Statistics

Question 1

sample

Answer 1

A sample is selected to represent the population in a research study; helps answer questions about a population.

Question 2

variable

Answer 2

characteristic or condition that can change or take on different values.

Question 3

discrete variables

Answer 3

(such as class size) consist of indivisible categories (weird when it is represented by a fraction

Question 4

continuous variables

Answer 4

(such as time or weight) are infinitely divisible into whatever units a researcher may choose. Also, that can be legitimately measured.

Question 5

goal of experiment

Answer 5

Goal of an experiment is to demonstrate a cause-and-effect relationship between two variables; that is, to show that changing the value of one variable causes changes to occur in a second variable.

Question 6

IV and DV

Answer 6

In an experiment, the manipulated variable is called the independent variable and the observed variable is the dependent variable.

Question 7

non-experimental or quasiexperimental

Answer 7

non-experimental or quasi-experimental, are similar to experiments because they also compare groups of scores. These studies do not use a manipulated variable to differentiate the groups. Instead, the variable that differentiates the groups is usually a pre-existing participant variable (such as male/female) or a time variable (such as before/after).

Similar to correlational research because they simply demonstrate and describe relationships

Question 8

positively skewed

Answer 8

In a positively skewed distribution, the scores tend to pile up on the left side of the distribution with the tail tapering off to the right.

Question 9

negatively skewed

Answer 9

In a negatively skewed distribution, the scores tend to pile up on the right side and the tail points to the left.

Question 10

percentile rank

Answer 10

The percentile rank for a particular X value is the percentage of individuals with scores equal to or less than that X value. When an X value is described by its rank, it is called a percentile.

Question 11

nominal

Answer 11

name only, categorical; only permit you to determine whether two individuals are the same or different. (i.e. male/female; diagnosis)

Question 12

ordinal

Answer 12

rank ordered (e.g. height shortest to tallest); tell you the direction of difference between two individuals.

spearman correlation (i.e. class rank)

Question 13

interval

Answer 13

consistent intervals between numbers but no absolute zero (i.e. IQ); identify the direction and magnitude of a difference

Question 14

ratio

Answer 14

interval plus absolute zero – height in inches; identify the direction and magnitude of differences and allow ratio comparisons of measurements

Question 15

reliability

Answer 15

same results with repeated administrations

Question 16

validity

Answer 16

measures what it says it measures- taps into the construct

Question 17

standard error of the mean (SEM)

Answer 17

measure of variability; the average expected difference between sample means (i.e. M1 – M2 expected)

Question 18

confidence interval

Answer 18

a type of interval estimate of a population parameter and is used to indicate the reliability of an estimate. Certain factors may affect the confidence interval size including size of sample, level of confidence, and population variability. A larger sample size normally will lead to a better estimate of the population parameter.

Question 19

sampling error

Answer 19

The discrepancy between a sample statistic and its population parameter is called sampling error.

Question 20

central tendency in normal distribution

Answer 20

a statistical measure that determines a single value that accurately describes the center of the distribution and represents the entire distribution of scores. The goal of central tendency is to identify the single value that is the best representative for the entire set of data. Allows researchers to summarize or condense a large set of data into a single value (thus a descriptive statistic).

Question 21

mean and a mean in skewed data

Answer 21

Mean: the average; most commonly used; requires scores that are numerical values measured on an interval or ratio scale

When a distribution contains a few extreme scores (or is very skewed), the mean will be pulled toward the extremes (displaced toward the tail).

Question 22

median

Answer 22

Median: If the scores in a distribution are listed in order from smallest to largest, the median is defined as the midpoint of the list; values measured on an ordinal, interval, or ratio scale

Relatively unaffected by extreme scores

Question 23

mode

Answer 23

the most frequently occurring category or score in the distribution; any scale of measurement: nominal, ordinal, interval, or ratio

Question 24

what will be equal in a symmetrical distribution

Answer 24

the mean and median will always be equal

Question 25

variability

Answer 25

goal is to obtain a measure of how spread out the scores are in a distribution; describes how the scores are scattered around that central point. In the context of inferential statistics, variability provides a measure of how accurately any individual score or sample represents the entire population. Measuring distance.

Question 26

range and interquartile range

Answer 26

largest to smallest

The interquartile range is the distance covered by the middle 50% of the distribution (the difference between Q1 and Q3).

