N580 Final Exam Questions and Answers
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Terms in this set (161)
analysis on a sample vs. analysis on the entire
statistics vs. parameters
population
quantitative: descriptive and inferential
descriptive: simply describe a characteristic of a
population/sample or a phenomenon
overview to data analysis
inferential: hope to generalize from a sample to a
population (based on probability)--we use parametric
and non-parametric statistics
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
parametric tests and non-
(must be interval-like in nature)
parametric tests
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
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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
analysis
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
you must look at what drives the tests-->the research
when you are beginning
question and the hypothesis--what are the variables
to think about statistical
and how are they measured? what is the level of
tests...
measurement?
if the question focuses on N & % (if data are nominal or categorical) or
describing some descriptive statistics (if measures are interval-like in
phenomena nature--mean, standard deviation, range)
if the research question or correlation
hypothesis is interested in Pearson's r (parametric test) or Spearman
a relationship between Rho/Kendall's Tau (non-parametric test)
two variables
if the research question or the independent t test (parametric) or the Mann
hypothesis is interested in Whitney U (if data weren't normally distributed)
a difference between two
independent groups
if the research question or ANOVA (parametric) or the Kruskal Wallis (if data
hypothesis is interested in aren't normally distributed or if measure is ordinal)
a difference between two
or more independent
groups
if the research question or the paired t test (parametric) or Wilcoxon (non-
hypothesis is interested in parametric)
differences in two groups
that are dependent
(repeated measures)
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if the research question or RANOVA (parametric) or Friedman (non-parametric)
hypothesis is interested in
the difference in two or
more groups that are
dependent
what is the research question or hypothesis asking?
are groups independent or dependent?
how can you choose? what is the level of measurement of the variables?
are the assumptions of the statistical test met,
particularly that of normal distribution?
research: making observations or measurements on
people, things, or events to answer a research
knowledge of statistics is
question
necessary to all who read
statistics: a set of procedures for describing those
or conduct research
measurements--how do we quantify and report these
measurements?
data the raw material of research
something that varies or takes on different values
variable we are always interested in variability and explaining
variation
discrete: finite number of value (almost like you can
variables are also count)--obtained by counting
classified as... continuous: infinite number of value between any two
points--obtained by measuring
univariate: average cholesterol
statistical methods are
bivariate: average cholesterol (exercise group and
sometimes described by
sedentary group)
the number of variables in
multivariate: average cholesterol (exercise: yes/no,
the analysis
diet: good/bad, gender: M/F)
key to capturing variables
assigning numbers to objects, events, etc. according
measurement to the rules
some things are more difficult to measure than others-
-consider temperature vs. self-efficacy
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