Data Processing-Social Sciencel Research BDG

8/22/2019 Data Processing-Social Sciencel Research BDG

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DATA PROCESSING,

ANALYSISANDINTERPRETATION

(SOCIAL SCIENCERESEARCH)

Pablo E. Subong, Jr., Ed.D., Ph.D.West Visayas State University


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OBJECTIVES

To develop skills in data processing manually and

with the use of SPSS

Be able to process hypothetical data

Be able to properly analyze the data


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INTRODUCTION

SPSS for windows is a computer package that will

perform a wide variety of statistical procedures.

Data management and analysis can be handled

well with SPSS.

Using SPSS we can manipulate data, make graphs

and perform statistical techniques varying from

means to regression.


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WHATIS SPSS?

SPSS stands for Statistical Package for the

Social Sciences

The SPSS home-page is: www.spss.com


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WHATCANYOUDOWITH SPSS?

Run Frequencies Calculate Descriptive

Statistics

Compare Means

Conduct Cross-Tabulations Recode Data

Create Graphs and Charts

Do T-Tests

Conduct ANOVAs

Run Various Type of

Regressions

And Much More!


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WHAT I WILLSHOWYOUTODAY!!

Bringing your data into SPSS

Recoding

SPSS uses

Survey

Experimental study

Social science research


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SPSS WINDOWSPROCESS

Data window

Variable view window

Output window

Chart editor window


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MANAGEMENTOFDATAANDFILES

SPSS can read different types of data files.

You can open not only SPSS files but also excel

and other files.

You can create a new data set with SPSS. You can also edit, delete and view the contents of

your data file.


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HOWTOUSEDIFFERENTFILETYPES?

Excel file

csv file

SPSS file


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TYPESOFVARIABLES

You can select type of variable

String

Numeric

You can also select format of variable

Categorical

Ordinal

Interval


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CATEGORICAL (NOMINAL)

A categorical variable is one that has two or more

categories, but there is no intrinsic ordering to the

categories.

Gender

Hair color is also a categorical variable


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ORDINAL VARIABLE

An ordinal variable (nominal) is similar to acategorical variable.

The difference between the two is that there

is a clear ordering of the variables. SES (Socio Economic Status)

Education

Even though we can order these from

lowest to highest, the spacing between thevalues may not be the same across thelevels of the variables.


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INTERVALVARIABLE

An interval variable is similar to an ordinal variable,

except that the intervals between the values of the

interval variable are equally spaced.

Annual Income measured in Euros


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WHYDOESITMATTER? Statistical computations and analyses assume that the

variables have specific levels of measurement

Can you compute average of hair color?

Does it makes sense to compute the average of

educational experience?

An average requires a variable to be interval.


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DATA ANALYSIS

Data analysis embraces both the problem of finding

an appropriate model, on the one hand, and model

estimation and testing, on the other.

In this context normality assumption becomes

important.

In social sciences, it is hard to find typical bell

shaped normal distribution.


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NORMALDISTRIBUTION

In general, the bell shape distribution has thefollowing characteristics

The average is located in the center of the distribution.

The greater the distance from average, the lower the

frequency.


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Infants sex =sex

Male=1Female=2

Family income ($)=fincome

5,000-29,999=0

30,000-59,999=1

60,000-99,999=2Maternal age (years)=m_age

Maternal Smoking status=m_smk

Yes=1

No=0

Birth weight (granms) =bwgt

Maternal weight before pregnancy (pounds)=m_wgt

Fathers weight before the pregnancy=f_wgt

Sample Coding Book


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ID sex fincome m_age m_smk bwgt m_wgt f_wgt

1 2 2 29 1 3770 122 167

2 1 2 25 1 3742 125 200

3 2 1 28 0 3175 160 210

4 2 0 28 1 2919 110 165

5 2 0 19 1 3288 105 1606 2 0 35 0 3175 120 160

7 1 0 27 1 3883 125 180

...