Question 27

standard deviation

Answer 27

average distance between most scores in the distribution (square root of the variance) – b/w the score and the mean

Question 28

variance

Answer 28

average squared deviation between most scores in the distribution (it is the standard deviation squared)

Question 29

negative skew vs positive skew outliers

Answer 29

Positive Skew – extreme outlier(s) in the positive/ high end

Negative Skew – extreme outlier(s) in the negative/ low end

Question 30

implication of skew for parameters

Answer 30

violates the parametric assumptions needed for the parametric tests (t-tests, anova, pearson correlation)

Question 31

descriptive statistics

Answer 31

methods for organizing and summarizing data
-mean, median, mode, standard deviation, variance

Question 32

parameter vs statistic

Answer 32

A descriptive value for a population is called a parameter and a descriptive value for a sample is called a statistic.

Question 33

frequency distribution

Answer 33

organized tabulation showing exactly how many individuals are located in each category on the scale of measurement. A frequency distribution presents an organized picture of the entire set of scores, and it shows where each individual is located relative to others in the distribution.

Question 34

regular frequency distribution

Answer 34

when a frequency distribution table lists all of the individual categories (X values) it is called a regular frequency distribution.

Question 35

grouped frequency distribution

Answer 35

In a grouped frequency distribution, the X column lists groups of scores, called class intervals, rather than individual values (too many different X values).

Question 36

why are frequency distribution graphs useful

Answer 36

Frequency distribution graphs are useful because they show the entire set of scores. At a glance, you can determine the highest score, the lowest score, where the scores are centered, most common score.

Question 37

inferential statistics

Answer 37

methods for using sample data to make general conclusions (inferences) about populations

Question 38

effect size

Answer 38

measure of the strength of a phenomenon

Question 39

hypothesis testing

Answer 39

general goal of a hypothesis test is to rule out chance (sampling error) as a plausible explanation for the results from a research study. A technique to help determine whether a specific treatment has an effect on the individuals in a population.

Question 40

null hypothesis

Answer 40

H0, always states that the treatment has no effect (no change, no difference). According to the null hypothesis, the population mean after treatment is the same is it was before treatment.

Test statistic in critical region → reject the null = p<0.05 = there is an effect, difference

Test statistic in the body (outside of the critical region) → fail to reject the null = there is no effect/ no difference

Question 41

alternative hypothesis

Answer 41

a statement that directly contradicts a null hypothesis by stating that the actual value of a population parameter is less than, greater than, or not equal to the value states in the null hypothesis

alpha level: establishes a criterion, or “cut-off”, for making a decision about the null hypothesis. The alpha level also determines the risk of a Type I error.

Power - probability that the test will reject the null hypothesis when the null hypothesis is false (find an effect when there is one)
-Influenced by: Alpha level; Sample size; Sensitivity of test; Effect size

Question 42

type 1 error

Answer 42

occurs when the sample data appear to show a treatment effect when, in fact, there is none. In this case the researcher will reject the null hypothesis and falsely conclude that the treatment has an effect.

Question 43

what causes type 1 error

Answer 43

caused by unusual, unrepresentative samples. Just by chance the researcher selects an extreme sample with the result that the sample falls in the critical region even though the treatment has no effect.

The hypothesis test is structured so that Type I errors are very unlikely; specifically, the probability of a Type I error is equal to the alpha level.

Question 44

type 2 error

Answer 44

occurs when the sample does not appear to have been affected by the treatment when, in fact, the treatment does have an effect. In this case, the researcher will fail to reject the null hypothesis and falsely conclude that the treatment does not have an effect.

Question 45

what causes type 2 errors

Answer 45

commonly the result of a very small treatment effect. Although the treatment does have an effect, it is not large enough to show up in the research study.

Question 46

directional test or one tailed test

Answer 46

A directional test or a one-tailed test includes the directional prediction in the statement of the hypotheses and in the location of the critical region.

Question 47

what is recommended that the hypothesis test be accompanied by

Answer 47

effect size

We use Cohen’s d as a standardized measure of effect size. Much like a z-score, Cohen’s d measures the size of the mean difference in terms of the standard deviation (Impact of the IV).

