Contingency Table Analysis contingency tables show frequencies produced

Contingency Table Analysis  contingency tables show frequencies produced

Contingency Table Analysis contingency tables show frequencies produced by cross-classifying observations e.g., pottery described simultaneously according to vessel form & surface decoration bowl jar olla polished 47 30 6 burnished 28 42 45 matte 3 8 25

most statistical tests for tables are designed for analyzing 2-dimensions only examine the interaction of two variables at one time most efficient when used with nominal data using ratio data means recoding data to a lower scale of measurement (ordinal) means ignoring some of the information originally available still, you might do this, particularly if you are interested in association between metric and non-metric variables e.g.: variation in pot size vs. surface decoration may decide to divide pot size into ordinal classes small small medium

large large rim diameter: small large 4 13 slip: specular non-specular 15 18 other options may let you retain more of the original information content could use a t-test to test the equality of the means makes full use of ratio data non-specular slip specular

slip polished burnished matte bowl 47 28 3 jar 30 42 8 olla 6

45 25 why do we work with contingency tables?? polished burnished matte bowl 47 28 3 jar 30 42 8

olla 6 45 25 because we think there may be some kind of interaction between the variables basic question: can the state of one variable be predicted from the state of another variable? if not, they are independent expected counts a baseline to which observed counts can be compared counts that would occur by random chance if the variables are independent, over the long run for any cell E = (col total * row total)/table total M

F PP 4 1 5 45% Pot 1 5 6 55% Total 5

6 11 45% 55% 2.3 2.7 5 2.7 3.3 6 5 6 11

significance = probability of getting, by chance, a table as or more deviant than the observed table, if the variables are independent deviant defined in terms of expected table no causality is necessarily implied by the outcome but, causality may well be the reason for observed association e.g.: grave goods and sex Fishers Exact Test just for 2 x 2 tables useful where chi-square test is inappropriate gives the exact probability of all tables with the same marginal totals as or more deviant than the observed table a c b

d 4 1 1 5 P = (a+b)!(a+c)!(b+d)!(c+d)! / (N!a!b!c!d!) P = 5!5!6!6! / 11!4!1!1!5! = 5*6!6! / 11! P = 5*6!6! / 11! = 5*6! / 11*10*9*8*7 P = 5*6! / 11*10*9*8*7 = 3600 / 55440 P = .065 a c b d 4 1 1 5

P = .065 use R (or Excel) if the counts arent too large > fisher.test(x) 0 5 5 5 1 6 5 6 11 3 2 5 2 4 6 5 6

11 1 4 5 4 2 6 5 6 11 4 1 5 1 5 6 5 6 11

2 3 5 3 3 6 5 6 11 5 0 5 0 6 6 5 6 11 (expected)

0 5 5 1 0.013 3 2 2 4 0.325 1 4 4 2 0.162 4 1

1 5 0.065 2 3 3 3 0.433 5 0 0 6 0.002 2.3 2.7 2.7 3.3 (observed)

P = 0.065+0.002 = 0.067 or P = 0.067+0.013 = 0.080 2-tailed test = 0.067+0.013 = 0.080 1-tailed test = 0.065+0.002 = 0.067 in R: M F PP 4 1 5 Pot 1 5

6 5 6 11 > fisher.test(x, alt = "two.sided") > fisher.test(x, alt = greater) [i.e.: H1: odds ratio > 1] Chi-square Statistic 2 k i 1 Oi Ei 2

Ei an aggregate measure (i.e., based on the entire table) the greater the deviation from expected values, the larger (exponentially!) the chisquare statistic one could devise others that would place less emphasis on large deviations |o-e|/eo-e|o-e|/e/e X2 is distributed approximately in accord with the X2 probability distribution X2 probabilities are traditionally found in a table showing threshold values from a CPD need degrees of freedom df = (r-1)*(c-1) just use R Status: low Ritual arch.: altar no altar low altar

no altar low altar no altar (7-11.8) 11.8 2 intermed. high 7 20 18 22 25 42 16 8 24 intermed. high 11.8 19.8

11.3 13.2 22.2 12.7 25 42 24 intermed. high 2.0 0.0 1.8 0.0 3.7 0.0 1.9 1.7 3.6 43 48 91 43 48

91 3.9 3.5 7.3 (43*24) 91 = 2 .025 X assumptions & problems 2 must be based on counts: not percentages, ratios or weighted data fails to give reliable results if expected counts are too low: obs. exp.

