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Advanced Nursing Research > Dr Mac's Final's List > Flashcards

Dr Mac's Final's List Flashcards

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1
Q

ANOVA- Analysis of Variance- CH 11

A

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.

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2
Q

Bonferroni correction- CH 11

A

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

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3
Q

F-statistic- CH. 11

A

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

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4
Q

Omega squared- CH. 11

A

The effect size for one-way ANOVA results.

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5
Q

Orthogonal planned contrasts- CH. 11

A

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.

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6
Q

Post hoc tests- CH. 11

A

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

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7
Q

T-test- CH. 11

A

A method for comparing two means.

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8
Q

Categorical data- CH. 14

A

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

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9
Q

Chi-square test- CH. 14

A

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

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10
Q

Contingency tables- CH. 14

A

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

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11
Q

Crosstab analysis- CH. 14

A

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

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12
Q

Fisher’s exact test- CH. 14

A

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.

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13
Q

Odds ratio- CH. 14

A

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

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14
Q

Phi and Cramer’s V- CH. 14

A

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

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15
Q

Relative risk- CH. 14

A

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

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16
Q

Analysis of covariance- CH. 12

A

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.

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17
Q

Assumption of sphericity- CH. 12

A

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

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18
Q

Box’s M test- CH. 12

A

A method used to test the homogeneity of covariance matrices.

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19
Q

Covariate- CH. 12

A

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.

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20
Q

Factorial analysis of variance- CH. 12

A

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

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21
Q

Multivariate- CH. 12

A

When a design examines two or more dependent variables.

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22
Q

Multivariate analysis of covariance- MANCOVA- CH. 12

A

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.

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23
Q

Multivariate analysis of variance- MANOVA- CH. 12

A

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

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24
Q

Simple effect analysis- CH. 12

A

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.

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25
Q

Sphericity-assumed statistics- CH. 12

A

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

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26
Q

Univariate- CH. 12

A

When a design examines a single dependent variable.

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27
Q

Multiple linear regression- CH. 10

A

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.

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28
Q

Enter method- CH. 10

A

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

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29
Q

Goodness of fit- CH. 10

A

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

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30
Q

Hierarchical method- CH. 10

A

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.

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31
Q

Linear- CH. 10

A

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

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32
Q

Linearity- CH. 10

A

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

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33
Q

Logistic regression- CH. 10

A

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.

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34
Q

Method of least squares- CH. 10

A

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

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35
Q

Multicollinearity- CH. 10

A

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

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36
Q

Percentage of variance- CH. 10

A

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

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37
Q

Regression model- CH. 10

A

The model created via regression analysis.

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38
Q

Residual- CH. 10

A

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

39
Q

Stepwise methods- CH. 10

A

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.

40
Q

Bar Chart- CH. 5

A

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.

41
Q

Boxplot- CH. 5

A

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

42
Q

Frequency Distribution- CH. 5

A

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

43
Q

Histogram- CH. 5

A

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.

44
Q

Line chart- CH. 5

A

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

45
Q

Percentile- CH. 5

A

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

46
Q

Pie chart- CH. 5

A

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.

47
Q

Scatterplot- CH. 5

A

A visual representation of the relationship between two continuous variables.

48
Q

Stem and leaf plot- CH. 5

A

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

49
Q

Association- CH. 9

A

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

50
Q

Causality- CH. 9

A

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

51
Q

Coefficient of determination- CH. 9

A

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

52
Q

Correlation- CH. 9

A

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

53
Q

Covariance- CH. 9

A

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.

54
Q

Partial correlation coefficient- CH. 9

A

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.

55
Q

Data cleaning- CH. 8

A

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

56
Q

Nonrandom missing data- CH. 8

A

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

57
Q

Outlier- CH. 8

A

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

58
Q

Random missing data- CH. 8

A

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

59
Q

Skewed- CH. 8

A

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

60
Q

Clinical significance- CH. 7

A

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

61
Q

Effect- CH. 7

A

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

62
Q

Effect size- CH. 7

A

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

63
Q

Generalizability- CH. 7

A

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

64
Q

Null hypothesis- CH. 7

A

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

65
Q

One-tailed test- CH. 7

A

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

66
Q

Statistical power- CH. 7

A

The probability of correctly rejecting the false null hypothesis.

67
Q

Two-tailed test- CH. 7

A

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

68
Q

Type I error- CH. 7

A

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

69
Q

Type II error- CH. 7

A

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

70
Q

Steps in Hypothesis testing

A
  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
71
Q

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

A

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

72
Q

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

A

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

73
Q

What is the Hypothesis

A

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

74
Q

Hypothesis testing can have how many tails?

A

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

75
Q

A ratio variable has what characteristics?

A

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

76
Q

An interval variable has what characteristics?

A

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

77
Q

A nominal variable has what characteristics?

A

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

78
Q

An Ordinal variable has what characteristics?

A

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

79
Q

A categorical variable has what characteristics.

A

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

80
Q

When does missing data occur?

A

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

81
Q

How do you fix transcription data errors?

A

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

82
Q

What is random pattern of missing data?

A

missing data scattered in the database without any pattern.

83
Q

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

A

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

84
Q

What are 3 remedies for missing data?

A
  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.
85
Q

When did the Belmont report come out?

A

1979

86
Q

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

A
  1. Respect for person
  2. Beneficience
  3. Justice
87
Q

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

A

Respect for person

88
Q

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?

A

Respect for person

89
Q

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

A

Beneficence & Nonmaleficence

90
Q

Belmont report defines what ethic as “Fairness in distribution?”

A

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.

91
Q

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

A

Tuskegee Syphilis Study.

92
Q

When did the Tuskegee Syphilis Study begin and end?

A

Began in 1932, ended in 1970s

93
Q

Who were the participants of the Tuskegee experiments?

A

399 African-American men who were poor and illiterate.

94
Q

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

A

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.

Advanced Nursing Research (10 decks)

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