Question 48

what are the three parametric assumptions we made to engage in inferential statistics

Answer 48

1) Independent Observations – random selection, representative sample you can’t just bring friends to the experiment – b/c of bias (not representative)

2) Normally distributed: Populations which samples are selected from are normally distributed (if not normally distributed then can’t run these tests)

3) Homogeneity of variance: populations from which samples are selected have equal variances (if change the experiment then the scores should shift equally)

Question 49

independent measures between subjects t test

Answer 49

(2 separate groups)

An independent-measures design can be used to test for mean differences between two distinct populations (such as men versus women) or between two different treatment conditions (such as drug versus no-drug).

Question 50

repeated measures design t test

Answer 50

single group of individuals is obtained and each individual is measured in both of the treatment conditions being compared. Thus, the data consist of two scores for each individual.

Question 51

related sample t -test, matched subjects design

Answer 51

each individual in one treatment is matched one-to-one with a corresponding individual in the second treatment

Question 52

ANOVA?

Answer 52

comparing 3 or more treatment conditions; more than 1 IV (factor); more than 2 levels of an IV

Question 53

what does analysis of variance do in ANOVA

Answer 53

controls the risk of a Type I error in situations where a study is comparing more than two population means

Question 54

why use post hoc tests

Answer 54

ANOVA simply establishes that differences exist, it does not indicate exactly which treatments are different. Specifically, you must follow the ANOVA with additional tests, called post hoc tests, to determine exactly which treatments are different and which are not.

Question 55

examples of post hoc tests

Answer 55

The Scheffe test and Tukey=s HSD are examples of post tests. Indicates exactly where the difference is

Question 56

what does the repeated measure design do

Answer 56

the repeated measures design eliminates individual differences from the between treatments variability because the same subjects are used in every treatment condition

Question 57

MANOVA

Answer 57

ANOVA with the addition of multiple Dependent variables

Question 58

ANCOVA

Answer 58

ANOVA with a covariate- a variable that varies systematically with the IV
-covariate: another variable that has a relation to the DV

Question 59

when are non parametric tests used

Answer 59

used when violate the parametric assumptions (or data is NOT interval or ratio level)

Question 60

chi square tests

Answer 60

tests the shape of the distribution of nominal data/ categorical/ not a parametric test

Question 61

goodness of fit in chi square

Answer 61

roughly the same number of subjects in each category? OR does the distribution fit a predetermined distribution (i.e. 40% male and 60% female)

Question 62

test for independence

Answer 62

similar to correlation in that it looks at the relationship between 2 variables but uses NOMINAL data

Question 63

Mann Whitney U

Answer 63

analogous to independent measures t-test

Question 64

Friedman tests

Answer 64

analogous to repeated measures anova

Question 65

Wilcoxin test

Answer 65

analogous to repeated measures t-test

Question 66

Kruskal Wallace

Answer 66

analogous to independent measures (one-way) anova

Question 67

correlation

Answer 67

tests the relationship between 2 variables that occur naturally: relationship only; no cause and effect; determine whether there is a relationship between two variables and to describe the relationship

Question 68

sign and strength

Answer 68

positive or negative tells nothing about the strength of the relationship but tells about the direction (both go up together or one goes up the other goes down)
-Negative correlation: one variable increases, other decreases
-Positive correlation: one variable increases, the other increases

Strength – between 0 and 1 (closer to 1 = stronger)

Question 69

restriction of range

Answer 69

can make the relationship seem weaker because you are only getting a small snapshot of the full relationship between the two variables (i.e. looking at the relationship between age and reaction time if you only use 19-22 year olds you won’t find a relationship)

Question 70

pearson product moment correlation

Answer 70

used for parametric data/ interval or ratio level data with a linear relationship

Question 71

spearman correlation

Answer 71

used for non-linear (i.e. inverted U relationship) or when data is ordinal/ ranked

Question 72

regression

Answer 72

used for prediction; a statistical technique that related a dependent variable to one or more independent (explanatory) variables. A regression model is able to show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables

Question 73

multiple regression

Answer 73

same as regression but using multiple variables to make the prediction (i.e. SAT scores, HS GPA, and quality of essay to predict college GPA)

Question 74

path analysis

Answer 74

allows for the evaluation of the causal flow of relationships because a priori predictions are made about the correlations

