Dr Mac's Final's List

Question 1

ANOVA- Analysis of Variance- CH 11

Answer 1

A statistical test that checks if the means for several groups are equal. Used as a way to avoid the increasing probability of a type I error that comes with running multiple t-tests.

Question 2

Bonferroni correction- CH 11

Answer 2

A method used to correct for type I errors that can arise from multiple comparisons.

Question 3

F-statistic- CH. 11

Answer 3

A test used with normally distributed populations to determine if the means of said populations are equal.

Question 4

Omega squared- CH. 11

Answer 4

The effect size for one-way ANOVA results.

Question 5

Orthogonal planned contrasts- CH. 11

Answer 5

A type of a priori test; comparisons that are planned before analysis of data has begun because certain results are expected. Orthogonal planned contrasts help reduce type I error inflation that comes from multiple comparisons.

Question 6

Post hoc tests- CH. 11

Answer 6

Comparisons made to data after analysis to determine which means are contributing the greatest amount of variance.

Question 7

T-test- CH. 11

Answer 7

A method for comparing two means.

Question 8

Categorical data- CH. 14

Answer 8

Data made up of categorical variables, which are variables measured at the nominal or ordinal level.

Question 9

Chi-square test- CH. 14

Answer 9

A non parametric test used to determine whether an actual distribution of categorical data values differ from the expected distribution.

Question 10

Contingency tables- CH. 14

Answer 10

A table used to display the frequency distributions of variables, often used to study the relationship between two or more categorical variables.

Question 11

Crosstab analysis- CH. 14

Answer 11

Using a contingency table to study the relationship(s) between variables and focus in on the most significant relationships.

Question 12

Fisher’s exact test- CH. 14

Answer 12

A method used to test the relationship between categorical values in instances where the sample size is too small to use the chi-square test.

Question 13

Odds ratio- CH. 14

Answer 13

A descriptive statistic used in categorical data analysis that measures effect size (the strength of the association between two binary data values).

Question 14

Phi and Cramer’s V- CH. 14

Answer 14

Statistics that report the strength of an association between two categorical variables.

Question 15

Relative risk- CH. 14

Answer 15

A descriptive statistic that measures the probability of an event occurring if exposed to a specific risk factor.

Question 16

Analysis of covariance- CH. 12

Answer 16

Also known as ANCOVA; a combination of analysis of variance (ANOVA) and regression analysis that checks if the population means for a dependent variable are equal across an independent variable, while controlling for the presence of covariates.

Question 17

Assumption of sphericity- CH. 12

Answer 17

An assumption of repeated-measures ANOVA that the difference scores of paired levels of the repeated measures factor have equal variance.

Question 18

Box’s M test- CH. 12

Answer 18

A method used to test the homogeneity of covariance matrices.

Question 19

Covariate- CH. 12

Answer 19

A variable that influences the dependent variable, but is not the independent variable (i.e., not the variable of interest). Also known as a covariable.

Question 20

Factorial analysis of variance- CH. 12

Answer 20

Data analysis that studies the effects of two or more independent variables (factors) on the dependent variable.

Question 21

Multivariate- CH. 12

Answer 21

When a design examines two or more dependent variables.

Question 22

Multivariate analysis of covariance- MANCOVA- CH. 12

Answer 22

An extension of analysis of covariance that is used in cases where there is one or more dependent variables and there is a covariate(s) that needs to be controlled.

Question 23

Multivariate analysis of variance- MANOVA- CH. 12

Answer 23

An extension of analysis of variance that examines group differences on a combination of multiple dependent variables.

Question 24

Simple effect analysis- CH. 12

Answer 24

Statistical analysis that examines the effect of one variable at every level of the other variable, to confirm if the effect is significant at each level.

Question 25

Sphericity-assumed statistics- CH. 12

Answer 25

Repeated-measures design statistics provided if the assumptions of sphericity is not violated.

Question 26

Univariate- CH. 12

Answer 26

When a design examines a single dependent variable.

Question 27

Multiple linear regression- CH. 10

Answer 27

An extension of simple linear regression, where two or more independent variables, either continuous or categorical, are jointly used to predict a single dependent variable.

Question 28

Enter method- CH. 10

Answer 28

The default method in regression analysis, when all of independent variables are fitted into the regression model at the same time.

Question 29

Goodness of fit- CH. 10

Answer 29

A measure of how well a model fits a set of observations.

Question 30

Hierarchical method- CH. 10

Answer 30

A method in regression analysis that utilizes blocks of independent variables (chosen based on importance), added one at a time, to see if there is any change in the predictability.

Question 31

Linear- CH. 10

Answer 31

Generally referring to the relationship of one variable to another, which resembles a line.

Question 32

Linearity- CH. 10

Answer 32

A statistical term that is used to represent a mathematical relationship and graphically shown as a straight line.

