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NURS 8004 ADVANCED DOCTORAL WRITING
FOR NURSES FINAL EXAM QUESTIONS WITH
CORRECT ANSWERS 2025 - 2026 UPDATE (SORE
A+) HIGLY RECOMMENDED CAPELLA
UNIVERSITY
Correlation A measure of the relationship between two variables
. CORRELATION DOES NOT MEAN CAUSATION! We want to see if they are positively related, as one variable increases/decreases so does the other, or negatively related, as one decreases the other increases or vice versa Pearson correlation test Pearson is a parametric test and Spearmen is not. So Pearson must have assumptions met; independence, Normality, Variables are interval/ratio level data. Example; Skin temp after swimming and temp of water in the swimming pool Spearman Correlation Test
Spearmen just wants independence of variables. Sometimes people will run a Pearson and see that their assumptions are not met and switch to spearmen. Example: Ordinal level of data; Anxiety score and score on self-esteem scale; one of the measurements (or both) are being measured with ordinal level of data. correlation coefficient Both Pearson and Spearmen describe the strength and direction of a relationship via a Correlation Coefficient. - 1 is a perfect negative relationship, +1 is a perfect positive relationship. - or + in front of number shows type of relationship whether it be negative or positive. The number gives the strength of the relationship. Differentiating negative vs positive correlation You would plot one variable across the x-axis and one across the y-axis to see if they correlate. Husbands age Wife age Example of top dot Husband 80 wife is 82 You can see that it makes a line, and it is positively correlated So as the husband gets older the wife gets older and vice versa so they are related in a positive way Negative correlation example: As our elevation gets smaller our temp gets higher and vice versa
First you see if the 2 things are correlated. If so then you would see if they want to use one to predict the other. That’s when you would use regression for PREDICTION Cohort Study Where researchers follow a group/cohort over time; Often time they will have a exposure to something and maybe people that don’t have exposure to something and see overtime if the develop a disease Example: smokers vs non-smokers; you would not implement the intervention of telling people to smoke. Its not ethical. Another example Menopausal women that are using hormone therapy and those who are not ; 2 cohorts and look at rates of cardiac disease Stronger study because you are looking at the now so more accurate Unrealistic a lot of the time because of the resources and time. Exposed to something when a child and 50 years later wanting to know if it caused anything. Case-control study Identify people that have a disease they are interested in. These are the cases; then identify a group of similar people that do not have the disease and these are the controls. Then they look back in their history to look at exposure
Example: Lung cancer patients and patients without lung cancer and ask questions about their smoking history; You could ask a group of post-menopausal women with heart disease and without heart disease if they have used hormonal replacement therapies. Used a lot with rare diseases and disease that have a long time in between exposure and disease. Cohort vs Case-Control study tests used Cohort Study: Relative Risk Case Control Study: Odds Ratio independent variable vs Dependent Variable independent: manipulated or controlled (compare; predicted) dependent: variable being tested or measured (outcome) ICP vs DO example gender differences and study habits
- ex: the number of students in a class (you can't have half a student) HR: Cant have a HR of 80. continuous: measured only limited by the tool you are using
- ex: a dog's weight frequency distribution graph Positive vs negative skew Negative Skew pulls mean to the left with the median and mode ahead of it Positive skew mean is in front of mode and median
- ... Mean, Median, Mode +... Mode, Median, Mean Has to do with outliers Box Plot (Box and Whisker Plot) 1.Order the Data 2.Find the Median of the full data set (2nd Quartile) 3.Find the Median of the "whiskers" aka the 25th percentile (1st Quartile) and the 75th percentile (3rd quartile) (Include the Median for the entire data set) 4.Draw x axis then plot the medians you found. The box shows that 50% of the values are between the 25th % and 75% percentile. "Whiskers" are the range. central tendency
mean; average; Interval/ratio level of data; outliers effect number median; middle; Ordinal,Interval/ratio level of data mode; Most frequent; any type of data Stanard Deviation average distance from the mean(shows what majority (2/3) of the scores are by adding and subtracting it from Mean) 68 - 95 - 99 rule in a normal model, about 68% of values fall within 1 standard deviation of the mean, about 95% fall within 2 standard deviations of the mean, and about 99% fall within 3 standard deviations of the mean null hypothesis the hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error. There is no relationship between pain and healing time research hypothesis The hypothesis that the variables under investigation are related in the population - that the observed effect based on sample data is true in the population.
p 0.08>0. Sensitivity Ability to correctly screen or diagnosis a condition Example; Mammograms Sensitivity would tell you how sensitive is a Mammogram at actually identifying cases of breast cancer a/(a+c) Specificity Ability to correctly screen out people without the condition Specificity: The ability of the mammogram to screen out people that DO NOT have breast cancer. d/(b+d) Sensitivty vs Specificity
Sensitivity a/(a+c) Specificity d/(b+d) -------D----No D +T. 18. 4
- T. 2. 76 18/(18+2) = 18/20 = .9 = 90% 76/(4+76) = 76/80 = 0.95 = 95% Independent t-test A statistical test to determine whether there are significant differences between two independent groups' means being tested on the same dependent variable. Ex: Drinking OJ daily and increasing memory; Compared mean scores of test between 2 groups one that drank OJ and one that did not Levene's test test for homogeneity of variance Paired t-test A test designed to determine the statistical difference between two groups' means where the participants in each group are either the same or matched pairs.
Post Hoc test in ANOVA Needed when there is a significant ANOVA and k>2. A significant ANOVA indicates that at least one pair of group means is different (Reject Null hypothesis) To see which one is significant need to look at post hoc test p values ( sig level) to see which is greater than 0.05. Whichever one is greater than 0.05 they are not as significant; the ones that are lower than 0.05 is the one that is more statistically significant 2 way ANOVA More than 1 IV effect on a DV What is the effect of IV A on DV? What is the effect of IV B on DV? Example: Students reported interest in politics between male and female and different education levels. interpreting odds ratio How many more times likely the odds are to find a exposure in someone that has disease as compared to finding the exposure of someone without the disease =1 No difference in exposure between 2 groups; exposure does not have an influence on disease OR >1 You have an increased frequency in finding the exposure in the diseased group OR <1 You have a decreased risk of finding the exposure in the diseased group
OR=odds of exposure in those with disease/odds of exposure in those without disease example: Examined whether there was an increased odds of advanced maternal age among children with autism compared to children without autism. OR=1.38 for each 10-year increment in maternal age (1-1.38)x100=0.38x100=38% Children with autism had a 38% increased odds of being born to a mother with advanced maternal age compared to children without autism. relative risk How many times more or less likely a person who has a exposure will be to develop a disease vs someone that does not have the exposure. RR=1 No increased or decreased risk RR>1 People exposed have a increased risk of outcome vs not exposed RR<1 Exposed group is less likely to get the disease vs the unexposed group (exposure is protective) Example: RR= 0.80 women who exercised 3-9 hours per week compared to women who engaged in less than 3 hours per week
Determines whether two uncorrelated means differ significantly when data are nonparametric Like the T test for independent samples but assumptions not met. Compare the MEDIANS not the means Wilcoxon Signed Rank Test a non-parametric test that looks for differences between two related samples. It is the non-parametric equivalent of the paired t-test. Cannot use with ordinal level of data Kruskal-Wallis test The non-parametric equivalent to the one-way ANOVA. Chi-square a common statistic used to analyze nominal and ordinal data to find differences between groups
Chi-square assumptions: Expected counts are greater than 1 and no more than 20% of cells are less than 5. If that is not met then switch to Fisher's exact test.