Question 75

logistic regression

Answer 75

prediction of group membership (doctor, lawyer, tradesperson) from interval or ratio level predictors (scores usually)

Question 76

discriminant analysis

Answer 76

same as Logistic regression but predictors can be any kind of variable (dichotomous, interval, ratio, nominal)

Question 77

statistics

Answer 77

branch of mathematics used to summarize, analyze, and interpret a group of numbers or observations

Question 78

research method or scientific method

Answer 78

set of systemic techniques used to acquire, modify, and integrate knowledge concerning observable and measurable phenomena

Question 79

experiments

Answer 79

use of methods and procedures to make observations in which a researcher fully controls the conditions and experiences of participants by applying three required elements of control (manipulation, randomization, and comparison/control) to isolate cause-and-effect relationships between variables

Question 80

correlations

Answer 80

examines the relationship between variables by measuring pairs of score for each individual. This method can determine whether a relationship exists between variables, but it lacks the appropriate controls needed to demonstrate cause and effect.

Question 81

quasi experiments

Answer 81

this study does not include a manipulated independent variable and it lack a comparison/control group

Question 82

quasi independent variable

Answer 82

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

Question 83

population

Answer 83

set of all individuals, items, or data of interest. This is the group about which scientist will generalize

Question 84

sample

Answer 84

set of individuals, items, or data selected from a population of interest.

Question 85

independent variable

Answer 85

variable that is manipulated in an experiment. This variable remains unchanged (or “independent”) between conditions being observed in an experiment. The specific conditions of the IV are referred to as levels.

Question 86

dependent variable

Answer 86

the variable that is measured in each group of a study, and it is believed to change in the presence of the independent variable. It is the “presumed effect.”

Question 87

descriptive statistics

Answer 87

procedures used to summarize, organize, and make sense of a set of scores called data. They are typically presented graphically or in tables.

Question 88

inferential statistics

Answer 88

procedures used that allow researchers to infer or generalize observations made with samples to the larger population from which they are selected

Question 89

population parameter

Answer 89

a characteristic (usually numeric) that describes a population. **Will usually never have this!

Question 90

sample statistic

Answer 90

a characteristic (usually numeric) that describes a sample

Question 91

nominal

Answer 91

are measurements in which a number is assigned to represent something or someone (a name, has no numerical properties)

Question 92

ordinal

Answer 92

measurements that convey order or rank alone (does not tell us space between ranks)

Question 93

interval

Answer 93

measurements that have no true zero and are distributed in equal units (equidistant, ex. Likert Scale)

Question 94

ratio

Answer 94

measurements that have a true 0 and are distributed in equal units

Question 95

qualitative

Answer 95

varies by class. This variable is often represented as a label and describes nonnumeric aspects of phenomena.

Question 96

quantitative

Answer 96

varies by amount. This variable is measured numerically and is often collected by measuring or counting.

Question 97

continuous

Answer 97

is measured alone a continuum at any place beyond the decimal points, can thus be measures in fractional units (ex. Time)

Question 98

discrete

Answer 98

measured in whole units or categories that not distributed along a continuum

Question 99

central tendency

Answer 99

statistical measures for locating a single score that is most representative or descriptive of all scores in a distribution

Question 100

mean and when best to use

Answer 100

also called the average) is the sum of a set of scores in a distribution, divided by the total number of scores summed.
**Best to use the mean in a normal distribution and with interval and ratio scales. In normal distributions, the mean median, and mode are all the same

Question 101

median and when best to use

Answer 101

is the middle value in a distribution of data listed in numeric order (aka the 50th percentile)
**Best to use the median with data that had outliers, is skewed and with ordinal scales

Question 102

mode and when best to use

Answer 102

is the value in a data set that occurs most often or most frequently (can have multiple modes)
**Best to use the mode when working with a modal/bimodal distribution (curve sinks in the middle) and with nominal scales

Question 103

normal distribution

Answer 103

bell-shaped) a theoretical distribution in which scores are symmetrically distributed above and below the mean, the median, and the mode at the centers of the distribution

Question 104

skewed distribution

Answer 104

distribution of scores that includes scores that fall substantially above or below most other scores in a data set

