SEE STATISTICAL TEST CHART+++++++++++++++++++++
Simple regression - one independent one dependent.
Multiple regression is unplanned regression: 2 measured variables –**more than one independent
variable one independent (unplanned approach)
Step wise – multiple regression: if you have more than one independent variable – it puts the
independent variables into the equation in a planned approach
Multivariate regression – one independent and more than one dependent (more than one outcome
score)
Multivariate Multiple regression - More than one independent and dependent variable
** you can never have more variables than subjects.
Set-Wise - allows you to put two sets of subsets (6 sub scales of ELA as a set) rather than as individual
variables. If any 6 predict the outcome at a given time you have a better chance of less testing.
Logistic regression – important when you are looking at an outcome that is dichotomous – did you pass
or fail (same as a categorical – but binary) It requires a logarithmic transformations. (use if probability is
low 95/5)
Ex – is math performance in school a predictor of ELA (the computer shows pass fail and
determines probability of passing) if I know score and prediction goes up I know that variable
was in important predictor.
** discriminate analysis when the outcome is rare (probability is high – 60/40)
***In correlation and regression the sign indicates direction – the value represents the strength of the
relationship between 2 variables (sample exam question)
Correlation /Regression graph example – attendance and ELA score chart –(in last set off notes) –
regression line called the least squares regression line – anything below is a minus – anything above is a
plus: add minus and plus to always end at zero (same as a mean). Line must be as close to every data
point as possible.
Count the space/square the distance and compare. (- 1 1/2 )2 + (1 ½) 2 gives the best possible difference
– SPSS will draw the line.
The dissertation that Dr. Fiedler reviewed tonight: http://gradworks.umi.com/34/59/3459114.html and
http://search.proquest.com/docview/873563740 (not full txt but first 12 pages)
Analysis of variance has two degrees of freedom. – sample dissertation
Cohort - Degrees of freedom – number of subjects -1
Within the group - DOF for groups – total number of subject - # of groups
Two types of interactions – see repeated measure notes
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lines that never touch – ordinal interaction
When they cross it’s a disordinal interaction
When cohort is significant it is a MAIN effect; a MAIN effect for education type & an interaction
between cohort and type.
Each type produces a line – main effects that have parallel lines will have no interaction.
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Sample exam question concepts:
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Mean differences in the numerator and standard error in the denominator describe which test?
o Student t-test
Be able to decide what test to use when you have 2 measured independent variable & 1
dependent variable (see notes below)
F-Ratio = analysis of variance – homogeneity! (review)
One way ANOVAs f ratio – at least one mean is different from the others
3 group ANOVA the researcher may choose to use any two tests = planned contrast
o 3 tests = post hoc testing (all comparisons)
Riders vs. non riders relationship with motorcycle riding vs. head injuries: chi-square
(categorical and categorical)
Multiple regression approach - put in one variable (two overlapping circle) influence could be
taken out – where they overlap it is multiple regression. If you are in this but do not have
control of variable – the computer picks out which goes in first, second, third – it finds the one
with the highest correlation and puts it in first.
Performance in the class : do certain kids perform better in math – what effects math scores
(gender; attendance; academic achievement) whatever you put in first you control (gender
related to math score) the variance they share is in the math score due to gender.
Step wise regression: when you pick the order of the variables.
Sample questions: answers are bolded
100 student study to show impact of computerized games on learning. 1 group got course
material in video game form; other group got reading assignments of similar material in a time
period. Student prior knowledge was used prior to, following experimental treatments and
using exam scores.
Which type of statistical procedure should be employed to test hypothesis of the study?
1.
2.
3.
4.
5.
1 way multivariate repeated measures of variance
Two way anova
Repeated measures anova
Regression
Independent t tests
NOTE:*****Video game group (1 variable with two categories); dependent variable is student
learning
It is a one way repeated measure ANOVA…..but go to chart number 2 regarding Choice #1
Between subject or not – yes there is one between subject categorical data; and I have 1
dependent variable – you get an univariate or multivariate approach (you must test for the
assumption of homogeneity of variance) see previous notes from today** This must be
tested for in t-test and analysis of variance
Univariate approach is more powerful – but multivariate would be conducted
DEFINITION of repeated measures– measuring the same subjects with the same instrument at
more than one time point (pre-post test would be give away)
Relationship between time spent on video games and change in performance For THE VIDEO
GROUP ONLY: what statistical procedure do you use?
1.
2.
3.
4.
ANOVA
Correlation – simply shows a relationship between two variables
Regression – test a hypothesis (specific relationship between two variables)
Chi-Square
**change means pre test pot test – this almost always means repeated measures (**but not on
this particular test)
1 meausredx1measured = single measure x single measure
Research suggests only children are less likely to participate in computers than those with
siblings. Suppose they want to study the effects of family group and treatment on student
performance. Which statistical procedure should be used to test this?
1.
2.
3.
4.
5.
1 way multivariate repeated measures ANOVA
Paired t-tests
Two way multivariate repeated measures ANOVA
Correlation
Chi-square
***rule out the NO’s to limit the choices ( the researchers would have to elaborate on methods
of data gathering).
FACULTY RANK vs teaching evaluation
Instructor
A prf
asc prof
full prof
AA
36
62
43
50
A
48
50
35
43
BA
30
13
20
35
What statistical tool would be the best to test the null hypothesis of no association between
evaluation and rank?
1.
2.
3.
4.
5.
Pearson correlation coefficient
Simple regression
Multiple regression
Chi-square to test if there is any association
ANOVA
REASON: 2 variables (teaching evalution results (categorical); rank (categorical) ) Indep. Cat and
dep Cat.
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EXAMPLE of CP vs NON diabled based on self esteem scores:
Independent CP vs non CP - categorical
Dependent - for self esteem measure – measured
TEST – Analysis of Variance
Males vs females; & CP vs non disabled – 2 categorical; one measured
TEST = two way ANOVA (two categorical – 2 way; 3 categorical 3 way…)
To compare groups on the set of seven subscale scores required which type of statistical
procedure?
1.
2.
3.
4.
Multivariate repeated measures ANOVA
Multivariate ANOVA
Multivariate Analysis of covariance
Multivariate regression
CODEBOOKS example will be on the final Repeated Measures - pre to post tests!