...

99 2 2 24 0 4337 123 173

100 1 1 23 0 4110 115 140

Sample Birth Weight Data


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-DATA PROCESSING

-SPSS DEMO


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3May1999

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USING SPSSFOR WINDOWS

Introduction Data procedures

Statistical procedures

Syntax files

Editing output


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INTRODUCTION


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STEPSFOR ANALYZING DATA

Enter the data

Select the procedure and options

Select the variables Run the procedure

Examine the output


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COMMON OPERATIONS -

MENU OPTIONS

In the menu,

click StatisticsChoose

Summarize

ClickFrequencies


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COMMON OPERATIONS -

VARIABLES DIALOG BOX

This type ofdialog box is

used for manyprocedures.

Variables are selectedfrom the list on the left.

Click the arrowto move them to theappropriate box on the right.


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USING SPSSFOR WINDOWS -

DATA PROCEDURES

Ways to Enter Data

Entering Data Directly

Defining variables Entering data

Viewing Data

Recoding Variables

Computing New Variables

Selecting Cases


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WAYSTO ENTERTHE DATA SPSS datafile

Import data

Database file

Spreadsheet file

ASCII text file

Enter data directly with Data Editor


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ENTERING DATA DIRECTLY-DEFINETHE

VARIABLES


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ENTERING DATA DIRECTLY-

DEFINETHE VARIABLE

Name

Type and size

Labels

Missing values


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DEFINETHE VARIABLE - NAMEName the variable

No more than 8

characters

Each name unique

Must begin with a

letter

Certain characters

not allowed

Not case sensitive


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DEFINETHE VARIABLE - TYPEDefine the variable

type.

Define the variablewidth.

Define the number

of decimal places.


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DEFINETHE VARIABLE - LABELSLabels will be displayed

in the output.

Variable Label

can be more descriptive

than variable name


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DEFINETHE VARIABLE -

MISSING VALUES

Missing values are

used to define user-specified missing

information.

No response

Refused to answer

Data entry mistakes


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DEFINETHE VARIABLE -

COLUMN FORMAT

Column Format is usedto define column width

and alignment in the

Data Editor window


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ENTERING DATA DIRECTLY

Each row is a case

(e.g., survey form).

Enter the valuefor each variable.

Press key

or right arrow keyto move to next variable.


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ENTERING DATA DIRECTLY Leave blank or

use user-defined

missing valueif no answer.

Press key to move to

next case.


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CHANGETHE VIEW - VALUE LABELSData entered as

numeric codes

can be displayed

as value labels. In the menu,

click View

ClickValue Labels


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RECODE PROCEDURE

Recode is used to

to change the values

of an existingvariable

to create a new

variable based onthe values an

existing variable


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RECODEINTO NEW VARIABLE

In the menu, click

Transform.

Select Recode.

Click

Into Different Variable(s)


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Select and move

variable(s) over.

Name and labelnew variable.

Click

Old and New Values


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RECODEINTO NEW VARIABLEFor each value of

the existing variable

Repeat for each

value or range ofvalues

Click Continue

Enter the newvalue


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Click Change

Click OK


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DEFINE

LABELS

FOR

NEW

VARIABLE

In the Data menu,

click Define

Variable.Click Labels.

Enter value labels for

the new variable.


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COMPUTE

PROCEDURE

Compute is used to

create a new

variable.

In the menu, click

Transform.

Click Compute.


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COMPUTE

PROCEDURE

Name the new

variable.

Click Type&Labelto define the

characteristics of

the new variable.


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COMPUTE

PROCEDURE

Label the new

variable.Enter the variable

type.


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COMPUTE

PROCEDURE

Enter the numeric

expression that

will determine thevalues of the new

variable.

Click OK.


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SELECT CASES

For a subset of the

datafile, use Select

Cases.In the menu, click Data.

Click

Select Cases...


