N580 FINAL QUESTIONS WITH ANSWERS VERIFIED CORRECT

Statistics vs. Parameters - analysis on a sample vs. Analysis on the entire population Overview to data analysis - 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 - 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 - 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... - 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 - 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 - 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 - 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 - 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) - 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 - ranova (parametric) or friedman (non-parametric) How can you choose? - 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 - 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 - the raw material of research Variable - something that varies or takes on different values We are always interested in variability and explaining variation Variables are also classified as... - 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 - univariate: average cholesterol Bivariate: average cholesterol (exercise group and sedentary group) Multivariate: average cholesterol (exercise: yes/no, diet: good/bad, gender: m/f) Measurement - 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 - 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 - 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? - 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