random selection: method of selecting subjects into a research study; all members of the population under study have an equal chance of being selected to participate in the research

random assignment: something that takes place after the subjects have been selected; the probability of subjects who have already been selected being assigned to each group is the same

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35
Q

matching

A

identifying subjects (through a pretest) who are similar in terms of their status on the extraneous variable, then grouping similar subjects and randomly assigning members of the matched group to the treatment groups

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36
Q

when is matching useful

A

when the sample size is small;
random assignment cannot be counted on to ensure equivalency among the groups in term of the extraneous variable

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37
Q

blocking

A

studying the effects of an extraneous variable (a pre-existing subject characteristic) to determine if and to what degree it is accounting for scores on the DV;
making the extraneous variable another IV

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38
Q

matching vs. blocking

A

matching: ensure equivalency in terms of the extraneous variable; doesn’t add an IV
blocking: determine the effects of the extraneous variable; add a new IV and, therefore, add additional experimental groups

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39
Q

Holding the Extraneous Variable Constant

A

including only subjects who are homogenous in terms of their status on the extraneous variable;
completely eliminates the effects of an extraneous variable;
con: cannot be generalized to populations that are not sampled

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40
Q

analysis of covariance (ANCOVA)

A

statistical strategy for increasing internal validity;
after the data are obtained, DV scores are adjusted so that subjects are equalized in terms of their status on one or more extraneous variables

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41
Q

external validity

A

the generalizability of the results of a research study to other settings, times, or people

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42
Q

interaction

A

some variable has one effect under one set of circumstances, but a different effect under another set of circumstances;
term implies that a given effect is not generalizable; that is, it doesn’t work the same way under all circumstances

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43
Q

interaction between selection and treatment

A

effects of a given treatment would not generalize to other members of the population of interest (or target population)

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44
Q

Interaction Between History and Treatment

A

effects of a treatment do not generalize beyond the setting and/or time period in which the experiment was done

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45
Q

Interaction Between Testing and Treatment

A

results of research in which pretests are used might not generalize to cases in which pretests are not used

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46
Q

pretest sensitization

A

effect in which the administration of a pretest affects the subsequent responses of a participant to experimental treatments

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47
Q

demand characteristics

A

cues in the research setting that allow subjects to guess the research hypothesis

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48
Q

The Hawthorne effect

A

tendency of subjects to behave differently due to the mere fact they are participating in research

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49
Q

Order Effects (AKA Carryover Effects and Multiple Treatment Interference)

A

when participants’ responses in the various conditions are affected by the order of conditions to which they were exposed;
effect of being tested in one condition on participants’ behavior in later conditions

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50
Q

repeated measures design

A

studies in which the same subjects are exposed to more than one treatment

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51
Q

random selection

A

all members of the population under study have an equal chance of being selected to participate in the research

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52
Q

stratified random sampling

A

taking a random sample from each of several subgroups of the total target population;
purpose is to ensure proportionate representation of the defined population subgroups

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53
Q

cluster sampling

A

unit of sampling is a naturally occurring group of individuals, rather than the individual

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54
Q

multistage cluster sampling

A

taking of samples in stages using smaller and smaller sampling units at each stage

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55
Q

naturalistic observation

A

behavior is observed and recorded in its natural setting or in a setting as similar to the natural one as possible;
lacks internal validity

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56
Q

analogue research

A

results of lab research studies are used to draw conclusions about a real-world phenomenon;
the researchers made analogies about real-world phenomena based on studies involving contrived, laboratory situations ;
good internal, bad external validity

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57
Q

single-blind study

A

subjects are not informed of the purpose of the study and do not know which treatment they have been assigned to

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58
Q

counterbalancing

A

different subjects or groups of subjects receive the treatments in a different order;
to control for order effects

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59
Q

latin square design

A

ordering the administration of treatments so that each appears once and only once in every position

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60
Q

true experiment

A

investigator randomly assigns subjects to different groups, which receive different levels of a manipulated variable;
greatest internal validity

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61
Q

quasi-experimental designs

A

used when random assignment of subjects to groups is not possible;
involves the use of intact groups, rather than groups that are constructed on the basis of random assignment