-Positive Skew: a distribution of scores that includes scores that are substantially larger than most other score (tail/flat part will be on the positive/ right side)
-Negative Skew: a distribution of scores that includes scores that are substantially smaller than most other scores (tail/flat part will be on the negative/ left side)

Question 105

bimodal distribution

Answer 105

a distribution of scores in which two scores occur most often or most frequently (a high hump where one mode is, a dip in the center (mean/median), and then another high hump where the second mode is, refer to pg. 95 in Privitera book for visual so you don’t have to keep listening to me talk about humps)

Question 106

variability

Answer 106

is a measure of the dispersion or spread of scores in a distribution that ranges from 0 to positive infinity

Question 107

range

Answer 107

is the difference between the largest value (L) and smallest value (S) in a data set.

Question 108

interquartile range

Answer 108

is the range of values between the upper (Q3) and the lower (Q1) quartiles of a data set

Lower Quartile: the median value of the lower half of a data set at the 25th percentile of a distribution

Upper Quartile: is the median value of the upper half of the data set at the 75th percentile of a distribution

Question 109

SS (sum of squares)

Answer 109

is the sum of the squared deviations of scores from their mean. The SS is the numerator in the variance formula

Question 110

variance

Answer 110

measure of variability for the average squared difference that scores deviate from their mean

Question 111

standard deviation

Answer 111

measure of variability for the average distance that scores deviate from their mean. It is calculated by taking the square root of the variance.

Question 112

z score

Answer 112

value on the x-axis of a standard normal distribution. The numerical value of a z score specifies the distance or the number of standard deviations that a value is above or below the mean

Question 113

characteristics of normal distribution

Answer 113

-Mathematically defined
-Theoretical (no distribution is perfect, it is approximate)
-Mean, median, and mode all in the 50th percentile
-It is symmetrical
-The total area under the curve is equal to 1 or 100%

Question 114

sampling distribution

Answer 114

a distribution of samples means or sample variances that could be obtained in samples of a given size from the same population
**Sampling distribution of sampling means is an unbiased estimator and follows the central limit theorem

Question 115

unbiased estimator

Answer 115

any sample statistic, such as sample variance when we divide SS by n-1, obtained from a randomly selected samples that equals the value of its respective population parameter, such as population variance, on average

Question 116

central limit theorem

Answer 116

explains that regardless of the distribution of scores in a population, the sampling distribution of samples means selected at random from that population will approach the shape of a normal distribution, as the number of samples in the sampling distribution increases

Question 117

standard error of the mean

Answer 117

the standard deviation of a sampling distribution of sampling means. It is the standard error or distance that sample mean values deviate from the value of the population mean

Question 118

sampling error

Answer 118

the extent to which sample means selected from the same population differ from the mean of sampling distribution of sample means (SDSM)
-The question is: is you randomly select one sample mean from the sample, how close will it be to the SDSM?
-Therefore, sampling error is the measurement of how narrow you SDSM is so a lot of error will mean that when taking a random sample mean, that number will be far off from the SDSM

Question 119

factors that decrease standard error

Answer 119

The larger the population standard deviation, the larger the standard error
-As numerator gets bigger, quantity as a whole gets bigger
-The wider the distribution, the less likely you are to get a random sample mean that is close to the SDSM

As sample size increases, standard error decreases
-As denominator gets bigger, quantity gets smaller
-Narrower distribution

bigger population = less error

Question 120

z scores for sample means

Answer 120

tells you the probability of sampling a particular mean from a particular population (NEED population standard devation)

Question 121

hypothesis testing

Answer 121

a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we use test a hypothesis by determining the likelihood that a sample statistic would be selected if the hypothesis regarding the population parameter were true
-Goal: to see if difference are significant

Question 122

null hypothesis

Answer 122

is a statement about a population parameter, such as the population mean, that is assumed to be true

Question 123

alternative hypothesis

Answer 123

a statement that directly contradicts a null hypothesis by stating that the actual value of a population parameter is less than, greater than, or not equal to the value states in the null hypothesis

Question 124

level of significance

Answer 124

is a criterion of judgment upon which a decision is made regarding the value stated in a null hypothesis. The criterion is based on the probability of obtaining a static measured in a sample if the value stated in the null hypothesis were true (p value)

Question 125

significance

Answer 125

describes a decision made concerning a value stated in the null hypothesis. When the null hypothesis is rejected, we reach significance. When the null hypothesis is retained, we fail to reach significance.

