Elaboration Model There is a gender difference in attitudes toward spending too much money on halting crime rate.

There is a gender difference in attitudes toward spending too much money on law enforcement.

There is a direct relationship between amount spend on halting crime rate and amount spent on law enforcement.

There is a gender differencet in attitudes toward spending too much money on halting crime rate that is directly moderated by amount spent on law enforcement.

Control Variable Question

100) We are faced with many problems in this country, none of which can be solved easily or inexpensively. I'm going to name some of these problems, and for each one I'd like you to tell me whether you think we're spending too much money on it, too little money, or about the right amount. E. Law enforcement (NATCRIMY).

QUESTION 3 -- Please refer respective chart and table

Table 3-1: Descriptive Table

Statistics

NATCRIMY

N

Valid

Missing

Std. Error of Mean

.01648

Std. Deviation

1.15403

Range

9.00

Minimum

.00

Maximum

9.00

Table 3-2: Frequency Table For NATCRIMY

NATCRIMY

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

.00

49.4

49.4

49.4

1.00

25.1

25.1

74.5

2.00

19.4

19.4

94.0

3.00

5.1

5.1

99.1

8.00

43

.9

.9

9.00

2

.0

.0

Total

b.

A histogram (interval or ratio interval) that graphically shows the distributions

Chart 3.-1: Histogram Graph For NATCRIMY

QUESTION 4 -- RECODE NATCRIMY

In order to have a meaningful representation those "Inapplicable, Don't Know and No Answer" had been recode to 4 and 5. Because of this, now the observation dispersion is scattered among 5 conditions from 6 conditions.

Table 4-1: Descriptive Statistics

Recode NATCRIMY

N

Valid

Missing

0

Mean

3.30

Median

4.00

Mode

5

Std. Deviation

1.759

Range

4

Minimum

1

Maximum

5

Table 4-2: Frequency Recode NATCRIMY

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

1

25.1

25.1

25.1

2

19.4

19.4

44.6

3

5.1

5.1

49.7

4

45

.9

.9

50.6

5

49.4

49.4

Total

Chart 4 -- 1: Histogram Chart For Recode NATCRIMY

QUESTION 5

Table 5-1: DESCRIPTIVESTATISTICS

NATCRIMY

Recode NATCRIMY

N

Valid

Missing

0

0

Mean

.8674

3.30

Median

1.0000

4.00

Mode

.00

5

Std. Deviation

1.15403

1.759

Range

9.00

4

Minimum

.00

1

Maximum

9.00

5

TABLE 5- 2: FREQUENCY TABLE FOR NATCRIMY

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

.00

49.4

49.4

49.4

1.00

25.1

25.1

74.5

2.00

19.4

19.4

94.0

3.00

5.1

5.1

99.1

8.00

43

.9

.9

9.00

2

.0

.0

Total

TABLE 5-3: FREQUENCY TABLE FOR RECODE NATCRIMY

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

1

25.1

25.1

25.1

2

19.4

19.4

44.6

3

5.1

5.1

49.7

4

45

.9

.9

50.6

5

49.4

49.4

Total

a.

In term of levels of measurement, mode, standard deviation and range had shown a significant difference with Mode, measurement shows a significant change from 0.0 to 5. Since most of the "Inapplicable, Don't Know and No Answer" had been recoded to 4 and 5, the Range for variables before and after recode is represented by a difference of 5 (which explains why Mode is 5).

Chart 5-1: Histogram Table For NATCRIMY

CHART 5-2: HISTOGRAM TABLE FOR RECODE NATCRIMY

b.

In terms of frequency of distribution, since 50% of the results were recoded, frequency for each response also drops by 50%. This is evidence in term of cumulative percentage. For example Categories 1 for NATCRIMY before recode cumulative percentage is 74.5 but after recode 25.1.

c.

mode, median and mean had shown a significant difference after recode. Mode measurement showed a significance change from 0 to 5 as mentioned earlier. Mean for before recode is shown as 0.8 but after recode mean is 3.0. This indicates that categories 4 and 5 which had been recorded contributes toward 3 times the whole mean of population.

d.

Standard deviation recode is recorded at 1.2 but after recode close to 1. This indicates the dispersion of the finding had been spread further as a result of regrouping certain observation under new observation categories.

QUESTION 6

a.

Cross-tabulation between Independent Variable vs. Dependent Variable.

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

Recode NATCRIME * SEX

0

0.0%

Recode NATCRIME * SEX Cross tabulation

Count

SEX

Total

1.00

2.00

Recode NATCRIME

1

2

3

94

59

4

21

45

66

5

Total

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

61.392a

4

.000

Likelihood Ratio

61.090

4

.000

Linear-by-Linear Association

10.196

1

.001

N of Valid Cases

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 28.70.

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Phi

.112

.000

Cramer's V

.112

.000

N of Valid Cases

b.

Cross-tabulation between Independent Variable vs. Control Variable.