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62
Q

developmental studies

A

assessing variables as a function of time (e.g., physical and psychological development)

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63
Q

3 types of developmental designs

A

longitudinal, cross-sectional, and cross-sequential

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64
Q

longitudinal study

A

same people are studied over a long period of time

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65
Q

cons of longitudinal studies

A

high cost (time and money); high subject dropout rates; and, in studies that involve assessing performance on a task, practice effects

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66
Q

why longitudinal designs tend to underestimate true age-related change

A

1) subjects who drop out of longitudinal designs tend to be those who are less able on the task studied, leaving the remaining subjects will be relatively high in ability, and the data will show a misleadingly low level of age-related decline;
2) practice effects can facilitate performance on the dependent variable

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67
Q

cross-sectional design

A

different groups of subjects, divided by age, are assessed at the same time;
tend to overestimate true age-related declines in performance

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68
Q

cohort effects (AKA intergenerational effects)

A

observed differences between different age groups may have to do with experience rather than age

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69
Q

cross-sequential design

A

representative samples of different age groups are assessed on two or more occasions

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70
Q

time-series design

A

taking multiple measurements over time (usually multiple pretest and post-test measures) to assess the effects of an IV;
the series of measurements on the DV is interrupted by the administration of a treatment

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71
Q

advantage of multiple measurements

A

allow one to rule out many threats to internal validity, such as maturation, regression, and testing;
biggest threat is history

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72
Q

two-group time-series design

A

take the same measurements from a comparison “control” group that is comparable to the one studied

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73
Q

Single-Subject Designs

A

number of subjects is one;
well-suited to research on behavior modification since the researcher is able to analyze the behavior before and during treatment - DV is measured several times during both phases

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74
Q

types of single subject designs

A

“AB” design, “reversal” design, and “multiple baseline” design

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75
Q

AB design

A

involves a single baseline phase and a single treatment phase;
con: easy for any observed change in behavior in the treatment phase to be due to a historical event or other extraneous factor

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76
Q

Reversal (or Withdrawal) Design

A

treatment is withdrawn and data are collected to determine if the behavior returns to its original level upon this withdrawal;
ABAB design, in which the treatment is re-applied after the second baseline phase

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77
Q

Multiple-baseline designs

A

when cannot use reversal design;
applying the treatment sequentially (across different baselines);
treatment may be applied sequentially across different behaviors of the same subject (multiple baseline across behaviors), to the same subject in different settings (multiple baseline across settings), or to the same behavior of different subjects (multiple baseline across subjects)

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78
Q

qualitative or descriptive research

A

type of research in which the investigator doesn’t start with a theory; theory is developed from the data rather than derived a priori (beforehand)

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79
Q

qualitative methods of research

A

participant observation, nonparticipant observation, interviews, surveys, case studies

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80
Q

surveys

A

used in areas such as attitude measurement, consumer preferences, and worker satisfaction studies;
3 basic techniques - personal interviews, telephone surveys, and mail surveys

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81
Q

case study

A

detailed examination of a single case (single individual, group, or phenomenon);
based on the assumption that the case under study can be viewed as an example of a more general class;
from an experimental POV, case studies don’t allow one to conclude the nature of relationships between variables (lack internal validity), and their results may not be generalizable to other cases (may lack external validity

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82
Q

protocol analysis

A

loosely applies to research involving the collection and analysis of verbatim reports;
subject is asked to think aloud as he or she is performing a task while the researcher records everything the subject says (this record is referred to as a protocol);
researcher analyzes the data in an attempt to identify cognitive processes involved in performing the task;
analysis is based on the researcher’s interpretation of the verbal protocol

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83
Q

statistics

A

methods of measuring variables and organizing and analyzing data

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84
Q

descriptive statistics

A

describe a set of data collected from a sample

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85
Q

inferential methods

A

used to make inferences about an entire population on the basis of sample data

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86
Q

nominal

A

divides a variable into unordered categories into which the data may fall;
qualitative data that groups variables into categories that do not overlap;
categories are not ordered;
“sex,” “diagnostic category,” “hair color”