2 3 3 3 5 6 5 6 2.27 2.72 2.72 3.27 X2=0.74 P(Fishers)=1.0 rules of thumb 1. no expected counts less than 5

almost certainly too stringent 2. no exp. counts less than 2, and 80% of counts > 5 more relaxed (but more realistic) collapsing tables can often combine columns/rows to increase expected counts that are too low may increase or reduce interpretability may create or destroy structure in the table no clear guidelines avoid simply trying to identify the combination of cells that produces a significant result obs. counts 8 6 6 3 23

3 1 4 12 20 6 6 5 8 25 2 5 4 3 14 19 18 19 26 82 4.6 4.4

4.6 6.3 20 5.8 5.5 5.8 7.9 25 3.2 3.1 3.2 4.4 14 19 18 19 26 82 8 11 9 11

39 19 18 19 26 82 9.0 8.6 9.0 12.4 39 19 18 19 26 82 exp. counts 5.3 5.0 5.3 7.3 23

obs. counts 11 7 10 15 43 exp. counts 10.0 9.4 10.0 13.6 43 chi-square is basically a measure of significance it is not a good measure of strength of association can help you decide if a relationship exists, but not how strong it is 17 13 13

17 60 34 26 26 34 120 X2=1.07 alpha=.30 X2=2.13 alpha=.14 also, chi-square is a global statistic says nothing (directly) about which parts of a table may be driving a large chi-square statistic chi-square contributions from individual cells can help: low altar no altar

intermed. high 2.0 0.0 1.8 0.0 3.7 0.0 1.9 1.7 3.6 3.9 3.5 7.3 Monte Carlo test of X2 significance based on simulated generation of cell-counts under imposed conditions of independence randomly assign counts to cells: 23 15 38 14

6 20 8 13 21 45 34 79 significance is simply the proportion of outcomes that produced a X2 statistic >= observed not based on any assumptions about the distribution of the X2 statistic overcomes the problems associated with small expected frequencies G Test Oi G 2 Oi * log e i 1 Ei 2

k a measure of significance for any r x c table look up critical values of G2 in an ordinary chi-square table; figure out degrees of freedom the same way conforms to chi-square distribution better than the chi-square statistic an R function for G 2 gsq.test function(obs) { df (nrow(obs)-1) * (ncol(obs)-1) exp chisq.test(obs)$expected G 2*sum(obs*log(obs/exp)) 2*dchisq(G, df) } Measures of Association

Phi-Square (2) an attempt to remove the effects of sample size that makes chi-square inappropriate for measuring association divide chi-square by n 2=X2/n limits: 0: 1: variables are independent perfect association in a 2x2 table; no upper limit in larger tables 17 13 13 17 2=0.18 60

34 26 26 34 2=0.18 120 Cramers V also a measure of strength of association an attempt to standardize phi-square (i.e., control the lack of an upper boundary in tables larger than 2x2 cells) V= 2/m where m=min(r-1,c-1) ; i.e., the smaller of rows-1 or columns-1) limits: 0-1 for any size table; 1=highest possible association Yules Q for 2x2 tables only Q = (ad-bc)/(ad+bc)

a c b d Yules Q often used to assess the strength of presence / absence association Male burial + - Bone needles + 12 14 16 3 Q = -.72 range is 1 (perfect negative association) to 1 (perfect positive association); values near 0

indicate a lack of association Yules Q not sensitive to marginal changes (unlike Phi2) multiply a row or column by a constant; cancels out Source A Source B jars 19 6 ollas 10 15 Source A Source B jars 19 6 ollas 20

30 (Q=.65 for both tables) Yules Q cant distinguish between different degrees of complete association cant distinguish between complete and absolute association RHS LHS RHS LHS M 60 0 M 60 0 F 10 30

100 F 20 20 100 RHS LHS M 60 0 F 0 40 100 odds ratio easiest with 2 x 2 tables what are the odds of a man being buried on his right side, compared to those of a woman?? if there is a strong level of association between sex and burial position, the odds

should be quite different a b c d odds ratio = a c b d M RHS LHS F 29 11 40

14 33 47 43 44 87 29/11=2.64 14/33=0.42 2.64/0.42=6.21 if there is no association, the odds ratio=1 departures from 1 range between 0 and infinity >1 =positive association <1 =negative association Goodman and Kruskals Tau () proportional reduction of error how are the probabilities of correctly assigning cases to one set of categories improved by the knowledge of another set of categories?? Goodman and Kruskals Tau ()

limits are 0-1; 1=perfect association same results as Phi2 w/ 2x2 table sensitive to margin differences asymmetric get different results predicting row assignments based on columns than from column assignments based on rows =[P(error|o-e|/erule 1)-P(error|o-e|/erule 2)] / P(error|o-e|/erule 1) rule 1: random assignments to one variable are made with no knowledge of 2nd variable rule 2: random assignments to one variable are made with knowledge of 2nd variable B1 B2 A1 A2 6 14 20 B1 A1 6

A2 0 6 B2 0 14 14 20 Table Standardization even very large and highly significant X2 (or G2) statistics dont necessarily mean that all parts of the table are equally deviant (and therefore interesting) usually need to do other things to highlight loci of association or interaction which cells diverge the most from expected values? very difficult to decide when both row and column totals are unequal Percent standardization highly intuitive approach, easy to interpret often used to control the effects of samplesize variation have to decide if it makes better sense to standardize based on rows, or on columns usually, you want to standardize whatever it is you want to compare

i.e., if you want to compare columns, base percents on column totals you may decide to make two tables, one standardized on rows, the other on columns Fauna bear moose coyote rabbit dog deer Fauna bear moose coyote rabbit dog deer Site A 2

