Exam 2

Front 1

Mean:

Back 1

Average numbers. Add together all of the numbers in a set and then divide the sum by the total count of numbers.

Front 2

Median:

Back 2

The statistical median is the middle number in a sequence of numbers. To find the median, organize each number in order by size; the number in the middle is the median.

For an even number add two middle numbers and divide by two.

Front 3

Mode:

Back 3

The mode is the number that occurs most often within a set of numbers.

Front 4

Range:

Back 4

The range is the difference between the highest and lowest values within a set of numbers. To calculate range, subtract the smallest number from the largest number in the set.

Front 5

Standard Deviation SD:

Back 5

is a measure that is used to quantify the amount of variation or dispersion of a set of data values.

Front 6

Variance:

Back 6

is the expectation of the squared deviation of a random variable from its mean, and it informally measures how far a set of (random) numbers are spread out from their mean.

Front 7

alternative hypothesis:

Back 7

what we hope to support in our research. Opposite of Null.

Front 8

Alpha. a= .05

Back 8

Confidence level. we use 95% confidence. which is P< .05

Front 9

One way Anova analysis of variance:

Back 9

inferential parametric statistic we use for comparing the means of a few groups. F= 3 or more. We symbolize it with the letter F. (3 independent variables.)

Front 10

Repeated Measures (within) One way Anova:

Back 10

Each person is tested on all levels of the independent variable.

Front 11

Parametric test:

Back 11

CI, T, F

Front 12

Percentile:

Back 12

How relative someone did based on others in the group.

Front 13

Post hoc Test:

Back 13

LSD test. Testing all possible pairs of groups to poinpoint which ones differ significantly from each other and which ones do not.

Front 14

Prep:

Back 14

A way to measure how likely we can replicate the results of the study, idealy in a different context or with a sample that has different characteristics.

Front 15

Positive skew:

Back 15

A distribution peak is to the left of the center point. slows to the right.

Front 16

Quasi experimental method:

Back 16

Research method in which the IV cannot be manipulated. Most often the variable is a characteristic of the people we are studying such as gender.We are restricted in the conclusions we can draw to difference conclusions, not knowing if the IV is the cause of any difference we observe in behavior. Just that there is a difference. (not cause and effect)

Front 17

ratio:

Back 17

numerical and has an absolute zero. common examples include time, centimeters.

Front 18

Raw score:

Back 18

A data point that has not been transformed, analyzed, or changed in any way. (hasnt been transformed or put into a t score).

Front 19

Sampling Error:

Back 19

The amount of inaccuracy in data due to studying a sample rather than testing the entire population. We can estimate this after determining our strength of our independent variable.

Front 20

Subject variable:

Back 20

A characteristic of the people we are studying, such as their gender or age, which allows difference in conclusions. (quasi).

Front 21

Situation variable:

Back 21

One we can manipulate, such as sending or not sending e-mails, which allows for cause-effect conclusions. (True experiment).

Front 22

Standard Scores:

Back 22

Raw scores converted into Z T

Front 23

Standard Normal Distribution:

Back 23

A normal distribution with a mean of 0 and a standard deviation of 1. When raw scores are converted to this distribution they are called standard scores or z-scores. We use Z-scores for comparing scores from different distributions and primarily for calculating percentiles.

Front 24

Standard error of the mean:

Back 24

The standard deviation of the sampling distribution, which we can approximate using statistics from samples (Sample size and standard deviation.

Front 25

If null is true?

Back 25

We risk Type two. (retain the null). NS.

Front 26

Type one's a chance?

Back 26

With significance. Reject the null. P<.05

Front 27

Cd 0:

Back 27

.0 negligible, paltry, very little or none. (strength, or effect.) 99% overlap which suggests highly similar.

Front 28

CD .2:

Back 28

Weak, small, not much, a little bit, a smidgen difference or effect. 20% overlap.

Front 29

CD .5:

Back 29

Moderate, modest, some, a fair amount, a reasonable amount, average.

Front 30

CD .8:

Back 30

Strong, lots, a large amount, a whole bunch, whopping, OMG this is big. (20% overlap).

Front 31

John Tukey developed the box and whisker plot to display a data set. The whiskers extend from the box to show:

Back 31

The minimum and maximum scores.

Front 32

Jacob Cohen developed a set of guidelines to evaluate the differences between two groups-say, the differences between males and females on a measure of mathematical skill. What would a cohens D= .11 tell us?

Back 32

Small difference, lots of overlap in the distributions of males and females.

Front 33

If we reject the null hypothesis and claim an outcome is significant, what are the chances we have made a type 1 error?

Back 33

P<.05

Front 34

If on the Neo-ff our class mean on the neuroticism scale is 28, with a 95% CI of {25, 31} the population mean is likely to be:

Back 34

Inside the 95% confidence interval.

Front 35

The number of scores in a data set that are free to vary is called:

Back 35

Degrees of freedom

Front 36

1-N2 is a good estimate of the amount of difference between groups due to:

Back 36

Error or chance factors.

Front 37

Which of the following is NOT a common Technique in exploratory data analysis?

Back 37

Anova

Front 38

If the IV is having no effect on the DV in ANOVA, the F will be:

Back 38

1

Front 39

If the standard deviation for a set of scores is small, the scores:

Back 39

are homogeneous.

Front 40

We can make pair-wise comparisons between any two means in an anlysis of variance using:

Back 40

LSD tests

Front 41

If you are in the 62%tile for agreeableness on the NEo-ff personality inventory, what does this say about you?

Back 41

You are above average on the Neo-ff scale

Front 42

Gender, age, and ethnicity are good examples of:

Back 42

Subject variables.