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87
Q

ordinal

A

variables have natural, ordered categories and the distances between the categories are not known;
Category 1 has less (or more) of the given attribute than Category 2;
ranks, satisfactory ratings, education

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88
Q

interval

A

numbers are scaled at equal distances, but the scale itself has no absolute zero point;
measured along a numerical scale that has equal distances (intervals) between adjacent values;
can add and subtract but can’t multiply or divide;
IQ, temperature

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89
Q

ratio

A

identical to interval scales, except they have an absolute zero point;
multiplication and division require a ratio scale;
money, distance, time

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90
Q

3 types of descriptive statistics

A

frequency distributions, measures of central tendency, measures of variability

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91
Q

frequency distribution

A

provides a summary of a set of data;
indicates the number (frequency) of cases that fall at a given category or score or within a given score range;
can be graphically displayed on a table, polygon, bar graph (histogram)

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92
Q

cumulative frequency (cf)

A

total number of observations that fall at or below the given category or score

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93
Q

histogram

A

scores are plotted on the x-axis (or abscissa), and frequency of occurrence of each score is plotted on the y-axis (or ordinate)

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94
Q

normal distribution

A

data are symmetrically distributed with no skew;
most values cluster around a central region, with values tapering off as they go further away from the center

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95
Q

negatively skewed

A

larger proportion of the scores falls toward the high end of the scale and relatively few scores fall toward the low end of the range of scores;
has a long tail on the left (the negative end of the distribution) and “lump” of scores on the right;
negatively skewed = easy test

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96
Q

positively skewed

A

larger number of scores at the low end of the scale (to the left side of the range of scores) and a long tail to the right (the positive end);
positively skewed = difficult test

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97
Q

mean

A

arithmetic average;
most useful measure of central tendency;
very sensitive to extreme values

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98
Q

median (Md)

A

middle value of the data when ordered from the lowest to the highest;
more useful measure of central tendency when a distribution is skewed

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99
Q

mode

A

most frequent value in a collection of numbers

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100
Q

multimodal

A

distribution with multiple modes

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101
Q

bimodal distribution

A

distribution with two modes

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102
Q

Relationship Between the Mean, the Median, and the Mode

A

normal distribution: 3 measures are equal;
positively skewed distribution: mean > median > mode;
negatively skewed distribution: mode > median > mean

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103
Q

variability

A

dispersion;
how spread-out scores are

104
Q

3 most commonly used measures of variation

A

range, variance, and standard deviation

105
Q

range

A

difference between the highest and lowest scores in a set;
general description of a distribution’s variability

106
Q

cons of range

A

affected by outliers;
no info about the distribution of the scores across the range (how bunched up or variable scores are around the mean)

107
Q

variance

A

average of the squared differences of each observation from the mean;
measure of how the scores disperse around the mean;
equal to the square of the standard deviation

108
Q

standard deviation

A

the square root of the variance;
the expected deviation from the mean of a score chosen at random;
can be used to calculate the percentage of scores that will fall within a given range, as well as the percentage that will fall above or below a given cutoff score

109
Q

transformed score

A

allow an individual raw score to be compared to scores in the rest of the distribution

110
Q

4 types of transformed scores

A

z-scores, T-scores, stanines, and percentile ranks

111
Q

z-scores

A

AKA standard scores;
raw scores stated in standard deviation terms;
measure of how many standard deviations a given raw score is from the mean

112
Q

linear transformation

A

transformation of scores in which the distribution’s shape does not change;
conversion of raw scores to z-scores (and vice-versa) is a linear transformation

113
Q

t-score

A

based on 10-point intervals with T = 50 being the distribution’s mean and every 10 points above or below 50 equivalent to a standard deviation away from the mean

114
Q

stanine scores

A

divide the distribution into 9 equal intervals, with stanine 1 being the lowest ninth of the distribution and stanine 9 being the highest ninth;
mean = 5, SD of about 2

115
Q

percentile rank

A

the percentage of individuals in the standardized group scoring below the individual’s attained raw score;
have a flat (or rectangular) distribution - within a given range of percentile ranks, there will always be the same number of scores