Question 33

Logistic regression- CH. 10

Answer 33

A type of regression analysis that predicts a group membership in a categorical dependent variable with independent variables, which are usually continuous but can be categorical as well; called binary logistic regression when the number of categories of the dependent variable is two and multinomial logistic regression if a dependent variable has more than two categories.

Question 34

Method of least squares- CH. 10

Answer 34

An approach used in regression analysis to find the line that best fits the data with the fewest residuals.

Question 35

Multicollinearity- CH. 10

Answer 35

A high correlation/relationship (over .85) between independent variables with one potentially being linearly predicted from the others (i.e., no added information explained).

Question 36

Percentage of variance- CH. 10

Answer 36

Proportion of variation in a given data set explained by independent, mediating and/or moderating variables.

Question 37

Regression model- CH. 10

Answer 37

The model created via regression analysis.

Question 38

Residual- CH. 10

Answer 38

The difference between the observed data and the data fitted to the regression model. May also be thought of as unexplained variance.

Question 39

Stepwise methods- CH. 10

Answer 39

Methods used in regression analysis where variables are added to the model based on predetermined statistical criteria. The three types of stepwise methods are: forward selection, backward selection, and stepwise selection.

Question 40

Bar Chart- CH. 5

Answer 40

A graphical representation of data, most useful for data at the nominal or ordinal level of measurement; the data categories are on the horizontal axis, while the frequencies of each category are on the vertical axis.

Question 41

Boxplot- CH. 5

Answer 41

A chart that represents the distribution of data values; it also illustrates the quartiles and any outliers.

Question 42

Frequency Distribution- CH. 5

Answer 42

A method for presenting data that includes possible values for a given variable and the number of times each value is present.

Question 43

Histogram- CH. 5

Answer 43

A visual method for presenting data that is similar to a bar chart, but instead groups data points into intervals, rather than individual categories; most useful for showing the distribution of continuous data.

Question 44

Line chart- CH. 5

Answer 44

A visual representation of data that is useful for following changes over time or for finding patterns in the data.

Question 45

Percentile- CH. 5

Answer 45

Where a data point falls within the data set; specifically, how many data values fall above or below a specific point.

Question 46

Pie chart- CH. 5

Answer 46

A circular chart in which the sections are proportionally representative of the frequencies of specific values of the given variable. It is most useful for the nominal and ordinal levels of measurement.

Question 47

Scatterplot- CH. 5

Answer 47

A visual representation of the relationship between two continuous variables.

Question 48

Stem and leaf plot- CH. 5

Answer 48

A visualization of continuous data that shows both frequency distribution and information on individual data values.

Question 49

Association- CH. 9

Answer 49

A relationship between the variables being studied; when a change in one variable is related to a change in another variable.

Question 50

Causality- CH. 9

Answer 50

When a change in one variable is known to produce an effect or change in another variable.

Question 51

Coefficient of determination- CH. 9

Answer 51

A measure of the amount of variability in one (dependent) variable that can be explained by a second (independent) variable.

Question 52

Correlation- CH. 9

Answer 52

A standardized measure of the strength and direction of the relationship between two variables.

Question 53

Covariance- CH. 9

Answer 53

A measure of how two variables are related to each other, ranging from negative infinity to positive infinity; a covariance of zero indicates that there is no relationship between the variables.

Question 54

Partial correlation coefficient- CH. 9

Answer 54

A measurement that allows us to look at the true relationship between two variables after controlling for an unwanted variable that may be affecting the relationship.

Question 55

Data cleaning- CH. 8

Answer 55

Inspecting and correcting a data set to ensure that the data are complete and free of errors prior to analysis.

Question 56

Nonrandom missing data- CH. 8

Answer 56

When the data that are missing appear to follow a specific pattern.

Question 57

Outlier- CH. 8

Answer 57

Any data value that is outside of the expected range of values.

Question 58

Random missing data- CH. 8

Answer 58

When the data that are missing do not appear to follow any sort of pattern.

Question 59

Skewed- CH. 8

Answer 59

When the data's mean is pulled toward one tail or the other; an absence of normal distribution in a data set.

Question 60

Clinical significance- CH. 7

Answer 60

Usually measured as effect size; may be used to determine the magnitude of impact of an intervention; useful for evaluating clinical practice.

Question 61

Effect- CH. 7

Answer 61

When changes in the independent variable result in changes in the dependent variable.

Question 62

Effect size- CH. 7

Answer 62

The measure of the magnitude of the relationship or difference between groups; often used as a measure of efficacy.

Question 63

Generalizability- CH. 7

Answer 63

The accuracy with which results from a sample can be extrapolated to encompass the population as a whole.

Question 64

Null hypothesis- CH. 7

Answer 64

Denoted as H0 (as in zero); the hypothesis that suggest there will e no statistically significant effect on the variable(s) being studied.

Question 65

One-tailed test- CH. 7

Answer 65

A test of significance that looks for an effect in a particular direction (positive or negative).

Question 66

Statistical power- CH. 7

Answer 66

The probability of correctly rejecting the false null hypothesis.

