Copyright © 2012 by Nelson Education Limited. Chapter 10 Hypothesis Testing IV: Chi Square 10-1

Copyright © 2012 by Nelson Education Limited. Bivariate (Crosstabulation) Tables The basic logic of Chi Square Perform the Chi Square test using the five-step model Limitations of Chi Square In this presentation you will learn about: 10-2

Copyright © 2012 by Nelson Education Limited. Bivariate tables: display the scores of cases on two different variables at the same time. Independent variable Dependent variable Cells Row and column marginals Total number of cases (n) Bivariate Tables 10-3

Copyright © 2012 by Nelson Education Limited. Columns are scores of the independent variable. –There will be as many columns as there are scores on the independent variable. Rows are scores of the dependent variable. –There will be as many rows as there are scores on the dependent variable. More on Bivariate Tables 10-4

Copyright © 2012 by Nelson Education Limited. Cells are intersections of columns and rows. –There will be as many cells as there are scores on the two variables combined. –Each cell reports the number of times each combination of scores occurred. Marginals are the subtotals. n is reported at the intersection of row and column marginals. More on Bivariate Tables (continued) 10-5

Copyright © 2012 by Nelson Education Limited.  Chi Square, χ 2, is a test of significance based on bivariate, crosstabulation tables.  Chi Square as a test of statistical significance is a test for independence. o Two variables are independent if the classification of a case into a particular category of one variable has no effect on the probability that the case will fall into any particular category of the second variable. Basic Logic of Chi Square 10-6

Copyright © 2012 by Nelson Education Limited. o Specifically, we are looking for significant differences between the observed cell frequencies in a table (f o ) and those that would be expected by random chance or if cell frequencies were independent (f e ): Basic Logic of Chi Square (continued) 10-7

Copyright © 2012 by Nelson Education Limited. Formulas for Chi Square 10-8

Copyright © 2012 by Nelson Education Limited. Is there a relationship between support for privatization of healthcare and political ideology? Are liberals significantly different from conservatives on this variable? o The table below reports the relationship between these two variables for a random sample of 78 adult Canadians. Computation of Chi Square: An Example Political Ideology Support ConservativeLiberal Total No Yes Total

Copyright © 2012 by Nelson Education Limited. Use Formula 10.2 to find f e. –Multiply column and row marginals for each cell and divide by n. (38*43)/78 = 1634 /78 = 20.9 (40*43)/78 = 1720 /78 = 22.1 (38*35)/78 = 1330 /78 = 17.1 (40*35)/78 = 1400 /78 = 17.9 Expected frequencies (f e ) Political Ideology Support Conservative Liberal Total No Yes Total Computation of Chi Square: An Example (continued) 10-10

Copyright © 2012 by Nelson Education Limited. A computational table helps organize the computations. fofo fefe f o - f e (f o - f e ) 2 (f o - f e ) 2 /f e Computation of Chi Square: An Example (continued) 10-11

Copyright © 2012 by Nelson Education Limited. Subtract each f e from each f o. The total of this column must be zero. fofo fefe f o - f e (f o - f e ) 2 (f o - f e ) 2 /f e Computation of Chi Square: An Example (continued) 10-12

Copyright © 2012 by Nelson Education Limited. Square each of these values fofo fefe f o - f e (f o - f e ) 2 (f o - f e ) 2 /f e Computation of Chi Square: An Example (continued) 10-13

Copyright © 2012 by Nelson Education Limited. Divide each of the squared values by the f e for that cell. The sum of this column is chi square fofo fefe f o - f e (f o - f e ) 2 (f o - f e ) 2 /f e χ 2 = 9.87 Computation of Chi Square: An Example (continued) 10-14

Copyright © 2012 by Nelson Education Limited. Independent random samples Level of measurement is nominal –Note the minimal assumptions. In particular, no assumption is made about the shape of the sampling distribution. The chi square test is nonparametric, or distribution-free. Performing the Chi Square Test Using the Five-Step Model Step 1: Make Assumptions and Meet Test Requirements 10-15

Copyright © 2012 by Nelson Education Limited. H 0 : The variables are independent Another way to state the H 0, more consistently with previous tests: H 0 : f o = f e H 1 : The variables are dependent Another way to state the H 1 : H 1 : f o ≠ f e Step 2: State the Null Hypothesis 10-16

Copyright © 2012 by Nelson Education Limited. Sampling Distribution = χ 2 Alpha =.05 df = (r-1)(c-1) = 1 χ 2 (critical) = Step 3: Select Sampling Distribution and Establish the Critical Region 10-17

Copyright © 2012 by Nelson Education Limited. χ 2 (obtained) = 9.87 Step 4: Calculate the Test Statistic 10-18

Copyright © 2012 by Nelson Education Limited. χ 2 (critical) = χ 2 (obtained) = 9.87 The test statistic is in the Critical (shaded) Region: –We reject the null hypothesis of independence. –Opinion on healthcare privatization is dependent on political ideology. Step 5: Make Decision and Interpret Results 10-19

Copyright © 2012 by Nelson Education Limited. The chi square test tells us only if the variables are independent or not. It does not tell us the pattern or nature of the relationship. –To investigate the pattern, compute %’s within each column and compare across the columns. Interpreting Chi Square 10-20

Copyright © 2012 by Nelson Education Limited. Column Percents (%) Political Ideology Support ConservativeLiberal No Yes Total 100% 100% This relationship has a clear pattern: people that support privatization of healthcare in Canada are more likely to be conservative, while those that oppose it are more likely to be liberal in their political ideology. o Chi square told us that this relationship is significant (unlikely to be caused by random chance) and now, with the aid of column percents, we know how the two variables are related. Interpreting Chi Square (continued) 10-21

Copyright © 2012 by Nelson Education Limited. 1.Difficult to interpret when variables have many categories. –Best when variables have four or fewer categories. 2.With small sample size, cannot assume that chi square sampling distribution will be accurate. –Small sample: High percentage of cells have expected frequencies of 5 or less. The Limitations of Chi Square 10-22

Copyright © 2012 by Nelson Education Limited. 3.Like all tests of hypotheses, chi square is sensitive to sample size. –As n increases, obtained chi square increases. –With large samples, trivial relationships may be significant. The Limitations of Chi Square 10-23

Copyright © 2012 by Nelson Education Limited. Important to Remember Statistical significance is not the same thing as substantive importance