116
Q

difference between percentage score and percentile rank

A

percentage score is referenced to items on the test;
percentile rank is referenced to other scores in the distribution

117
Q

nonlinear transformation

A

transformation that results in a change of the distribution’s shape

118
Q

memorize the following points about the standard deviation curve

A

1) In a normal distribution, about 68% of all scores fall between -1.0z and +1.0z.
2) In a normal distribution, about 95% of all scores fall between z-scores of -2.0 and +2.0.
3) In a normal distribution, the z-score of +1.0 is equivalent to a percentile rank (PR) of 84 and is therefore the cutoff point for the top 16%. Conversely, the z-score -1.0 is equivalent to a PR of 16 and is therefore the cutoff point for the bottom 16%.
4) In a normal distribution, the z-score of +2.0 is approximately equivalent to the 98th percentile and is therefore the cutoff point for about the top 2%. Conversely, the z-score -2.0 is approximately equivalent to a PR of 2 and is therefore the cutoff point for the bottom 2%.

119
Q

why in a normal distribution, there is a far greater range of percentile ranks contained in the middle of the distribution than at either extreme

A

a change in an individual’s raw score in the middle of the distribution results in a much greater change in his or her percentile rank than the same raw score change at the distribution’s extreme

120
Q

sampling error

A

the inaccuracy of a sample value;
the difference between a sample value (a statistic) and the corresponding population value (a parameter)

121
Q

sample mean

A

an estimate of a population mean

122
Q

error of the mean

A

difference between a sample mean and the population mean

123
Q

standard error of the mean

A

refers to the expected error of a given sample mean;
indicates the extent to which a sample mean can be expected to deviate from its corresponding population mean

124
Q

standard error of the mean formula

A

SEmean = s.d. / √N

125
Q

why as the sample size increases, the standard error of the mean becomes smaller

A

the larger the sample size, the more we approximate the size of the population, and thus, the closer the sample statistic will be to the population parameter

126
Q

purpose of statistical hypothesis testing

A

quantitatively test a research hypothesis

127
Q

null hypothesis

A

hypothesis of no difference;
the IV does not have an effect on the DV;
implies that the sample means are drawn from the same population

128
Q

alternative hypothesis

A

that the IV does have an effect on the DV, or that the means of the populations of interest on the DV are not equal;
sample means are sufficiently different to conclude that they come from different populations

129
Q

two-tailed hypothesis

A

a mean (or means) is different from another mean (or other means), but we do not know in which direction;
psychotherapy will change the scores

130
Q

one-tailed hypothesis

A

a mean (or means) is either greater than or less than another mean (or other means);
psychotherapy will improve scores

131
Q

power

A

probability of rejecting the null hypothesis when, in fact, it is false;
probability of making a correct decision (reject the null hypothesis) when the null hypothesis is false;
probability of declaring that there is a difference when one really exists;
probability of NOT making a Type II error

132
Q

Type I error

A

when the null hypothesis is rejected when it is true;
concludes that a difference exists when it really doesn’t;
“thinking you have something when you really don’t”

133
Q

Type II error

A

failure to reject the null hypothesis when it is false;
stating that we don’t have a sufficient difference to reject the null hypothesis, when in fact a real difference does exist;
“thinking you don’t have something when you really do”

134
Q

alpha level

A

level of significance;
probability of making a Type I error is equivalent to the alpha level - researchers determine in advance - .01 or .05 level;
if results indicate that there’s only a 5% or less chance of the null hypothesis being true, then the researcher will reject the null - conclude that it is false

135
Q

retention region

A

area of a graph where you would accept the null hypothesis if your test results fall into that area;
if results fall into that area then they are NOT statistically significant

136
Q

rejection region

A

AKA critical region;
area of a graph where you would reject the null hypothesis if your test results fall into that area;
if results fall into that area then they are statistically significant

137
Q

significance level

A

the probability at which we reject the null hypothesis as being true;
when the results of a statistical test are significant at the predetermined alpha level, the null hypothesis is rejected

138
Q

beta (β)

A

probability of making a Type II error

139
Q

factors that affect power

A

1) Sample Size: The larger the sample size, the greater the power.
2) Alpha: As the pre-set alpha level increases, power increases - higher the alpha, easier to reject null hypothesis.
3) Directional and Non-Directional Statistical Tests: One-tailed (directional) tests are more powerful than two-tailed (nondirectional) tests.
4) Magnitude of the Population Difference: The greater the difference between the population means under study, the more likely the researcher will be able to detect these differences (the more power).