15 2 16 2 16 53 Site A 3.8 28.3 3.8 30.2 3.8 30.2 100 B C 1 5 0 8 3 8

25 B 4.0 20.0 0.0 32.0 12.0 32.0 100 0 10 0 12 0 7 29 C 0.0 34.5 0.0 41.4 0.0 24.1

100 3 30 2 36 5 31 107 MNIs Fauna bear moose coyote rabbit dog deer Site A 66.7 50.0 100.0 44.4 40.0

51.6 B 33.3 16.7 0.0 22.2 60.0 25.8 C 0.0 33.3 0.0 33.3 0.0 22.6 100 100 100 100 100 100 Binomial Probabilities

P(n,k,p): probability of k successes in n trials, with p probability of success in any one trial 5 1 3 4 3.7 2.3 13 n = 13 k=5 p = 3.7/13 4.3 2.7 13 Binomial Probabilities in R: > pbinom(k, n, p) easy to build into a function

K-S test for cumulative percents 100 90 90 80 80 70 70 percent 60 50 40 cumulative percent 100

60 50 40 30 30 20 20 10 10 100 90 80 cumulative percent 70 60 50 40

30 20 10 Cumulative Percent Graph 100 90 cumulative percent 80 70 60 50 40 30 20 10 (ordinal or ratio scale) good for comparing data sets some useful statistical

measures can be misleading when used with nominal data Percentages Sites A Types 1 2 3 4 5 6 7 8 9 B 5 45 5

5 5 5 20 5 5 100 Cumulative Percents Sites A B Types 1 5 2 50 3 55 4 60 5 65 6 70 7

90 8 95 9 100 C 5 0 48 5 5 5 5 22 5 100 5 30 5 5 5 5 35 5

5 100 120 A C 5 5 53 58 63 68 73 95 100 B 100 5 35 40 45 50 55

90 95 100 C 80 60 40 20 0 1 2 3 4 5 6

7 8 9 120 A B 100 C 80 60 120 40 A B 100

C 20 80 0 1 2 3 4 5 6 7 60 8 9

40 20 0 1 5 3 4 2 6 7 8 9 K-S test find Dmax: maximum difference between 2 cumulative

proportion distributions compare to critical value for chosen sig. level C*((n1+n2)/(n1n2))^.5 alpha =.05, C=1.36 alpha =.01, C=1.63 alpha =.001, C=1.95 example 2 mortuary data (Shennan, p. 56+) burials characterized according to 2 wealth (poor vs. wealthy) and 6 age categories (infant to old age) Rich Poor Infans I 6 23 Infans II 8

21 Juvenilis 11 25 Adultus 29 36 Maturus 19 27 Senilis 3 4

Total 76 136 burials for younger age-classes appear to be more numerous among the poor can this be explained away as an example of random chance? or do poor burials constitute a different population, with respect to age-classes, than rich burials? we can get a visual sense of the problem using a cumulative frequency plot: 1 0.9 0.8 rich 0.7

poor 0.6 0.5 0.4 0.3 0.2 0.1 Senilis Maturus Adultus Juvenilis Infans II Infans I 0 K-S test (Kolmogorov-Smirnov test) assesses the significance of the maximum divergence between two

cumulative frequency curves H0:dist1=dist2 an equation based on the theoretical distribution of differences between cumulative frequency curves provides a critical value for a specific alpha level observed differences beyond this value can be regarded as significant at that alpha level if alpha = .05, the critical value = 1.36*(n1+n2)/n1n2 1.36*(76+136)/76*136 = 0.195 the observed value = 0.178 0.178 < 0.195; dont reject H0 1 0.9 0.8 rich 0.7 poor 0.6

0.5 Dmax=.178 0.4 0.3 0.2 0.1 Senilis Maturus Adultus Juvenilis Infans II Infans I 0 example 2 statement/question: Oil exploration should be allowed in coastal California

age <= 30 age > 30 strongly disagree 8 7 mildly disagree 5 9 disagree 6 6 no opinion 0

1 agree 2 2 mildly agree 1 3 strongly agree 2 3 example 3 survey data 100 sites broken down by location and time: early late

Total piedmont 31 19 50 plain 19 31 50 Total 50 50 100

we can do a chi-square test of independence of the two variables time and location H0:time & location are independent alpha = .05 time location time location H0 H1 2 values reflect accumulated differences between observed and expected cell-counts expected cell counts are based on the assumptions inherent in the null hypothesis if the H0 is correct, cell values should reflect an even distribution of marginal totals piedmont plain Total

early 25 late 50 50 Total 50 50 100 chi-square = ((o-e)2/e) observed chi-square = 4.84 we need to compare it to the critical value in a chi-square table: chi-square = ((o-e)2/e) observed chi-square = 4.84 chi-square table: critical value (alpha = .05, 1 df) is 3.84 observed chi-square (4.84) > 3.84 we can reject H0

H1: time & location are not independent what does this mean? early late Total piedmont 31 19 50 plain 19 31 50 Total

50 50 100

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