Category: Analysis of variance (created 2007-06-20). Analysis of
variance (ANOVA) is an approach that allows you to compare a continuous outcome
variable across a factor representing three or more groups and to examine
interactions among factors.
Closely related categories:
Articles are arranged by date with the most recent entries at the top. You
can find outside resources at the bottom of
this page. Other entries about analysis of variance can be found in the
analysis of variance page at the
StATS website.
2008
- P.Mean: Using ANOVA for a sum of Likert scaled
variables (created 2008-10-09). I want to analyse data derived from a
questionnaire. The range of possible values that my variable can take goes
from 20 to 100. No evidence for rejecting the hypothesis of normality was
found. I would therefore apply an ANOVA, but I still have some doubts whether
this methods of analysis is valid, since the range of my dependent variable is
not [- infinity;+ infinity]. Is the ANOVA a valid method of analysis or are
there other approaches I can apply?
Outside resources:
Assessing the Performance of a Sensory Panel -
Panelist monitoring and tracking. Martin Kermit, Valérie Lengard. Journal
of Chemometrics 2006: 19(3); 154-61.
[PDF].
Description: This article shows a range of statistical analyses that can be
used in a typical senory panel experiment.
Multiple Comparisons with Repeated Measures. David C. Howell,
University of Vermont. Excerpt: "One of the commonly asked questions on
listservs dealing with statistical issue is 'How do I use SPSS (or whatever
software is at hand) to run multiple comparisons among a set of repeated
measures?' This page is a (longwinded) attempt to address that question. I
will restrict myself to the case of one repeated measure (with or without a
between subjects variable), but the generalization to more complex cases
should be apparent." This website was last verified on 2008-URL:
www.uvm.edu/~dhowell/StatPages/More_Stuff/RepMeasMultComp/RepMeasMultComp.html
Regression with SAS. Chapter 5: Additional coding systems for categorical
variables in regression analysis. Xiao Chen, Phil Ender, Michael
Mitchell, Christine Wells, UCLA Academic Technology Services. Excerpt:
Categorical variables require special attention in regression analysis
because, unlike dichotomous or continuous variables, they cannot by entered
into the regression equation just as they are. For example, if you have a
variable called race that is coded 1 = Hispanic, 2 = Asian 3 = Black 4 =
White, then entering race in your regression will look at the linear effect of
race, which is probably not what you intended. Instead, categorical variables
like this need to be recoded into a series of variables which can then be
entered into the regression model. There are a variety of coding systems that
can be used when coding categorical variables. Ideally, you would choose a
coding system that reflects the comparisons that you want to make. In Chapter
3 of the Regression with SAS Web Book we covered the use of categorical
variables in regression analysis focusing on the use of dummy variables, but
that is not the only coding scheme that you can use. For example, you may want
to compare each level to the next higher level, in which case you would want
to use "forward difference" coding, or you might want to compare each level to
the mean of the subsequent levels of the variable, in which case you would
want to use "Helmert" coding. By deliberately choosing a coding system, you
can obtain comparisons that are most meaningful for testing your hypotheses.
This website was last verified on 2008-URL: www.ats.ucla.edu/stat/sas/webbooks/reg/chapter5/sasreg5.htm
Data sets:
Nambeware
Polishing Times. DASL. Excerpt: Authorization: free use.
Description: Nambe Mills manufactures a line of tableware made from sand casting
a special alloy of several metals. After casting, the pieces go through a series
of shaping, grinding, buffing, and polishing steps. In 1989 the company began a
program to rationalize its production schedule of some 100 items in its
tableware line. The total grinding and polishing times listed here were a major
output of this program. Number of cases: 59. Variable Names: 1. BOWL: Bowl (1)
or not (0); 2. CASS: Casserole (1) or not (0); 3. DISH: Dish (1) or not (0); 4.
TRAY: Tray (1) or not (0); 5. DIAM: Diameter of item, or equivalent (inches); 6.