140
Q

parametric tests

A

used for interval and ratio data;
t-test and ANOVA

141
Q

Parametric tests are based on the following assumptions

A

1) Normal Distribution: assumes the DV values are normally distributed in the population.
2) Homogeneity of Variance: variance of all groups is equal. This is referred to as the homogeneity of variance assumption.
3) Independence of Observations: scores within the same sample or group should not be correlated with each other.

142
Q

nonparametric tests

A

test hypotheses based on DVs that are measured on an ordinal or nominal scale;
chi-square and Mann-Whitney U

143
Q

unbiased sample

A

sample that is representative of the population

144
Q

critical value

A

the value of the test statistic which defines the upper and lower bounds of a confidence interval, or which defines the threshold of statistical significance in a statistical test

145
Q

factors that impact chosen critical value

A

1) the pre-set alpha level (e.g., .01 or .05)
2) the degrees of freedom for the statistical test

146
Q

types of parametric tests

A

t-test, one-way ANOVA, factorial ANOVA, and MANOVA

147
Q

t-test

A

used to test hypotheses about two different means

148
Q

3 types of t-tests

A

the one sample t-test, the t-test for independent samples, and the t-test for correlated samples

149
Q

t ratio

A

used for t-tests;
when statistically significant, this indicates that the two means are significantly different and the null hypothesis is therefore rejected

150
Q

One Sample t-Test

A

designed to compare the mean of a single sample to a known population mean;
df = N-1

151
Q

t-Test for Independent Samples

A

used to compare two means derived from independent (unrelated) samples;
df = N-2

152
Q

t-Test for Correlated Samples

A

performed when the samples consist of matched pairs of similar units, or when there are cases of repeated measures;
matched samples or pretest-posttest;
df - N-1, where N = number of pairs of scores

153
Q

One-Way Analysis of Variance (ANOVA)

A

study with one independent variable where the means of more than two groups are compared;
What is the probability that these means are from the same population?

154
Q

F ratio

A

if the value of F is statistically significant, then the means are significantly different and the null hypothesis is rejected;
represents a comparison between two estimates of variance

155
Q

between-group variance (or treatment variance)

A

the degree to which the groups as a whole differ from one another

156
Q

within-group variance (or error variance)

A

the degree to which subjects within experimental groups differ from each other

157
Q

ANOVA statistic is a fraction

A

Variance Between Groups/Variance Within Groups;
if the top term is large and the bottom term is small, we have a large ANOVA statistic, and that’s good - indicates that our treatment had an effect - the difference between the group mean scores is too large to be accounted for by “error” or by the individual differences that will be found even within the same population

158
Q

sum of squares

A

measure of the variability of a set of data

159
Q

df between (dfb)

A

k-1, where k equals the number of groups

160
Q

df within (dfw)

A

N-k, where N equals the total number of subjects

161
Q

mean square

A

statistical measure used to estimate between- and within-group variance

162
Q

Mean Square Between (MSB)

A

Sum of Squares between divided by the df between

163
Q

Mean Square Within (MSW)

A

Sum of Squares within divided by the df within

164
Q

f-ratio formula

A

F = MSB / MSW;
mean square between / mean square within

165
Q

post-hoc tests

A

indicate exactly which means significantly differ from each other in ANOVA;
involves making pairwise or complex comparisons between means

166
Q

pairwise comparison

A

comparison between two means

167
Q

con of multiple comparisons

A

the more comparisons that are done, the higher the probability that at least one Type I error (incorrect rejection of the null) will be made

