N580 FINAL EXAM QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2026

N580 FINAL EXAM QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2026 statistics vs. parameters - Answers analysis on a sample vs. analysis on the entire population overview to data analysis - Answers quantitative: descriptive and inferential descriptive: simply describe a characteristic of a population/sample or a phenomenon inferential: hope to generalize from a sample to a population (based on probability)--we use parametric and non-parametric statistics parametric tests and non-parametric tests - Answers every statistical test has assumptions that must be met parametric tests have more stringent assumptions--two of the most critical assumptions: normal distribution of the data and level of measurement (must be interval-like in nature) non-parametric tests don't have such limitations we like parametric tests b/c they are more powerful and more likely to find a difference if there is one one chosen will depend on your data analysis - Answers clearly the problem is the driving force--the sophistication of analysis will never compensate for an insignificant problem always remember the research question or hypothesis always drives the analysis look at the hypothesis or question--should be able to begin to think about the type of analysis that would be appropriate when you are beginning to think about statistical tests... - Answers you must look at what drives the tests--the research question and the hypothesis--what are the variables and how are they measured? what is the level of measurement? if the question focuses on describing some phenomena - Answers N & % (if data are nominal or categorical) or descriptive statistics (if measures are interval-like in nature--mean, standard deviation, range) if the research question or hypothesis is interested in a relationship between two variables - Answers correlation Pearson's r (parametric test) or Spearman Rho/Kendall's Tau (non-parametric test) if the research question or hypothesis is interested in a difference between two independent groups - Answers the independent t test (parametric) or the Mann Whitney U (if data weren't normally distributed) if the research question or hypothesis is interested in a difference between two or more independent groups - Answers ANOVA (parametric) or the Kruskal Wallis (if data aren't normally distributed or if measure is ordinal) if the research question or hypothesis is interested in differences in two groups that are dependent (repeated measures) - Answers the paired t test (parametric) or Wilcoxon (non-parametric) if the research question or hypothesis is interested in the difference in two or more groups that are dependent - Answers RANOVA (parametric) or Friedman (non-parametric) how can you choose? - Answers what is the research question or hypothesis asking? are groups independent or dependent? what is the level of measurement of the variables? are the assumptions of the statistical test met, particularly that of normal distribution? knowledge of statistics is necessary to all who read or conduct research - Answers research: making observations or measurements on people, things, or events to answer a research question statistics: a set of procedures for describing those measurements--how do we quantify and report these measurements? data - Answers the raw material of research variable - Answers something that varies or takes on different values we are always interested in variability and explaining variation variables are also classified as... - Answers discrete: finite number of value (almost like you can count)--obtained by counting continuous: infinite number of value between any two points--obtained by measuring statistical methods are sometimes described by the number of variables in the analysis - Answers univariate: average cholesterol bivariate: average cholesterol (exercise group and sedentary group) multivariate: average cholesterol (exercise: yes/no, diet: good/bad, gender: M/F) measurement - Answers key to capturing variables assigning numbers to objects, events, etc. according to the rules some things are more difficult to measure than others--consider temperature vs. self-efficacy level of measurement - Answers influences what statistical tests can be chosen 4 basic levels: nominal ordinal interval ratio want to know about it when collecting data, when analyzing data (level of measurement will influence statistical test you can select) always measure at the highest level realistically possible: more powerful test, greater flexibility, more info nominal - Answers category: categorical variable (M/F/transgender, race, etc.--can go in one but not the other) can't treat mathematically if these are the dependent variable: they are reported by n and %, mode also used in: grouping (study has 3 independent interventions [groups] looking at an independent variable), logistic regression (results in odds ratios--dependent variable is categorical--given a certain treatment, what are the odds the patient is dead or alive) ex: preferred mode of transportation, blood type, gender non-parametric how are numbers assigned to nominal level data for statistical analysis? - Answers blood type 1=O+ 2=A+ 3=B+ 4=AB+ and so forth think about gender, race, marital status--how would you assign numbers to levels of these variables? we assign numbers to enter them in a computer recommend to always collect data at the highest level ordinal - Answers uses numbers to designate ordering of an attribute--relative standing describes an amount of some attribute but there is not an equivalent distance between each number (so really is ranking) think about track finishes--not an equivalent amount between 1st place and 2nd place, 2nd place and 3rd place, and so on examples of ordinal data - Answers socioeconomic status academic rank psychological inventories (mini-mental status exam, QOL scales, etc.) treatment of ordinal data varies - Answers parametric or non-parametric general rule is non-parametric tests should be used if there are 11 or so measures and if the scores are well-distributed, then parametric tests can be used interval - Answers amount of an attribute, equal distance between each number in terms of amount (no absolute 0 value) ex: Fahrenheit scale can be used in parametric tests so dependent variable is interval-like in nature and there is a normal distribution ratio - Answers same as interval but has an absolute 0 morphine in mgs, length in inches, Kelvin temp can make comparative statements (twenty pounds is twice as heavy as ten pounds) don't have to distinguish between ratio and interval data because statistically they are treated the same identifying the characteristics of data - Answers can the data be put in order? no=nominal do the data have units, including numbers of things? no=ordinal yes=metric do the data come from measuring or counting things? if measuring then continuous data, if counting then discrete data quantitative research - Answers descriptive or inferential inferential can be parametric or non-parametric none of this applies for qualitative studies what type of research questions are answered by descriptive statistics - Answers try to describe an event/phenomena that we don't know a lot about what are the most frequently perceived benefits of and barriers to medication and dietary compliance among two samples of patients with heart failure? what is the infant mortality rate among US immigrants from Iraq and Afghanistan? In addition, descriptive statistics are always used in describing the research sample and should always be presented in regard to outcome variables when you read results you always want to read... - Answers what were the values in the outcome variables statistically significant doesn't mean clinically significant/relevant descriptive statistics - Answers describe and summarize answer research questions, describe samples, and values of outcome variables not generalizable how are descriptive statistics reported? - Answers nominal data - N, %, and mode measures of central tendency: mean, median, and mode measures of variability: such as range and standard deviation measures of distribution: skew and kurtosis nominal data - Answers categorical marital status gender race N & % can also display findings graphically (such as bar, pie chart) bar graphs as opposed to histograms: spaces between them making sense of raw data or how do we first look at data? - Answers frequency distributions frequency - count of cases percent - % of time a given value occurs cumulative percent - percentage for given score combined with all percentages that preceded it as you collect data, you want to run frequency distribution to get a sense of what data look like frequency distributions - Answers can be grouped into class intervals/sets (useful for large samples) displayed graphically: bar graph (nominal data), pie chart, histograms (interval/ratio data), frequency polygon temperature=interval data--can use histogram box and whisker plot could also be used (dark line in middle of block=50th percentile, bottom of box=25th percentile, top=75th percentile--will show outliers) use of frequency distribution - Answers get a feel for data helps in cleaning the data identify % of missing values interval or ratio level of measurement - Answers measures of central tendency measures of dispersion measures of distribution sometimes used for ordinal data (controversial)--if there are sufficient number of measures measures of central tendency - Answers mode=most frequent value in a distribution--can be used also with nominal data median=middle value--score wherein 50% of scores are above and 50% are below--can be used when data are really skewed mean=avg value--add all values in a distribution and divide by total number of values--used with interval or ratio data mean - Answers works best for symmetrical distributions extreme values can distort the mean can be manipulated as it is algebraic appropriate if distribution is normal inappropriate if distribution is abnormal not practical if data are really skewed median - Answers simply the mid-point 50% percentile odd # of scores - take middle value even # of scores - add the middle 2 and divide by 2 not sensitive to extreme scores - useful for skewed data median = (N+1)/2 mode - Answers most frequent score even if you have nominal data you can report the mode bimodal - two modes primary and secondary modes don't usually see it reported in research studies--if it is, it's reported to make a point perfect normal distribution - Answers mean, median, mode are the same see mean reported most often in research studies measures of dispersion, variability, or scatter - Answers nothing in statistics that is more important than variability standard deviation, variance (won't see it reported in literature), interpercentile/interquartile measures (IQR--belongs with median), range (difference between highest and lowest) standard deviation - Answers most commonly reported measure of variability - avg. amount by which scores vary around the mean reported in same unit as the variable like the mean it is sensitive to extreme values and like the mean it is algebraic normal distribution - Answers 68% of scores will fall within 1 SD of mean 95% fall within 2 SD of mean 99.7% will fall within 3 SD of mean variance - Answers not usually reported in descriptive statistics avg. of the squared deviations around the mean the squared SD look at the number and it doesn't mean anything to you range - Answers difference btwn minimum and maximum value report as low and high score interquartile range - Answers used if you have skewed data or data you don't really want to report based on percentages 75th percentile minus 25th percentile so gives you middle 50% Q3-Q1 can adjust this how you want won't see this very often use the interquartile range if range isn't representative of data symmetry of distribution - Answers shape: skew, kurtosis perfect normal distribution: skew=0, kurtosis=0 skew=more important than kurtosis authors won't report if data aren't skewed but will report if they are skew: named in direction of tail (positive skew, negative skew)--mean and standard deviation won't be appropriate kurtosis: refers to height of curve/distribution--positive=too peaked=leptokurtic--negative=too flat=platykurtic inferential statistics - Answers techniques used generalize from characteristics of a small group to a larger group that is unmeasured, i.e. the researcher via inferential statistics is able to infer the characteristics of the larger group from the measured characteristics of the smaller group allows researchers to draw conclusions beyond their sample to the larger population don't allow you to prove anything--can only support findings statistical inferences are not subjective there is always the possibility of error population - Answers all the possible members of a group defined by the researcher sample - Answers any number of cases less than the total number of cases in the population from which it was drawn