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SELECT CASES -

ALCOHOL

DRINKERS

ONLY

To select onlythose caseswhich meetcertain criteria,

choose the Ifoption.


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SELECT CASES -

ALCOHOL

DRINKERS

ONLY

Enter the

expression that

will determinewhich variables

will be selected.

Click Continue.


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SELECT CASES -

ALCOHOLDRINKERSONLY

When youvefinishedspecifyingselection

criteria, clickOK.


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STATISTICAL PROCEDURES

Summarizing Data Frequencies

Crosstabs (Chi Square)

Comparing Means T-Tests

One-Way Analysis of Variance

Nonparametric Tests Wilcoxon Signed Ranks

Mann-Whitney U Kruskal-Wallis


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FREQUENCIES

In the menu,

click StatisticsChoose

Summarize

Click

Frequencies


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FREQUENCIES

Select andmove the

variables. ClickStatistics.


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FREQUENCIES

Choose theappropriate

statistics.

Click

Continue.


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FREQUENCIES - CHARTS

For

histograms or

other charts,

click Charts.


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FREQUENCIES

Choose the

type of chart

and click

Continue


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FREQUENCIES

To select theformat of the

table(s), click

Format.


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FREQUENCIES

Choose theformat and click

Continue


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FREQUENCIES

Click OK torun the

Frequencies

procedure.


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FREQUENCIES - FORMATOPTION

ORGANIZE OUTPUTBY VARIABLES


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FREQUENCIES - FORMAT OPTION

COMPARE VARIABLES


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FREQUENCIES - DISTRIBUTION TABLEi


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FREQUENCIES - HISTOGRAM

Apgar 1 m inute score

10.08.06.04.02.00.0

Apgar 1 minute score300

200

100

0

Std. Dev = 1.83

Mean = 7.8

N = 424.00


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CROSSTABS

In the menu, click

on Statistics

ChooseSummarize

Click Crosstabs


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CROSSTABS

Move the outcome

variable(s) to the

Row(s) box.

Move the predictorvariable(s) to the

Column(s) box.

Click Statistics.


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CROSSTABS

Select the

appropriatestatistics.

Click Continue.


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CROSSTABS

To select the counts,

percentages, and

residuals to bedisplayed in each

cell, click Cells.


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CROSSTABS

Select the

information to be

displayed in each

cell.

Click Continue.


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CROSSTABS

To run the Crosstabs

procedure, click OK.


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CROSSTABS - OUTPUT


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CROSSTABS - OUTPUTe


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INDEPENDENT SAMPLES T-TEST

In the menu, click

Statistics.

Choose

Compare Means.

Click

Independent Samples T-Test.


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Select and move

Test Variable(s)

GroupingVariable

Click

Define Groups.


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Enter the values

for the groups.

Click Continue.


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Click OK to runthe procedure.


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INDEPENDENT SAMPLES T-TEST - OUTPUT


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ONE-WAY ANALYSISOF VARIANCE

In the menu, click on

Statistics.

ChooseCompare Means.

Click

One-Way Analysis of Variance.


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Move the dependent

variable(s) to the

Dependent List box.

Move the grouping

variable(s) to the

Factor box.

For comparisontests, click Post Hoc.


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Select the

appropriate

Post Hoccomparisons

.

Click

Continue.


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Click Options for

Descriptive statistics

Homogeneity ofvariance

Mean plots

Missing valuesoptions


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Make appropriate

selections, then click

Continue.


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To run the

One-way ANOVAprocedure, click OK.


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ONE-WAY ANALYSISOF VARIANCE -

OUTPUT

i


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OUTPUT


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OUTPUT

o

T

T*


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WILCOXON SIGNED RANKS TEST

In the menu, click

Statistics

ChooseNonparametric Tests

Click

2 Related Samples


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Move selected

variable pairs to

the Test Pair(s)

List box.

Choose the

statistical test(s).

ClickOptions...