168
Q

you should know the following about the specific post-hoc tests

A

1) Of all the post-hoc tests, the Scheffe is the most conservative - provides the greatest protection against the inflation in the Type I error rate that occurs when multiple comparisons are made (but that in turn increases the risk of Type II)
2) If conducting pairwise comparisons, the Tukey is the appropriate post-hoc test

169
Q

factorial ANOVA

A

used when a study has 2 or more independent variables;
permits the assessment of both main effects and interaction effects

170
Q

MANOVA

A

used when a study has two or more dependent variables

171
Q

main effect

A

the effect of one independent variable by itself without considering the other independent variable;
can be seen by examining the difference between the marginal means

172
Q

interaction effect

A

the effects of an independent variable at the different levels of the other independent variables;
the effect of one IV depends on which level of the other IVs you are at

173
Q

cell mean

A

means inside the boxes in statistical analyses

174
Q

complex comparisons

A

comparisons involving combined means

175
Q

factorial ANOVA for repeated measures

A

all levels of all independent variables are applied to a single group of subjects

176
Q

mixed ANOVA (split-plot ANOVA)

A

has at least one between-subjects independent variable and at least one repeated measures variable (or within-subjects) variable

177
Q

chi-square test

A

used when frequencies, or the number of subjects within each category (as opposed to the mean scores on a measure), are given;
assess whether these observed frequencies differ from the expected frequencies;
compares observed frequencies of observations within nominal categories to frequencies that would be expected under the null hypothesis

178
Q

chi-square (X2)

A

statistic that indicates whether the obtained frequencies in a set of categories differ significantly from what is expected under the null hypothesis

179
Q

single-sample chi-square test

A

collecting categorical data from only one sample of individuals;
df equals C-1, where C represents the number of categories

180
Q

multiple-sample chi-square test

A

adding another variable in addition to the one that gives rise to the classification categories;
df equals (C-1)(R-1), where C represents the number of categories; R = the number of rows (# of levels of the second variable)

181
Q

Cautions in Using Chi-square

A

1) All observations must be independent of each other: No “before and after” studies
2) Each observation can be classifiable into only one category or cell: Mutually exclusive
3) Percentages of observations within categories cannot be compared

182
Q

Calculating Expected Frequencies in single-sample Chi-Square

A

dividing the total number of subjects by the number of cells

183
Q

Calculating Expected Frequencies in multiple-sample Chi-Square

A

fe = (column total)X(row total) / total N
where fe = the expected frequency for any cell;
column total = the sum of observations within a column containing that cell;
row total = the sum of observations within a row containing that cell;
total N = total number of subjects

184
Q

Mann-Whitney U Test

A

compare two independent groups on a dependent variable measured with rank-ordered data

185
Q

Wilcoxon Matched-Pairs Test

A

compare two correlated groups on a dependent variable measured with rank-ordered data

186
Q

Kruskal-Wallis Test

A

compare two or more independent groups on a dependent variable with rank-ordered data

187
Q

nonparametric alternative to Wilcoxon Matched-Pairs Test

A

t-test for correlated samples

188
Q

nonparametric alternative to Kruskal-Wallis Test

A

one-way ANOVA

189
Q

nonparametric alternative to Mann-Whitney U Test

A

t-test for independent samples

190
Q

correlation

A

a relationship between two or more variables;
measure their “co-relation”, or the degree to which they co-vary

191
Q

correlation coefficient

A

measures the correlation between two variables;
tells its magnitude and its direction;
ranges between -1.00 and +1.00

192
Q

positive coefficient

A

indicates that the two variables move in the same direction

193
Q

negative correlation

A

indicates that as one variable goes up, the other goes down

194
Q

scattergram

A

relationship between two variables can be depicted on a graph;
an individual point represents the scores obtained by one individual on two measures

195
Q

Pearson r

A

calculating the relationship between two variables that are measured on an interval or ratio scale

