The Research Process

First,

• Collect data and make sure that everything is coded properly, things are not missing. Do this for whatever program your using (SPSS, EXCEL, etc.)

• If your using SPSS, its easy to check the data because you will run frequencies.

CHLDIDEL Ideal Number of Children

9 .6 .9 .9

36 2.4 3.7 4.7

553 36.9 57.3 62.0

205 13.7 21.2 83.2

87 5.8 9.0 92.2

11 .7 1.1 93.4

3 .2 .3 93.7

3 .2 .3 94.0

58 3.9 6.0 100.0

965 64.3 100.0

485 32.3

50 3.3

535 35.7

1500 100.0

0

1

2

3

4

5

6

7 Seven+

8 Aa Many as Want

Total

Valid

-1 NAP

9 DK,NA

Total

Missing

Total

Frequency Percent Valid PercentCumulative

Percent

If anything looks odd, you can fix it, was everyone asked the question?

SEX Respondent's Sex

641 42.7 42.7 42.7

859 57.3 57.3 100.0

1500 100.0 100.0

1 Male

2 Female

Total

ValidFrequency Percent Valid Percent

CumulativePercent

INCOME4 Total Family Income

585 39.0 39.0 39.0

300 20.0 20.0 59.0

230 15.3 15.3 74.3

385 25.7 25.7 100.0

1500 100.0 100.0

1.00 24,999 or less

2.00 25,000 to 39,999

3.00 40,000 to 59,999

4.00 60,000 or more

Total

ValidFrequency Percent Valid Percent

CumulativePercent

Next, run crosstabs and look for patterns..

SEX Respondent's Sex * INCOME4 Total Family Income Crosstabulation

224 143 118 156 641

38.3% 47.7% 51.3% 40.5% 42.7%

361 157 112 229 859

61.7% 52.3% 48.7% 59.5% 57.3%

585 300 230 385 1500

100.0% 100.0% 100.0% 100.0% 100.0%

Count

% within INCOME4 Total Family Income

Count

% within INCOME4 Total Family Income

Count

% within INCOME4 Total Family Income

1 Male

2 Female

SEX Respondent'sSex

Total

24,999 orless

25,000 to39,999

40,000 to59,999

60,000 ormore

INCOME4 Total Family Income

Total

Is the difference you see in sex and income significant or due to an error in the way you collected the sample?

• Determine the appropriate statistical tests to tell you this (refer to charts and examples in your text)

• Here we have sex which is nominal, but our dependent variable is on a scale. Why can’t you use correlation here?

Because…

When one thing goes up,

the other must go up (or down)

Sex can’t go up (at least not in this example)

In this instance, we use an independent samples t test to compare the means of both

group or ANOVA

Independent Samples Test

6.122 .013 1.224 1498 .221 .0781 .06380 -.04707 .20320

1.233 1414.912 .218 .0781 .06332 -.04614 .20227

Equal variancesassumed

Equal variancesnot assumed

INCOME4 TotalFamily Income

F Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Our results are not significant.

• We can’t show that any differences we see in income level are due to sex..but wait…could it be that we need to look at single males vs. single females?? Strange recode? Hmmm..the adventure continues..

Create the report