Case Processing Summary

Cases

Sig. (2-sided)
Pearson Chi-Square

59.156a

4

.000

Likelihood Ratio

60.170

4

.000

Linear-by-Linear Association

30.173

1

.000

N of Valid Cases

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 19.57.

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Phi

.110

.000

Cramer's V

.110

.000

N of Valid Cases

c.

Cross-tabulation between Control Variable vs. Dependent Variable.

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

Recode NATCRIME * Recode NATCRIMY

0

0.0%

Recode NATCRIME * Recode NATCRIMY Cross tabulation

Count

Recode NATCRIMY

Total

1

2

3

4

5

Recode NATCRIME

1

0

0

0

0

2

0

0

0

0

3

0

0

0

0

4

0

0

0

0

66

66

5

45

0

Total

45

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

16

.000

Likelihood Ratio

16

.000

Linear-by-Linear Association

1

.000

N of Valid Cases

a. 3 cells (12.0%) have expected count less than 5. The minimum expected count is .61.

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Phi

1.000

.000

Cramer's V

.500

.000

N of Valid Cases

d.

Cross-tabulation between Independent Variable vs. Dependent Variable vs. Control Variable.

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

Recode NATCRIME * SEX * Recode NATCRIMY

0

0.0%

Recode NATCRIME * SEX * Recode NATCRIMY Cross tabulation

Count

Recode NATCRIMY

SEX

Total

1.00

2.00

1

Recode NATCRIME

5

Total

2

Recode NATCRIME

5

Total

3

Recode NATCRIME

5

Total

4

Recode NATCRIME

5

11

34

45

Total

11

34

45

5

Recode NATCRIME

1

2

3

94

59

4

21

45

66

Total

Total

Recode NATCRIME

1

2

3

94

59

4

21

45

66

5

Total

Chi-Square Tests

Recode NATCRIMY

Value

df

Asymp. Sig. (2-sided)

1

Pearson Chi-Square

.b

N of Valid Cases

2

Pearson Chi-Square

.b

N of Valid Cases

3

Pearson Chi-Square

.b

N of Valid Cases

4

Pearson Chi-Square

.b

N of Valid Cases

45

5

Pearson Chi-Square

41.515c

3

.000

Likelihood Ratio

41.728

3

.000

Linear-by-Linear Association

12.636

1

.000

N of Valid Cases

Total

Pearson Chi-Square

61.392a

4

.000

Likelihood Ratio

61.090

4

.000

Linear-by-Linear Association

10.196

1

.001

N of Valid Cases

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 28.70.

b. No statistics are computed because Recode NATCRIME is a constant.

c. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 30.78.

Symmetric Measures

Recode NATCRIMY

Value

Approx. Sig.

1

Nominal by Nominal

Phi

.c

N of Valid Cases

2

Nominal by Nominal

Phi

.c

N of Valid Cases

3

Nominal by Nominal

Phi

.c

N of Valid Cases

4

Nominal by Nominal

Phi

.c

N of Valid Cases

45

5

Nominal by Nominal

Phi

.131

.000

Cramer's V

.131

.000

N of Valid Cases

Total

Nominal by Nominal

Phi

.112

.000

Cramer's V

.112

.000

N of Valid Cases

c. No statistics are computed because Recode NATCRIME is a constant.

QUESTION 7

a.

The amount difference between IV vs. DV is considered insignificant. Chi-square vs. likelihood ratio only differ by 0.302 (61.392-61.090) which is less than 5.

b.

The value column = 0.112, this indicates a slight relation between sex (gender) and amount spent in fighting crime. The significance level = 0.0001, which is highly significant (p< 0.05) that also means the relationship is generalizable to the populations. We could conclude, there is a gender difference in opinion toward increasing amount of money spent in halting crime rate. Women (56.5%) were more likely to agree with the statement than men (43.5%)

QUESTION 8

a.

The amount difference between IV vs. CV is considered insignificant. Chi-square vs. likelihood ratio only differs by 1.0140 (60.170-59.156) which is still less than 5.

b.

The value column = 0.110, this indicates a slight relation between sex (gender) and amount spent on law enforcement. The significance level = 0.0001, which is highly significant (p< 0.05) that also means the relationship can be generalised to the populations. We could conclude, there is a gender difference in opinion toward increasing amount spends on law enforcement toward reducing crime rate. Women (56.5%) were more likely to agree with this statement than men (43.5%)

QUESTION 9

a.

The amount difference between CV vs. DV is considered significant. Chi-square vs. likelihood ratio only differ by 1,892.518 (6,793.518-4901.00) which is more than 5.

b.

The value phi = 1.00 and Cramer's V = 0.0500, this indicates there is a mutual relation between amount spent in fighting crime and amount spend on law enforcement. The significance level = 0.0001, which is high significance (p< 0.05) that also means the relationship can be generalised to the populations. We could conclude, there is a mutual relationship between…