TIME: Grinding and polishing time (minutes); 7. PRICE: Retail price ($). Note:
Items not classed as bowl, casserole, dish, or tray are plates. This website
was last verified on 2008-URL: lib.stat.cmu.edu/DASL/Datafiles/nambedat.html
All of the material above this paragraph is licensed under a
Creative Commons Attribution 3.0 United States License. This page was written by
Steve Simon and was last modified on
2008-12-03. The material
below this paragraph links to my
old website, StATS. Although I wrote all of the material
listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright
ownership of this material. The brief excerpts shown here are included under
the fair use provisions of U.S. Copyright laws.
2008
- Stats: When does heterogeneity
become a concern? (June 5, 2008). Dear Professor Mean, I have an ANOVA
model and I am worried about heterogeneity--unequal standard deviations in
each group. How should I check for this?
- Stats: What statistic should I use
when? (January 4, 2008). Someone was asking about a multiple choice
question on a test that reads something like this: A group of researchers
investigating in patients with diabetes on the basis of demographic
characteristics and the level of diabetic control. Select the most
appropriate statistical method to use in analyzing the data: a t-test, ANOVA,
multiple linear regression, or a chi-square test. This is one of the more
vexing things that people face--what statistic should I use when.
2007
- Stats: Analyzing data from a simple
crossover design (August 22, 2007). A doctor brought
me some data from a crossover design and asked me to help analyze it. The
analysis was a bit trickier than I had expected, so I reviewed some of the
material in Stephen Senn's book.
2006
- Stats: (Seminar notes) Confidence
intervals for a variance ratio (July 17, 2006). One of the talks at the
18th Annual Applied Statistics in Agriculture Conference, sponsored by Kansas
State University was "Selecting the Best Confidence Interval for a Variance
Ratio (or Heritability)" by Brent Burch, Northern Arizona University. Here
are my notes from that talk.
- Stats: (Seminar notes) Adjustments
for multiple comparisons (July 17, 2006). One of the talks at the 18th
Annual Applied Statistics in Agriculture Conference, sponsored by Kansas
State University was "A Comparison of Multiple Tests Procedures: Spinosad as
a Treatment for Lice on Cattle" by Zhanglin Cui, Eli Lilly and Company.
Daniel H. Mowrey, Alan G. Zimmermann, and Douglas E. Hutchens, also of Eli
Lilly and Company were co-authors.
-
Stats: Post hoc comparisons (March
15, 2006). Dear Professor Mean, I need to run multiple comparisons
among all possible pairs of means following an analysis of variance test.
What is the best approach? Tukey? Scheffe? Bonferroni?
2005
- Stats: When the F test is significant, but
Tukey is not (September 9, 2005). Someone asked me how to interpret a one
factor analysis of variance where the overall F test was significant, but the
Tukey folloup test comparing all four group means was not significant for any
pair of means.
2004
- Stats: Multiple degree of freedom tests
(September 22, 2004). Someone sent me an email describing a situation
where an interaction effect in SPSS had a large p-value, but one of the
individual components of that interaction had a small and statistically
significant p-value. This can occur in many statistical models where you are
testing a factor or interaction that involves multiple degrees of freedom.
2003
- Stats: Guidelines for ANOVA models (June 20,
2003). Dear Professor Mean, I wanted to compare two groups in my
research, those who completed every test battery, and those who completed
only some of them. I ran ANOVAs on age, iq, adhd score, and so forth. My
professor says that I should have used a t-test instead. Why can't I use
ANOVA. Isn't ANOVA better than a t-test? --Angry Anastasia
2002
2001
- Stats: Unequal group sizes (November 2, 2001).
Dear Professor Mean: I am comparing several groups of subjects, but the
number of subjects in each group differ quite a bit. How does this affect the
assumptions in analysis of variance?
What now?
Browse other categories at this site
Browse through the most recent
entries
Get help
This work is licensed under a
Creative
Commons Attribution 3.0 United States License. This page was written by
Steve Simon and was last modified on
2008-12-03.