Question 67

Two-tailed test- CH. 7

Answer 67

A test of significance that looks for an effect without concern as to the direction (positive or negative) of the effect.

Question 68

Type I error- CH. 7

Answer 68

When the null hypothesis is rejected by mistake; the probability of rejecting a true null hypothesis.

Question 69

Type II error- CH. 7

Answer 69

When the null hypothesis is not rejected by mistake; the probability of not rejecting a false null hypothesis.

Question 70

Steps in Hypothesis testing

Answer 70

1. State the Null and alternative hypothesis 2. propose an appropriate statistical test 3. check assumptions of th chosen test 4. compute the test statistics (find the P- value) 5. use the p- value to quantify evidence against the null hypothesis

Question 71

What does the Null Hypothesis state/mean? How is the Null Hypothesis denoted?

Answer 71

Null hypothesis assumes there is not effects and is denoted as H0

Question 72

What does the Alternative Hypothesis state/mean? How is the Null Hypothesis denoted?

Answer 72

Alternative hypothesis is a hypothesis that states an effect denoted as H1

Question 73

What is the Hypothesis

Answer 73

hypothesis is an estimate of the probability that the null hypotheses is correct

Question 74

Hypothesis testing can have how many tails?

Answer 74

hypothesis testing done with either one-tailed or two-tailed tests of inference,

Question 75

A ratio variable has what characteristics?

Answer 75

There is a meaningful zero Age, Height, Weight, blood pressure, years of work experience, time to complete a task.

Question 76

An interval variable has what characteristics?

Answer 76

Interval- There is no absolute value of zero. Temperature, IQ, SAT score, depression score, time of the day, dates (years).

Question 77

A nominal variable has what characteristics?

Answer 77

Nominal- Name or category, gender, ethnicity, zip code, medical dx, names of medicines

Question 78

An Ordinal variable has what characteristics?

Answer 78

Ordinal- ranking or ordering examples pain scale, age groups, grade (A,B,C,D) patient satisfaction score, nurse performance score,

Question 79

A categorical variable has what characteristics.

Answer 79

Categorical- variables measured at the nominal and ordinal levels of measurements are discrete or categorical.

Question 80

When does missing data occur?

Answer 80

Missing data occurs when a study participant or subject deliberately or accidentally omits responses to a variable.

Question 81

How do you fix transcription data errors?

Answer 81

Transcription data errors can be fixed by returning to the original data and making the necessary changes in the statistical software database.

Question 82

What is random pattern of missing data?

Answer 82

missing data scattered in the database without any pattern.

Question 83

between Random Pattern of missing data and Nonrandom Pattern of missing data, which is more problematic? Why?

Answer 83

Nonrandom Pattern of missing data- More problematic because the pattern will distort the results

Question 84

What are 3 remedies for missing data?

Answer 84

1. Deletion- delete the cases that are missing data and run data analyses with only the complete cases. This only works if little data is missing. Only use if little data is missing 2. Estimation- estimate the missing value and use these estimates in the data analysis. Using well educated guess from prior knowledge to substitute for the missing data. 3. Substituting missing data with mean values is another simple approach for estimating missing data. Calculated mean replaces each missing data point and it often provides a more accurate estimation than prior knowledge.

Question 85

When did the Belmont report come out?

Answer 85

1979

Question 86

What are the three fundamental principles of ethical conduct in human research?

Answer 86

1. Respect for person 2. Beneficience 3. Justice

Question 87

Individuals should be treated as autonomous agents is an example of what ethic?

Answer 87

Respect for person

Question 88

When a person is denied freedom to act on his or her decisions or when information needed to make a decision is withheld without a compelling reason to do so, what ethic is violated?

Answer 88

Respect for person

Question 89

What principle requires researchers to do no harm and maximize possible benefits?

Answer 89

Beneficence & Nonmaleficence

Question 90

Belmont report defines what ethic as "Fairness in distribution?"

Answer 90

Justice... The burden of service as a participant in research fell largely on the poor in early research studies (1800s), but the benefit went largely to those who could pay for care. Hence why Justice is an ethic.

Question 91

Deception and failure to provide adequate treatment of a serious disease were primary ethical violations of what study?

Answer 91

Tuskegee Syphilis Study.

Question 92

When did the Tuskegee Syphilis Study begin and end?

Answer 92

Began in 1932, ended in 1970s

Question 93

Who were the participants of the Tuskegee experiments?

Answer 93

399 African-American men who were poor and illiterate.

Question 94

Despite having a cure for syphilis available, what were the men given? What was the result?

Answer 94

The men were initially given small doses of the current treatment of syphilis (bismuth, neoarsphenamine, and mercury) which was later replaced with aspirin, which is not effective for syphilis. Some men entered the military, but still were not provided a cure. The public health service rept them from receiving penicillin. END RESULT. Half the men died despite the availability of an effective treatment for over 20 years before the experiment ended.