chi-square cj 526 statistical analysis in criminal justice
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Chi-Square
CJ 526 Statistical Analysis in Criminal Justice
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Parametric vs Nonparametric Parametric
1. DV: Interval/Ratio
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Nonparametric Nonparametric
1. DV: Nominal/ordinal
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Chi-Square Test for Goodness of Fit One sample, DV is at
Nominal/Ordinal Level of Measurement
Determines whether the sample distribution fits some theoretical distribution
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Null Hypothesis
1. Population is evenly distributed Or Some other distribution, such as the
normal distribution
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Observed Frequency Number of individuals from the
sample who are classified in a particular category
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Expected Frequency The frequency value for a
particular category that is predicted from the null hypothesis and the sample size
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Chi-Square Statistic Sum of (Observed - Expected)2
divided by Expected
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Degrees of Freedom df = C - 1 where C is the number of
categories The degrees of freedom are the
number of categories that are free to vary
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Interpretation If H0 is rejected, distribution is
different from what is expected
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Report Writing: Results Section The results of the Chi-Square Test
for Goodness of Fit involving <IV> were (not) statistically significant, 2 (df) = <value>, p < .05.
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Report Writing: Discussion Section It appears as if the <sample> is
<not> distributed as expected.
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Example Concerned about health, neither
concerned or not concerned, not concerned about health
Could assume that a sample would be equally split among these three categories i.e., 120 subjects, 40 would say concerned, 40 neither, 40 not concerned
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Example
O E O-E (O-E)^2
/E
60 40 20 400 10
40 40 0 0 0
20 40 20 400 10
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Chi square Chi square = 20 D.f. = 2 See p. 726 Chi square = 20, p < .01 The distribution is significantly
different from the expected distribution
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Example Dr. Zelda, a correctional
psychologist, is interested in determining whether the intelligence of delinquents enrolled in a state training school is normally distributed
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Distribution of Intelligence in the General Population
IQ Range Z-scorePercentage of
General Population
Below 60 -3 .0228 (23)
60-85 -2 .1359 (136)
86-100 -1 .3413 (341)
101-115 +1 .3413 (341)
116-130 +2 .1359 (136)
131+ +3 .0228 (23)
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Distribution of Intelligence in Dr. Zelda’s School
Below 60 119
60-85 150
86-100 687
101-115 32
116-130 12
131+ 0
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1. Number of Samples: 12. Nature of Samples: N/A3. N/A4. IV: School enrolled in5. DV: IQ categories6. Target Population: all delinquents
enrolled in the state training school
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7. Inferential Test: Chi-Square Test for Goodness of Fit
8. H0: The distribution of frequencies of the IQ categories for the sample will not be different from the population distribution of frequencies of the IQ categories
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9. H1: The distribution of frequencies of the IQ categories for the sample will be different from the population distribution of frequencies of the IQ categories
10. If the p-value of the obtained test statistic is less than .05, reject the null hypothesis
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Calculations
O E O-E (O-E)^2
/E
119 23 96 9216 401
150 136 14 196 1
687 341 346 119716 351
32 341 309 95481 280
12 136 124 15376 113
0 23 23 529 23
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11. X2 (5) = 1169, p < .00112. Reject H0
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SPSS: Chi-Square Goodness of Fit Test Weight Cases
Data, Weight Cases Check Weight Cases by Move weighted variable over to Frequency
Variable
Analysis Analyze, Nonparametric Statistics, Chi-
Square Move DV to Test Variable List Enter Expected Values
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Results Section The results of the Chi-Square Test
for Goodness of Fit involving the distribution of IQ categories for the state training school were statistically significant, X2 (6) = 682.646, p < . 001.
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Discussion Section It appears as if the distribution of
frequencies of the IQ categories for students enrolled in the state training school is different from the population distribution of frequencies of the IQ categories.
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Chi-Square Test for Independence Used to assess the relationship
between two or more variables
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Null Hypothesis No relationship between the two
variables Or Alternative: the two variables are
related to one another
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Degrees of Freedom df = (R - 1)(C - 1), Where R is the number of rows and
C is the number of columns in a bivariate table
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Example Dr. Cyrus, a forensic psychologist,
is interested in determining whether gender has an effect on the type of sentence that convicted burglars receive
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Dr. Cyrus’ Results
Probation Jail Prison
Females 37 42 14
Males 23 16 58
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1. Number of Samples: 22. Nature of Samples: Independent3. N/A4. IV: Gender5. DV: Type of sentence received
1. Nominal
6. Target Population: all convicted burglars
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7. Inferential Test: Chi-Square Test for Independence
8. H0: There is no relationship between gender and type of sentence received
9. H1: There is a relationship between gender and type of sentence received
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Create a bivariate table
probation jail total
male 14 80 94
female 46 20 66
60 100 160
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Calculate expected values For each cell, row total times
column total, divided by the total number of subject
i.e., for the first cell, (94 x 60)/160 = 35
(66x60)/160 = 25, (94x100)/160 = 59, (66x100)/160 = 41
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O E (O-E) (O-E)^2
/E
14 35 21 441 12.6
80 59 21 441 7.5
46 25 21 441 17.6
20 41 21 441 10.6
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10. If the p-value of the obtained test statistic is less than .05, reject the null hypothesis
11. X2 (2) = 48.3, p < .00112. Reject H0
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Probation Jail Total
Male 14 (35) 80 (59) 94
Female 46 (25) 20 (41) 66
60 100 160
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SPSS: Chi-Square Test of Independence Analyze
Descriptive Statistics Crosstabs
Move DV into Columns Move IV into Rows
Statistics Chi-Square
Cells Percentage
Rows Columns
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Results Section The results of the Chi-Square Test
for Independence involving gender as the independent variable and type of sentence received as the dependent variable were statistically significant, X2 (2) = 41.745, p < .001.
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Discussion Section It appears as if gender has an
effect on the type of sentence received.
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Assumptions Independence of Observations