N580 Final
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 - answerclearly 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... - answeryou 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 - answerN & % (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 - answercorrelation
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 - answerthe 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 - answerANOVA (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) - answerthe 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 - answerRANOVA (parametric) or Friedman (non-
parametric)
how can you choose? - answerwhat 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 -
answerresearch: 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 - answerthe raw material of research
variable - answersomething that varies or takes on different values
we are always interested in variability and explaining variation
variables are also classified as... - answerdiscrete: 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 -
answerunivariate: average cholesterol
bivariate: average cholesterol (exercise group and sedentary group)
multivariate: average cholesterol (exercise: yes/no, diet: good/bad, gender: M/F)
measurement - answerkey 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 - answerinfluences 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 - answercategory: 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? - answerblood
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 - answeruses 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 - answersocioeconomic status
academic rank
psychological inventories (mini-mental status exam, QOL scales, etc.)
treatment of ordinal data varies - answerparametric 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 - answeramount of an attribute, equal distance between each number in terms
of amount (no absolute 0 value)
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 - answerclearly 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... - answeryou 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 - answerN & % (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 - answercorrelation
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 - answerthe 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 - answerANOVA (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) - answerthe 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 - answerRANOVA (parametric) or Friedman (non-
parametric)
how can you choose? - answerwhat 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 -
answerresearch: 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 - answerthe raw material of research
variable - answersomething that varies or takes on different values
we are always interested in variability and explaining variation
variables are also classified as... - answerdiscrete: 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 -
answerunivariate: average cholesterol
bivariate: average cholesterol (exercise group and sedentary group)
multivariate: average cholesterol (exercise: yes/no, diet: good/bad, gender: M/F)
measurement - answerkey 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 - answerinfluences 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 - answercategory: 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? - answerblood
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 - answeruses 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 - answersocioeconomic status
academic rank
psychological inventories (mini-mental status exam, QOL scales, etc.)
treatment of ordinal data varies - answerparametric 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 - answeramount of an attribute, equal distance between each number in terms
of amount (no absolute 0 value)
