N580 Final
Study online at https://quizlet.com/_6ulfuf
1. statistics vs. pa- analysis on a sample vs. analysis on the entire population
rameters
2. overview to data quantitative: descriptive and inferential
analysis descriptive: simply describe a characteristic of a popula-
tion/sample or a phenomenon
inferential: hope to generalize from a sample to a pop-
ulation (based on probability)--we use parametric and
non-parametric statistics
3. parametric tests every statistical test has assumptions that must be met
and non-para- parametric tests have more stringent assumptions--two of
metric tests 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
4. analysis clearly the problem is the driving force--the sophistica-
tion 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
5. when you are be- you must look at what drives the tests-->the research
ginning to think question and the hypothesis--what are the variables and
about statistical how are they measured? what is the level of measure-
tests... ment?
6. if the question N & % (if data are nominal or categorical) or descriptive
focuses on de- statistics (if measures are interval-like in nature--mean,
scribing some standard deviation, range)
phenomena
, N580 Final
Study online at https://quizlet.com/_6ulfuf
7. if the research correlation
question or hy- Pearson's r (parametric test) or Spearman Rho/Kendall's
pothesis is inter- Tau (non-parametric test)
ested in a rela-
tionship between
two variables
8. if the research the independent t test (parametric) or the Mann Whitney
question or hy- U (if data weren't normally distributed)
pothesis is inter-
ested in a dif-
ference between
two independent
groups
9. if the research ANOVA (parametric) or the Kruskal Wallis (if data aren't
question or hy- normally distributed or if measure is ordinal)
pothesis is inter-
ested in a dif-
ference between
two or more inde-
pendent groups
10. if the re- the paired t test (parametric) or Wilcoxon (non-paramet-
search question ric)
or hypothesis
is interested in
differences in
two groups that
are dependent
(repeated mea-
sures)
11. if the research RANOVA (parametric) or Friedman (non-parametric)
question or hy-
pothesis is inter-
ested in the dif-
ference in two or
more groups that
are dependent
, N580 Final
Study online at https://quizlet.com/_6ulfuf
12. how can you what is the research question or hypothesis asking?
choose? 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?
13. knowledge of research: making observations or measurements on peo-
statistics is nec- ple, things, or events to answer a research question
essary to all who statistics: a set of procedures for describing those mea-
read or conduct surements--how do we quantify and report these mea-
research surements?
14. data the raw material of research
15. variable something that varies or takes on different values
we are always interested in variability and explaining vari-
ation
16. variables are discrete: finite number of value (almost like you can
also classified count)--obtained by counting
as... continuous: infinite number of value between any two
points--obtained by measuring
17. statistical meth- univariate: average cholesterol
ods are some- bivariate: average cholesterol (exercise group and seden-
times described tary group)
by the number of multivariate: average cholesterol (exercise: yes/no, diet:
variables in the good/bad, gender: M/F)
analysis
18. measurement key to capturing variables
assigning numbers to objects, events, etc. according to
the rules
some things are more difficult to measure than oth-
ers--consider temperature vs. self-efficacy
19. level of measure- influences what statistical tests can be chosen
ment 4 basic levels:
nominal
Study online at https://quizlet.com/_6ulfuf
1. statistics vs. pa- analysis on a sample vs. analysis on the entire population
rameters
2. overview to data quantitative: descriptive and inferential
analysis descriptive: simply describe a characteristic of a popula-
tion/sample or a phenomenon
inferential: hope to generalize from a sample to a pop-
ulation (based on probability)--we use parametric and
non-parametric statistics
3. parametric tests every statistical test has assumptions that must be met
and non-para- parametric tests have more stringent assumptions--two of
metric tests 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
4. analysis clearly the problem is the driving force--the sophistica-
tion 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
5. when you are be- you must look at what drives the tests-->the research
ginning to think question and the hypothesis--what are the variables and
about statistical how are they measured? what is the level of measure-
tests... ment?
6. if the question N & % (if data are nominal or categorical) or descriptive
focuses on de- statistics (if measures are interval-like in nature--mean,
scribing some standard deviation, range)
phenomena
, N580 Final
Study online at https://quizlet.com/_6ulfuf
7. if the research correlation
question or hy- Pearson's r (parametric test) or Spearman Rho/Kendall's
pothesis is inter- Tau (non-parametric test)
ested in a rela-
tionship between
two variables
8. if the research the independent t test (parametric) or the Mann Whitney
question or hy- U (if data weren't normally distributed)
pothesis is inter-
ested in a dif-
ference between
two independent
groups
9. if the research ANOVA (parametric) or the Kruskal Wallis (if data aren't
question or hy- normally distributed or if measure is ordinal)
pothesis is inter-
ested in a dif-
ference between
two or more inde-
pendent groups
10. if the re- the paired t test (parametric) or Wilcoxon (non-paramet-
search question ric)
or hypothesis
is interested in
differences in
two groups that
are dependent
(repeated mea-
sures)
11. if the research RANOVA (parametric) or Friedman (non-parametric)
question or hy-
pothesis is inter-
ested in the dif-
ference in two or
more groups that
are dependent
, N580 Final
Study online at https://quizlet.com/_6ulfuf
12. how can you what is the research question or hypothesis asking?
choose? 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?
13. knowledge of research: making observations or measurements on peo-
statistics is nec- ple, things, or events to answer a research question
essary to all who statistics: a set of procedures for describing those mea-
read or conduct surements--how do we quantify and report these mea-
research surements?
14. data the raw material of research
15. variable something that varies or takes on different values
we are always interested in variability and explaining vari-
ation
16. variables are discrete: finite number of value (almost like you can
also classified count)--obtained by counting
as... continuous: infinite number of value between any two
points--obtained by measuring
17. statistical meth- univariate: average cholesterol
ods are some- bivariate: average cholesterol (exercise group and seden-
times described tary group)
by the number of multivariate: average cholesterol (exercise: yes/no, diet:
variables in the good/bad, gender: M/F)
analysis
18. measurement key to capturing variables
assigning numbers to objects, events, etc. according to
the rules
some things are more difficult to measure than oth-
ers--consider temperature vs. self-efficacy
19. level of measure- influences what statistical tests can be chosen
ment 4 basic levels:
nominal