Question 126

type 1 error

Answer 126

is the probability of rejecting the null hypothesis that is actually true. Researchers directly control for the probability of committing this type of error (using alpha). So, saying there is significance and rejecting the null when the null was actually true (false positive)

Question 127

alpha level

Answer 127

the largest probability of committing a Type I error than we will allow and still decide to reject the null hypothesis (usually .05)

Question 128

type 2 error

Answer 128

is the probability of retaining a null hypothesis that is actually false. So, retaining the null and saying your results are not significant when really the alternative hypothesis is true (false negative)

Question 129

power

Answer 129

the probability that a randomly selected sample will show that the null hypothesis is false when the null hypothesis is indeed false

Question 130

critical value

Answer 130

a cutoff value that define the boundaries beyond which less than 5% of sample means can be obtained if the null hypothesis is true

Question 131

z statistic

Answer 131

an inferential statistic used to determine the number of standard deviations in a standard normal distribution that a sample mean deviates form the population mean stated in the null hypothesis
**To do a z-test, you need a POPULATION standard deviation, which is very unlikely. That is why t test are more commonly used.

Question 132

effect size

Answer 132

a statistical measure od the size of an effect in a population, which allows researchers to describe how far scores shifted in the population, or the present of variance that can be explained by a given variable (Cohen’s d)

Cohen’s d effects: Small= d < 0.2, Medium= 0.2 < d < 0.8, Large= d > 0.8

Question 133

one tailed

Answer 133

alternative hypothesis is stated as specifically greater of less than, region of rejection is on either the upper or lower portion of sampling distribution, makes it easier to reject the null and has greater power BUT is harder to justify (everybody will be like… that’s some whack ass research)

Question 134

two tailed

Answer 134

alternative hypothesis is stated as not equal, region of rejection is upper AND lower portion of sampling distribution, ALWAYS USE TWO TAILS

Question 135

t statistic

Answer 135

an inferential statistic used to determine the number of standard deviations in a t distribution that a sample mean deviates from the mean value or mean difference stated in the null hypothesis

Question 136

assumptions for t test

Answer 136

Normality

Random Sampling

Independence

Equal Variances
-(larger s2 divided by smaller s2, should be less than 2 to show equality)

Question 137

one independent sample t test

Answer 137

a statistical procedure used to compare a mean value measured in a sample to known value in the population. It is specifically used to test hypothesis concerning the mean in a single population with an unknown variance (or other arbitrary value).

Question 138

two independent sample t test

Answer 138

a statistical procedure used to compare the mean difference between two independent groups. This test is specifically used to test hypotheses concerning the difference between two population means, where the variance in one or both populations is unknown

Question 139

related samples

Answer 139

participants are related

Question 140

repeated measures design

Answer 140

research design in which the same participants are observed in each treatment

Question 141

pre post test design

Answer 141

a types of repeated-measures design in which researchers measure a dependent variable from participants before and after a treatment

Question 142

within subjects design

Answer 142

type of repeated-measures design in which researchers observe the same participants across many treatments but not necessarily before and after a treatment

Question 143

matched pairs design and the two ways it can be done

Answer 143

research design in which participants are selected and then matched based on common characteristics or traits

Experimentally: ex. measure intelligence and then match the two participants who scored the highest (put one in the drug group and one in the placebo group)

Naturally: ex. testing twins

Question 144

advantages for selecting related samples

Answer 144

More practical

Minimizes standard error
-Most important advantage, it reduces standard error by removing individual differences and isolating effect

Increases power

Question 145

disadvantages for selecting related samples

Answer 145

Requires individuals to be exposed to more than one treatment
-The 1st test may influence the score of the 2nd test (aka order effects)
-People get tired the more tests you give them

Question 146

one sample z test

Answer 146

a statistical procedure used to test hypothesis concerning the mean in a single population with a known variance

Question 147

independent sample

Answer 147

a type of sample in which different participants are independently observed one time in each group

Question 148

one independent sample t test

Answer 148

only one group of participants that are given treatment and then compared to the population mean or some arbitrary value

Question 149

two independent sample t test

Answer 149

participants in each group or sample are unrelated, selected from 2 or more populations, or single population with random different groups, then the groups are compared to each other