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Check Descriptives

box for descriptive

statistics.


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Click OK to run

the procedure.


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MANN-WHITNEYUTEST

In the menu, click

Statistics

Choose

Nonparametric Tests

Click

2 Independent Samples


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MANN-WHITNEYUTEST

Select and move

Test Variable(s)

GroupingVariableClick

Define Groups.


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MANN-WHITNEYUTEST

Enter the values for

the groups.Click Continue.


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MANN-WHITNEYUTEST

Click Options.

After changing

options, click

Continue.

Click OK to run

the procedure.


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MANN-WHITNEYUTEST - OUTPUT


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KRUSKAL-WALLIS TEST

In the menu, click

Statistics

Choose

Nonparametric Tests

Click

K Independent Samples


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KRUSKAL-WALLIS TEST

Move the

dependent

variable(s) to the

Test Variable Listbox.

Move the grouping

variable(s) to the

Grouping Variable

box.

Click Define Range.


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KRUSKAL-WALLIS TEST

Enter the minimum

and maximum

values for theGrouping Variable.

Click Continue.


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KRUSKAL-WALLIS TEST

Check the box for

the Kruskal-WallisH.

Click OK to run the

procedure.


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KRUSKAL-WALLIS TEST - OUTPUT



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EDITINGTHE OUTPUT

Pivot Tables

Scatterplots Charts


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SCATTERPLOT

In the menu, click on

Graphs.

Choose Scatter.


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SCATTERPLOT

Choose the

appropriate type ofplot.

Click Define.


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SCATTERPLOT

Select and move

the variables for

the X and Y axesto the appropriate

box.

Click OK to run the

procedure.


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BMI

70605040302010

5000

4000

3000

2000

1000

0

SCATTERPLOT - OUTPUT

Regression linemust be added.


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EDITTHE SCATTERPLOT

In the Output Window

Click the chart object

to select it.

In the menu, click

Edit.

Choose SPSS Chart

Object.Click Open.


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SCATTERPLOT

The Chart

Window

will open.


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EDITTHE SCATTERPLOT

In the Chart Window

In the menu, clickChart.

Click Options.


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EDITTHE SCATTERPLOT

Check the Totalbox.

Click OK.


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SCATTERPLOT - OUTPUT

Regression lineis added.

BMI

70605040302010

5000

4000

3000

2000

1000

0


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EXERCISE DATASETS

Coding and recoding Survey about smoking habit

Test of Difference


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STATISTICAL DATA ANALYSISAND

INTERPRETATIONPrepared By:

PABLO E.SUBONG, JR., Ed.D., Ph.D.

Category Mean Description S.D.

TABLE 1: NMAT PERFORMANCEOFTHE BS BIOLOGY STUDENTS


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A. Entire Group 1.96 Average 0.60

B. Gender

Male 1.85 Average 0.66

Female 2.09 Average 0.51

C. SES

High 1.75 Average 0.72

Average 1.83 Average 0.48

Low 2.29 Average 0.45

D. Type of School

Private 1.85 Average 0.61

Public 2.07 Average 0.59

E. Mental Ability

High 1.67 Average 0.67

Average 1.90 Average 0.47

Low 1.96 Average 0.60

Scale Description

1.00-1.66 High

1.67-2.32 Average

2.33-3.00 Low

NMAT PERFORMANCEOFTHE BS BIOLOGY


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The NMAT Performance of the BS Biology studentsis presented in Table 1. Generally, the NMAT

performance of the BS Biology students is average,

(M=1.96, s.d.=0.60)

When they are classified into their gender,

socioeconomic status, type of school, and mental

ability, the BS Biology students exhibited the same

level of NMAT performance which is average.

STUDENTS

TABLE 2: T-TEST RESULTSFORTHE DIFFERENCESINTHE

NMAT PERFORMANCE OF THE BS BIOLOGY STUDENTS


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Compared Groups d.f. Mean s.d. t-ratio t-Prob.

