Testing for Marginal Independence Between Two Categorical

Report
Testing for Marginal Independence
Between Two Categorical Variables
with Multiple Responses
Robert Jeutong
Outline
• Introduction
– Kansas Farmer Data
– Notation
• Modified Pearson Based Statistic
– Nonparametric Bootstrap
– Bootstrap p-Value Methods
• Simulation Study
• Conclusion
Introduction
• “pick any” (or pick any/c) or multiple-response
categorical variables
• Survey data arising from multiple-response categorical
variables questions present a unique challenge for
analysis because of the dependence among responses
provided by individual subjects.
• Testing for independence between two categorical
variables is often of interest
• When at least one of the categorical variables can have
multiple responses, traditional Pearson chisquare tests
for independence should not be used because of the
within-subject dependence among responses
Intro cont’d
• A special kind of independence, called marginal
independence, becomes of interest in the presence of
multiple response categorical variables
• The purpose of this article is to develop new
approaches to the testing of marginal independence
between two multiple-response categorical variables
• Agresti and Liu (1999) call this a test for simultaneous
pair wise marginal independence (SPMI)
• The proposed tests are extensions to the traditional
Pearson chi-square tests for independence testing
between single-response categorical variables
Kansas Farmer Data
• Comes from Loughin (1998) and Agresti and Liu
(1999)
• Conducted by the Department of Animal Sciences
at Kansas State University
• Two questions in the survey asked Kansas farmers
about their sources of veterinary information and
their swine waste storage methods
• Farmers were permitted to select as many
responses as applied from a list of items
Data cont’d
• Interest lies in determining whether sources of
veterinary information are independent of waste
storage methods in a similar manner as would be
done in a traditional Pearson chi-square test
applied to a contingency table with singleresponse categorical variables
• A test for SPMI can be performed to determine
whether each source of veterinary information is
simultaneously independent of each swine waste
storage method
Data cont’d
• 4 × 5 = 20 different 2 × 2 tables can be
formed to marginally summarize all possible
responses to item pairs
Professional consultant
Lagoon
1
0
1
34
109
0
10
126
• Independence is tested in each of the 20 2 × 2
tables simultaneously for a test of SPMI
Data cont’d
• The test is marginal because responses are
summed over the other item choices for each
of the multiple-response categorical variables
• If SPMI is rejected, examination of the
individual 2 × 2 tables can follow to determine
why the rejection occurs
Notation
• Let W and Y = multiple-response categorical
variables for an r × c table’s row and column
variables, respectively
• Sources of veterinary information are denoted by
Y and waste storage methods are denoted by W
• The categories for each multiple-response
categorical variable are called items (Agresti and
Liu, 1999); For example, lagoon is one of the
items for waste storage method
• Suppose W has r items and Y has c items. Also,
suppose n subjects are sampled at random
Notation cont’d
• Let Wsi = 1 if a positive response is given for item
i by subject s for i = 1,.. ,r and s = 1,.. ,n; Wsi = 0
for a negative response.
• Let Ysj for j = 1,.., c and s = 1..,n be similarly
defined.
• The abbreviated notation, Wi and Yj , refers
generally to the binary response random variable
for item i and j, respectively
• The set of correlated binary item responses for
subject s are
• Ys = (Ys1, Ys2,…,Ysc) and Ws = (Ws1, Ws2,…,Wsr )
Notation cont’d
• Cell counts in the joint table are denoted by ngh
for the gth possible (W1…,Wr ) and hth possible
(Y1…,Yc )
• The corresponding probability is denoted by τgh.
Multinomial sampling is assumed to occur within
the entire joint table; thus, ∑g,h τgh = 1
• Let mij denote the number of observed positive
responses to Wi and Yj
• The marginal probability of a positive response to
Wi and Yj is denoted by πij and its maximum
likelihood estimate (MLE) is mij/n.
Joint Table
SPMI Defined in Hypothesis
• Ho: πij = πi•π•j for i = 1,...,r and j = 1,...,c
• Ha: At least one equality does not hold
• where πij = P(Wi = 1, Yj = 1), πi• = P(Wi = 1), and
π•j = P(Yj = 1). This specifies marginal
independence between each Wi and Yj pair
• P(Wi = 1, Yj = 1) = πij
• P(Wi = 1, Yj = 0) =πi• − πij
• P(Wi = 0, Yj = 1) = π•j − πij
• P(Wi =0, Yj = 0) = 1 − πi• − π•j + πij
Hypothesis
• SPMI can be written as ORWY,ij =1 for i =
1,…,r and j = 1,…,c where OR is the
abbreviation for odds ratio and
– ORWY,ij = πij(1 − πi• − π•j + πij)/[(πi• − πij)(π•j − πij )]
• Therefore, SPMI represents simultaneous
independence in the rc 2 × 2 pairwise item
response tables formed for each Wi and Yj pair
• Join independence implies SPMI but the
reverse is not true
Modified Pearson Statistic
• Under the Null
Yj
Wi
1
0
1
πij
πi• − πij
π•i
0
π•j − πij
1 − πi• − π•j + πij
1-π•i
π•j
1-π•j
• (1,1), (1,0), (0,1), (1,1)
The Statistic
Nonparametric Bootstrap
• To resample under independence of W and Y,
Ws and Ys are independently resampled with
replacement from the data set.
• The test statistic calculated for the bth resample
of size n is denoted by X2∗S,b.
• The p-value is calculated as
– B-1∑bI(X2∗S,b ≥X2S)
• where B is the number of resamples taken and
I() is the indicator function
Bootstrap p-Value Combination
Methods
• Each X2S,i,j gives a test for independence between
each Wi and Yj pair for i = 1,…,r and j = 1,…,c.
The p-values from each of these tests (using a χ21
approximation) can be combined to form a new
statistic p tilde
• the product of the r×c p-values or the minimum of
the r×c p-values could be used as p tilde
• The p-value is calculated as
– B-1∑bI(p* tilde ≤ p tilde)
Results from the Farmer Data
Method
My p-value Authors p-value
Bootstrap X2s
0.0001
<0.0001
Bootstrap product of p-values
0.0001
0.0001
Bootstrap minimum p-values
0.0047
0.0034
Interpretation and Follow-Up
• The p-values show strong evidence against SPMI
• Since X2S is the sum of rc different Pearson chi-square test
statistics, each X2S,i,j can be used to measure why SPMI is
rejected
• The individual tests can be done using an asymptotic χ21
approximation or the estimated sampling distribution of the
individual statistics calculated in the proposed bootstrap
procedures
• When this is done, the significant combinations are
(Lagoon, pro consultant), (Lagoon, Veterinarian), (Pit,
Veterinarian), (Pit, Feed companies & representatives),
(Natural drainage, pro consultant), (Natural drainage,
Magazines)
Simulation Study
• which testing procedures hold the correct size
under a range of different situations and have
power to detect various alternative hypotheses
• 500 data sets for each simulation setting
investigated
• The SPMI testing methods are applied (B =
1000), and for each method the proportion of
data sets are recorded for which SPMI is
rejected at the 0.05 nominal level
My Results
• n=100
• 2×2 marginal table
• OR = 25
Method
My p-value
Authors p-value
Bootstrap X2s
0.04
0.056
Bootstrap product of p-values
0.042
0.056
Bootstrap minimum p-values
0.036
0.044
Conclusion
• The bootstrap methods generally hold the
correct size

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