Front 43

Determining a persons sex involves measurement on a _____

Back 43

Nominal

Front 44

Determining a persons reaction time involves a measurement on a _____

Back 44

Interval/ratio

Front 45

A recent report concludes that rats given vitamin supplements have better maze learning scores than rats on a regular diet. The IV is:

Back 45

The type of diet

Front 46

A recent study with college students reports that reaction times in the morning are faster than reaction times in the afternoon. For this report, reaction time is the:

Back 46

Dependent variable.

Front 47

If a population of N = 10 scores has a mean of 30 and a standard Deviation of 4, then the variance equals?

Back 47

16 (just square 4)

Front 48

Suppose you earned a score of 40 on an exam. Which parameters would give you the highest grade?

Back 48

Mean of 45 and S= 10

Front 49

Which is not a measure of variability?

Back 49

Mean

Front 50

A positive deviation score indicates that the raw score

Back 50

Greater than the mean

Front 51

Regression equation:

Back 51

Y= a + bX

Front 52

confidence interval:

Back 52

Yt = +_ 1.96(standard error of estimate)

Front 53

eta square is to analysis of variance as ____ is to _____

Back 53

coefficient of determination/correlation

Front 54

in a 2 x 2 ANOVA, there are ____ separate null Hypothesis

Back 54

3

Front 55

In a positive relationship

Back 55

as x increases, y increases

as x decreases, y decreases

Front 56

Y can be most accurately predicted from x if the correlation between x and y is:

Back 56

-.98 (or plus)

Front 57

if there is a positive correlation between x and y then the regression equation, Y = a + bX will have

Back 57

b>0

Front 58

For what scale of measurement is chi-square most readily suited

Back 58

nominal

Front 59

As the differences between expected and observed frequencies increase

Back 59

the likelihood of rejecting Ho also increases (Fo=Fe) if different, there is significance.

Front 60

A chi square to see if political affiliation (republican, democrat, independent, ) is related to religious affiliation (protestant, catholic, Jew, non-denominational) is a

Back 60

3x4 chi-square

Front 61

The null hypothesis in the chi-square test for independence is that

Back 61

the variables are independent

Front 62

If we found that position on abortion depended on gender, what would be the proper citation?

Back 62

x2 (1, N =100) = 4.22, p < .05

Front 63

Adjusted standardized residual:

Back 63

in chi-square research this is the difference between the observed frequency (fo) and the expected frequency (Fe) for a cell, divided by standard error. We can ask SPSS for this number for each cell and use it to decide which cells we need ot talk about and which cells we can ignore in our interpretation. An ASR > 1.96 is significant.

Front 64

cause in correlation:

Back 64

Correlation shows the relation between two sets of scores--two DVs we can only determine cause with a true experiment, manipulating an IV and measuring its effects on the DV, so we cannot NORMALLY draw cause-and-Effect conclusions with correlation.

Front 65

Cell Mean:

Back 65

The mean for any unique combination of the levels of the IVs in a factorial design ANOVA. Cell means are what we graph and we might polish them to make an interaction stand out.

Front 66

Correlational Method:

Back 66

A research method in which the relationship between at least two dependent variables is measured. Usually neither of the variables can be manipulated or controlled. Almost always both variables are dependent variables. When one of the variables is an independent variable then we say we have a quasi-experimental design and we do regression analysis.

Front 67

Factorial design:

Back 67

with more than one independent variable. This phrase comes up most often in reference to analysis of variance where a design and analysis involving 2 independent variables is called a factorial design and uses a factorial analysis of variance.

Front 68

Interaction:

Back 68

The effect of each independent variable across levels of the other independent variable. This occurs in 2-way anova in the summary table the interaction line appears as IV1*IV2. When the means of each group are graphed, the interaction shows a non parallel lines. when writing about the interaction we have to say how the variables in combination influence the dependent variable.

Front 69

intercept:

Back 69

The predicted value for Y when X is equal to 0 in a regression equation, or the point where the regression line crosses (or intercepts) the Y-axis. If the regression equation has the general linear from Y= a + bX the intercept is "A"

Front 70

linear relation:

Back 70

A relation between two dependent variables best fit by a straight line. It is the relation detected by Pearson's correlation and it is represented on the scatter plot as the regression line. If we claim there is a correlation between two variables we mean a linear relation-no correlation is no linear relation.

Front 71

main effect:

Back 71

An effect of a single independent variable. This term shows up most often in analysis of variance. The result of a one-way ANOVA is a simple main effect because the study involves only one IV and the source table has only one F to reflect that main effect. In a two-way ANOVA there are two main effects and an interaction, so there are 3 F values in the source table, two of which are main effects, one for each IV.

Front 72

multiple regression:

Back 72

A proceedure in correlation that uses two or more predictor variables in a regression equation. If there are two predictor variables, a graph of the data is three dimensional. Somewhat hard to visualize but we can try by using one-point perspective and representing some of the data points as larger and smaller bubbles.

Front 73

Negative correlation:

Back 73

Also a negative relationship; an inverse relation between two variables where an increase in one variable is related to a decrease in the other. We could describe this as a teeter totter relation, as one goes up the other goes down. When one is higher the other is low.

Front 74

observed frequencies:

Back 74

The number of participants in each category in our sample. We symbolize this as Fo and our chi-square compares the observe frequencies to expected frequencies.

Front 75

Pearsons correlation:

Back 75

The most common of the correlation statistics when both variables are measured on an interval or ratio scales. The letter associated with Pearsons correlation is R and these values are often reported in a correlation matrix, a table that shows the R values for all pairs of variables tested. Pearsons can range from 0-1 and can be positive or negative.