This document provides guidance on conducting valid data analysis in SPSS. It discusses: 1) The importance of solid study planning and design; 2) Ensuring a valid data set through proper data collection, entry, cleaning and manipulation; 3) Selecting appropriate statistical procedures that match the data types and meet statistical assumptions; 4) Correctly interpreting and summarizing results in the context of the research question.

1. SPSS Hands-on Training:
Descriptive analysis
by SPSS soft-ware
Dr Tin Myo Han
M.B;B.S, M.Med.Sc(P.H), M.P.H ( Belgium), Diploma in Family Medicine(Malaysia)
24th May2013 KOD, IIUM, Kuantan

2. SPSS soft-ware Hands-on training 2:
Step by Step for descriptive statistics
• Hands-on demonstration
- Frequency tables
- Transformation of Data
- Descriptive analysis
- splitting data file
• Hands-on exercise
• Overview of data Analysis
- Valid data analysis

3. Data Management
• Data coding & data cleaning by manual
method
• Data entering
• Data cleaning by soft ware
• Data Analysis - Descriptive analysis
&
- Inferential analysis

4. Data coding & data cleaning by
manual method
• Have You already collected Data!
• Have you already coded your data &
cleaned manually?
• Have You already set a SPSS data sheet to
enter your data?
• Have You had your own SPSS data set?
( 10- 20% of your sample size “n” is OK !)

5. Open SPSS soft- ware
Open existing file
Data Editor Window

6. Open your File
Open your file
Data Editor Window

7. Open data file

8. Data editor window: Variable view
Go to variable view
-displays a table consisting
of variable names and
their attributes.
-You can
1. modify the properties
of each variable or
2. add new variables or
3. delete existing variables
in the variable view
Window.
Variable view Window

9. Analysis of Data

10. Descriptive Statistics
• typical tools for exploring the
descriptive summaries
• useful for data exploration
• useful for data cleaning.
before any further analysis.
• P-P plots and Q-Q plots are
useful for checking the
distribution assumption
required by statistical
techniques.

11. Descriptive Analysis:
Frequency ( eg-age )
Step-1
Step-2
Step-3 Step-4 SPSS –out put

12. Descriptive Analysis:
Frequency ( eg-age group)
Step-1
Step-3 SPSS –out put
Age
group
Step-2
Step-4

13. Quantitative Data
Data
Categorical
Ordinal
e.g <good,
poor, fair>
Nominal
e.g. Color of
teeth
Numerical
Continuous
<Age of students >
Discrete
<Number of teeth at
80 yr old>

14. Data management by SPSS:
Transform Menu:
• Transform data from continuous numerical data
 categorical /ordinal data  nominal data
eg(1) Age  age groups ( < 5 yrs, 6-10 year, > 11 years ]
younger age group, elder age group
(2) DMFT value  high caries /low caries group
(3) CFU/bacterial count in contaminated water 
acceptable level / unacceptable level
! Cut-off level (drinking water/ domestic use/ industrial use)

15. Data management by SPSS:
Transform Menu:
• Recode value of variable into same variable or
into different/new variable
eg(1) value of gender 
( male = 1 , female =2 ) (male= M, female= F)
(2) response of smoking cessation
3 categories  2 categories
( no smoke= 1, still smoke = 2, trying to quit =3)
{( no smoke = 1], [ still smoke+ trying to quit = smoke= 2 ])

16. SPSS Pull down menu:
Transform: ( recode into different variables)
Step-1 Step-2 Step-3
Step-4
a new variable “age group” in
variable view

17. Descriptive analysis :
Distribution of DPH marks of 10 Dental Students
by SPSS-software
5/24/2013 17
Descriptive
Statistic Std. Error
DPH mark Mean 56.30 5.239
95% Confidence Interval for Mean Lower Bound 44.45
Upper Bound 68.15
5% Trimmed Mean 55.28
Median 50.00
Variance 274.456
Std. Deviation 16.567
Minimum 42
Maximum 89
Range 47
Interquartile Range 27
Skewness 1.096 .687
Kurtosis .051 1.334
Central
Tendency
Dispersion
Shape

18. Descriptive Analysis: Descriptive
Step-1
SPSS –out put
Step-2
Step-3

19. Data management by SPSS:
Data Menu:
• Split the file
• One master file into 2 or 3 sub-files
master file age of all participants
( smokers + non smokers)
• Age distribution of all participants
(min, max and mean ± SD )
-age distribution of smoker group?
-age distribution of non-smoker group?

