N580 Final Test Questions and
Complete Solutions Graded A+
statistics vs. parameters - Answer: analysis on a sample vs. analysis on the entire population
overview to data analysis - Answer: 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 - Answer: 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 - Answer: 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... - Answer: 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 - Answer: 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 - Answer:
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 -
Answer: 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 - Answer: 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) - Answer: 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 - Answer: RANOVA (parametric) or Friedman (non-parametric)
how can you choose? - Answer: 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 - Answer: 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 - Answer: the raw material of research
variable - Answer: something that varies or takes on different values
, we are always interested in variability and explaining variation
variables are also classified as... - Answer: 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 - Answer:
univariate: average cholesterol
bivariate: average cholesterol (exercise group and sedentary group)
multivariate: average cholesterol (exercise: yes/no, diet: good/bad, gender: M/F)
measurement - Answer: 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 - Answer: 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 - Answer: 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
Complete Solutions Graded A+
statistics vs. parameters - Answer: analysis on a sample vs. analysis on the entire population
overview to data analysis - Answer: 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 - Answer: 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 - Answer: 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... - Answer: 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 - Answer: 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 - Answer:
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 -
Answer: 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 - Answer: 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) - Answer: 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 - Answer: RANOVA (parametric) or Friedman (non-parametric)
how can you choose? - Answer: 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 - Answer: 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 - Answer: the raw material of research
variable - Answer: something that varies or takes on different values
, we are always interested in variability and explaining variation
variables are also classified as... - Answer: 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 - Answer:
univariate: average cholesterol
bivariate: average cholesterol (exercise group and sedentary group)
multivariate: average cholesterol (exercise: yes/no, diet: good/bad, gender: M/F)
measurement - Answer: 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 - Answer: 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 - Answer: 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