Question 150

related or dependent sample

Answer 150

participants are related

Question 151

repeated measures design

Answer 151

is a research design in which the same participants are observed in each treatment. Two types of repeated-measures designs are the pre-post design and the within-subjects design

Question 152

pre-post design

Answer 152

types of design in which researchers measure a dependent variable for participants before (pre) and after (post) a treatment

Question 153

within subject design

Answer 153

is a type of repeated-measures design in which researchers observe the same participants across many treatments but not necessarily before and after a treatment

Question 154

matched pairs design

Answer 154

a within-subjects research design in which participants are selected and then matched, experimentally or naturally, based on common characteristics or traits

Experimentally: ex. Measure intelligence then match the two participants who scored the highest with each other and so on

Naturally: ex. Match based on genetics (twins)

Question 155

difference score

Answer 155

score or value obtained by subtracting one score from another. In a related-samples t test, difference score are obtained prior to computing the test statistic

Question 156

error

Answer 156

a test, this refers to any unexplained difference that cannot be attributed to, or caused by, having different treatments. The standard error of the man is used to measure error or unexplained differences in a statistical design.

Question 157

levels of the factor

Answer 157

symbolized as K, are the number of groups or different ways in which an independent or quasi-independent variable is observed

Question 158

analysis of variance (ANOVA)

Answer 158

statistical procedure used to test hypothesis for one or more factors concerning the variance among two or more group means (k>2), where the variance in one or more populations is unknown

Question 159

one way between subjects ANOVA

Answer 159

a statistical procedure used to test hypothesis for one factor with two or more levels concerning the variance among group means. This test is used when different participants are observed at each level of a factor and the variance in any one population is unknown.

Question 160

source of variation

Answer 160

variation that can be measures in a study. In the one-way between-subjects ANOVA, there are two sources of variation: variation attributed to difference between group means and variation attributed to error.

Question 161

within groups variation

Answer 161

is the variation attributed to mean differences with each group. This source of variation cannot be attributed to or caused by having difference groups and is therefore called error variation (this is bad variation)

Question 162

between groups variation

Answer 162

variation attributed to the mean differences between groups (this is good variation). This is where you will see treatment effects.

Question 163

post hoc test

Answer 163

statistical procedure computed following a significant ANOVA to determine which pair or pairs of group means significantly differ. This test is only necessary when k >2 because multiple comparisons are needed. When k=2, only one comparison is made because only one pair of group means can be compared.

Question 164

factorial design

Answer 164

research design in which participants are observed across the combination of levels of two of more factors

Question 165

two way ANOVA

Answer 165

statistical procedure used to test hypotheses concerning the variance of groups created by combining the levels of two factors. This test is used when the variance in any one population is unknown.

Question 166

1 between 1 within design or mixed design

Answer 166

research design in which different participants are observed at each level of the between-subjects factor and the same participants are observed across the levels of the within-subjects factor.

Question 167

cell

Answer 167

combination of one level from each factor. Each cell is a group in a research study

Question 168

complete factorial design

Answer 168

research design in which each level of one factor is combined or crossed with each level of the other factor, with participants observed in each cell or combination of levels

Question 169

main effect

Answer 169

source of variation associated with mean differences across the levels of a single factor. In the two-way ANOVA, there are two factors and therefore two main effects: one for Factor A and one for Factor B.

Question 170

interaction

Answer 170

source of variation associated with the variance of group means across the combination of levels of two factors. It is a measure of how cell means at each level of one factor change across the levels of a second factor.

Question 171

levels

Answer 171

number of different ways a variable is manipulated or measured

Question 172

conditions

Answer 172

unique combinations of different levels of different independent variables

Question 173

simple main effects test

Answer 173

are hypothesis tests used to analyze a significant interaction by comparing the mean differences or simple main effects of one factor at each level of a second factor

**Main effects and interactions occur independently of each other

Question 174

eye balling

Answer 174

add cell means then divide by how many cells, the marginal means will inform you about main effects and the cell means will inform you about interactions

Question 175

one way vs two way anova

Answer 175

one way: uses one variable (e.g. average heights of plants grown with different fertilizers)

two way: uses two independent variables, also assesses for interaction between the two independent variables (e.g. worker productivity based on gender and department)