196
Q

Factors Affecting the Pearson r

A
  1. Linearity: not the appropriate correlation coefficient to assess nonlinear relationships;
  2. Homoscedasticity: heteroscedasticity will lower the Pearson r correlation coefficient;
  3. Range of Scores: the wider the range of sampled behavior, the more accurate the estimation of correlation
197
Q

scedasticity

A

refers to the way points are dispersed in a scattergram

198
Q

homoscedasticity

A

dispersion of scores is equal throughout the scattergram

199
Q

heteroscedasticity

A

more dispersion at some parts of the scattergram than at others;
he magnitude of the relationship between two variables depends on what level of the “X” or “Y” variable you are considering

200
Q

Interpretation of the Pearson r

A

square it - the square of the correlation coefficient indicates the percentage of variability in one measure that is accounted for by variability in the other measure

201
Q

point-biserial correlation

A

relates one continuous variable (interval or ratio scaled variable) and one dichotomous variable (one that can take only two values - gender)

202
Q

biserial coefficient

A

two continuous variables are correlated, with one artificially being made dichotomous (income - high vs low)

203
Q

phi coefficient

A

correlates both variables are dichotomous

204
Q

tetrachoric coefficient

A

correlates both variables are artificially dichotomized

205
Q

contingency correlation coefficient

A

correlation between two nominally scaled variables (two unordered variables, with each having more than two categories)

206
Q

Spearman’s Rho

A

used to correlate two variables that have been ordinally ranked

207
Q

eta

A

measures a nonlinear relationship

208
Q

regression equation

A

when two variables are correlated, it is possible to construct an equation that could be used to estimate the value of a “criterion” (outcome) variable based on scores on a “predictor” (input) variable

209
Q

continuous variable

A

variable that can assume an infinite number of real values within a given interval

210
Q

regression line

A

a straight line that describes how a response variable Y changes as an explanatory variable X changes

211
Q

error score

A

the difference between the predicted and the actual criterion scores;
assumed to be normally distributed with a mean of 0

212
Q

least squares criterion

A

constructing the regression line involves identifying the line that results in the least amount of error in predicting Y scores from X scores;
regression line is drawn at the location where the sum of squared distances of dots from the line is the lowest

213
Q

multiple regression

A

when two or more predictor variables are used to predict scores on one criterion

214
Q

multiple correlation coefficient, or multiple R

A

statistic to measure multiple regressions;
the predictive power of the multiple regression equation;
the higher the value of multiple R, the stronger the relationship between the combination of predictor variables and the criterion variable

215
Q

understand the following points about multiple correlation and multiple regression

A

1) multiple R is highest when predictor variables each have high correlations with the criterion but low correlations with each other;
2) multiple R is never lower than the highest simple correlation between an individual predictor and the criterion;
3) multiple R can never be negative;
4) multiple R can be squared to facilitate its interpretation

216
Q

multicollinearity

A

significant predictor overlap

217
Q

coefficient of multiple determination

A

multiple R squared;
indicates the proportion of variance in the criterion variable accounted for by the combination of predictor variables

218
Q

Stepwise Multiple Regression

A

come up with the smallest set of predictors that maximizes predictive power;
useful technique if you have a relatively large number of potential predictors, but you want to use a smaller subset of these predictors in the final

219
Q

reasons why you might want to cut back on the number of predictors used

A

1) the fewer the predictors, the less costly it is (time, money) to collect the data
2) due to multicollinearity, at some point, adding predictors results in little or no increase in predictive power

220
Q

forward stepwise regression

A

start out with one predictor, and add predictors to the equation one at a time;
with each addition, conduct an analysis to determine if the predictive power of the multiple regression equation is substantially increased;
the more commonly used type

221
Q

backward stepwise regression

A

start out with all of the potential predictors, and remove predictors one at a time

222
Q

canonical correlation coefficient

A

used to calculate the relationship between two or more predictors and two or more criterion variables

223
Q

discriminant function analysis

A

used when the goal is to classify individuals into groups on the basis of their scores on multiple predictors;
scores on two or more variables are combined to determine whether they can be used to predict which criterion group a person will belong to

224
Q

difference between discriminant function analysis and multiple regression

A

1) discriminant: the DV is discrete (i.e., finite), predict criterion group membership;
2) multiple: the DV is continuous (i.e., infinite), multiple predictors are used to estimate a person’s criterion score