A. Gender

Male 34 82.80 16.74 1.782 .084

Female 71.63 20.92

B. Type of SchoolPrivate 34 76.22 22.52 0.496 0.623

Public 79.44 15.87

p > 0.05 Not significant at 0.05 alpha

NMAT PERFORMANCEOFTHE BS BIOLOGY STUDENTS

DIFFERENCESINTHE NMAT PERFORMANCEOF


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The differences in the NMAT performance of the BS Biologystudents are shown in Table 2. The t-test computations revealno significant differences in the NMAT performance of the BSBiology students when they are classified into gender,t(34)=1.782, p=0.084. The null hypothesis of no significantdifference in the NMAT performance of the BS Biologystudents that would exist between gender was accepted.

This simply shows that both male and female BS Biologystudents have the same performance in their NMAT. Likewise,when they are classified into type of school, students comingfrom private and public schools exhibited the sameperformance in their NMAT, t(34)=0.496, p=0.623.

This similar performance might be attributed to the fact thatpublic school nowadays can now compete with the privateschools in terms of scholastic performance of the students.

THE BS BIOLOGY STUDENTS

TABLE 3 A: ANOVA RESULTS FOR THE DIFFERENCES


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Sources of

Variation

Degrees of

Freedom

Sum of

Squares

Mean

Squares

F-ratio F-Prob.

Between Groups 2 1143.17 571.58 1.591 0.219

Within Groups 33 1855.83 359.27

Total 35 12999.00

p > 0.05 Not significant at 0.05 alpha

TABLE 3-A: ANOVA RESULTSFORTHE DIFFERENCES

INTHE NMAT PERFORMANCEOFTHE BS BIOLOGY

STUDENTS CLASSIFIEDASTOSOCIOECONOMIC STATUS

TABLE 3 B: ANOVA RESULTS FOR THE DIFFERENCES


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Sources of

Variation

Degrees

ofFreedom

Sum of

Squares

Mean

Squares

F-ratio F-

Prob.

Between Groups 2 5346.50 2673.25 11.528 0.000

Within Groups 33 7652.50 231.89

Total 35 12999.00p < 0.05 Significant at 0.05 alpha

TABLE 3-B: ANOVA RESULTSFORTHE DIFFERENCES

INTHE NMAT PERFORMANCEOFTHE

BS BIOLOGY STUDENTS CLASSIFIEDASTOTHEIR MENTAL ABILITY

TABLE 3 C: POST HOC TEST FOR THE DIFFERENCES


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NMAT Performance Mental Ability Mean

Difference

Significant

High Average 12.75 0.138

Average Low 29.75 0.000

Low 17.00 0.034

p < 0.05 Significant at 0.05 alpha

TABLE 3-C: POST HOC TESTFORTHE DIFFERENCES

IN MEANSINTHE NMAT PERFORMANCEOF BS

BIOLOGY STUDENTS CLASSIFIEDASTO MENTALABILITY

ANOVA results revealed no significant differences in the


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g

NMAT performance of the BS Biology students when they

classified as to their socioeconomic status,

F(2,33)=1.591, p=0.219. Meaning, those BS Biologystudents with high, average, and low socioeconomic

status, their performance level in their NMAT is similar.

But when the BS Biology students are classified into their

mental ability, ANOVA results revealed a significantdifference in their NMAT performance, F(2,33)=11.528,

p=0.000. The results are reflected in Table 3-B.

Pair-wise comparison using Scheffe Test in Table 3-C


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Pair-wise comparison using Scheffe Test in Table 3-C

showed that those BS Biology students with high and

average mental ability do not differ significantly in theirNMAT performance, but those students with high mental

ability, differ in their NMAT performance with those

students with low mental ability. Likewise, those students

with average mental ability differ in their NMAT

performance with those students with low mental ability.


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THANKYOU

Data Processing-Social Sciencel Research BDG

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