225
Q

differential validity

A

each predictor has a different correlation with each criterion variable;
the computed validity coefficients are significantly different for different groups of examinees

226
Q

logistic regression

A

process of modeling the probability of a discrete outcome given an input variable;
no assumptions need to be met for this;
nominal (categorical) or continuous

227
Q

multiple cutoff

A

procedure involving setting a minimum cutoff score on a series of predictors;
if the cutoff score is not achieved on even one of the predictors, the person is not selected (college, job)

228
Q

partial correlation

A

used to assess the relationship between two variables with the effects of another variable “partialled out” (statistically removed)

229
Q

zero-order correlation

A

correlation between two variables is determined without regard to any other variables

230
Q

suppressor variable

A

suppresses the relationship between a predictor and a criterion.

231
Q

structural equation modeling

A

general term for a set of techniques that involve calculating the pairwise correlations between multiple variables;
used for the purpose of causal modeling - testing a hypothesis that posits a causal relationship among multiple (3 or more) variables;
includes path analysis and LISREL

232
Q

path analysis

A

used to verify simpler causal models that propose only one-way causal flows between variables

233
Q

LISREL

A

used when a model includes one-way and/or two-way causal relationships

234
Q

latent variable

A

one that you infer is being measured, on the basis of statistical analysis

235
Q

trend analysis

A

way of measuring the trend of change (linear, quadratic, cubic, quartic) in a DV in a repeated measures design;
indicate which (if any) trends tested for are significant;
both variables are quantitative (interval or ratio)

236
Q

break point

A

a point where scores for all subjects change direction in a predictable way (stop increasing and start decreasing, or stop decreasing and start increasing)

237
Q

population

A

the whole set of cases the researcher is interested in

238
Q

population distribution

A

one that includes every single score in the population

239
Q

sample distribution

A

set of scores obtained from a sample;
less score variability than the population distribution

240
Q

sampling distribution

A

probability distribution of a statistic (mean, median, mode) that is obtained through repeated sampling of a specific population;
less variability than the population distribution

241
Q

sampling with replacement

A

selected subjects are put back into the population before another subject are sampled

242
Q

central limit theorem

A

under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution

243
Q

robust

A

rate of false rejections of the null hypothesis (Type I) is not substantially increased by violations of these assumptions

244
Q

autocorrelation

A

the degree of correlation of a variable’s values over time

245
Q

Bayes’ Theorem

A

the probability of an event, based on prior knowledge of conditions that might be related to the event

246
Q

meta-analysis

A

method of analyzing a group of independent studies with a common conceptual basis (integrating studies of the effectiveness of psychotherapy)

247
Q

pros and cons of meta-analysis

A

Pro: allows for the consideration of the size of effects;

Cons: subject to the biases of the person doing the analysis, concentrating only on main effects and ignoring interactions results in a loss of information

248
Q

moderator

A

a qualitative (e.g., race, sex, class) or quantitative (e.g., level of reward) variable that affects the direction and/or strength of the relation between an independent or predictor variable and a dependent or criterion variable

249
Q

probands

A

individuals who are first brought to the attention of the researcher - i.e., individuals manifesting the characteristic of interest or disease

250
Q

eigenvalue

A

a statistic that indicates the degree to which a particular factor is accounting for variability in the variables studied;
indicates its strength or explanatory power

251
Q

resampling procedures

A

creation of new samples based on one observed sample;
compute a test statistic for each sample or rearrangement with the resulting set constituting the sampling distribution (often called a reference distribution) of that statistic

252
Q

permutation test

A

begin with the original data then systematically or randomly reorder (shuffle) the data, and then calculating the appropriate test statistic on each reordering

253
Q

cross-validation

A

uses a part of the available observation to fit the model, and another part to test in the computation of predication error

254
Q

pooled variance

A

weighted average variance for each group;
“weighted” based on the # of subjects in each group;
assumes that the population variances are approximately the same, even though the sample variances differ

255
Q

Solomon four-group design

A

a true experimental design used to evaluate the effects of pretesting, since some groups are pretested and others are not

EPPP (11 decks)

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