20. SPSS Pull down menu:
Data ( split file)
Step-1 Step-2
SPSS –out put
SPSS –out put

21. SPSS Pull down menu: Analysis by Graph

22. Today ! Exercise with Your Data
• a SPSS file for respective research data entry
• Analysis:
• (1)Frequency table to check data &
(2) descriptive statistics of continuous variables
• Enter you data

23. Overview of Data Analysis

24. Milestones of
Dental students research project
(KOD, IIUM)
• 1st batch: 4th Malaysian Dental student conferences (2012)
3rd prize in oral presentation (Individual group)
• 2nd batch: 5th Malaysian Dental student conferences (2013)
1st prize in oral presentation(Individual group)
“Champion” in overall results( University)
( Out of 14 Universities, ties with IMU, USM)

25. Remarks!
• They did not try to win a prize for their
research projects!
• Their research projects deserved to be
awarded because of …
applicability of results of their
research projects!
effort to get the valid results !

26. Overview of Data Analysis

27. Validity of Questionnaires, SOP, Standardization,
“Supervision” “27
Validity of
Data!

28. Valid Data Analysis
1.A valid data analysis starts with a solid planning
of the study
2.A valid data analysis must have a valid set of
data ( supervision of your project supervisor)
3.Appropriate Statistical Procedures are the key
to a correct analysis.
4. Appropriate analysis needs correct
interpretation of the results.

29. I. A valid data analysis starts with a solid
planning of the study.
• For a survey study or an observational study --
the adequate measurement,
-the target population,
-sampling techniques, sample size,
-factors associated with the intended
characteristics,
- designing questionnaires, &
ways of distributing and collecting the survey.

30. I. A valid data analysis starts with a solid
planning of the study.
• For a controlled experimental study,
• by considering the measurement,
• the potential confounding factors associated
with the measurement,
• the intended factors for the experiment,
• the design of the experiment,
• experimental units, sample size, and possible
statistical techniques for analysis based on the
experimental design.

31. II. A valid data analysis must have a
valid set of data
• to design a proper format for data entry
• Many times data are entered in such a way that it is
not readable by statistical software.
• data values can only be either numeric or non-
numeric( String)
• Numeric values can be quantified; while non-
numeric values can only be summarized in most
cases.
• It is important that proper data values be created so
that statistical software ( SPSS) can perform the
analysis.

32. II.A valid data analysis must have a
valid set of data
• After data entry, it is the data cleaning &
manipulation stage.
• It can happen that some data points are
entered completely out of range.
• A quick way of locating these out-of-range
data values is by performing frequency
procedures or descriptive procedures, and
check the output results to see if any variable
has such a problem.
• Data transformation is often used before a
valid analysis can be performed.

33. III. Appropriate Statistical Procedures
are the key to a correct analysis.
• Almost every statistical procedure has
assumptions behind it.
• It is necessary to carefully consider the violation
of the assumptions for a statistical procedure.
• A minor violation usually does not create
serious problems.
• However, if there is a serious violation,
appropriate data transformation or selecting
different statistical procedures may be
necessary.

34. III. Appropriate Statistical Procedures
are the key to a correct analysis.
• It is often the case that appropriate statistical procedures
are associated with the types of data.
• Categorical data needs to be analyzed using procedures
that are developed for analyzing categorical data.
• We do not perform frequency analysis or cross-tabulation
procedures to analyze continuous data.
• It happens often in data analysis that one needs to
conduct several analyses before an appropriate one is
selected.
• One should expect that the analysis is never only a one
step process. It involves many back and forth analyses
and decisions for a proper analysis.

35. IV. Appropriate analysis needs correct
interpretation of the results.
• How to interpret and summarize the results
from a huge pile of output is certainly a
crucial step for a valid data analysis.
• It involves the understanding of the project,
the statistical techniques and how to bring
the numbers into the context of the project.
• One must make sure that the output is
properly interpreted and summarized to a
degree that non-statisticians can understand
them.

36. V. Data Types and Analysis
• Generally speaking, statistical
techniques are often determined based
on the type of data!!

37. General Considerations
- There is no best way to conduct a quantitative
study.
- Different projects involve different considerations
of the contexts behind the study.
- Without proper understanding of the contexts of
the study that are associated with the project, the
quantitative study will be purely empirical.
- The empirical results may not be able to answer
the root causes of the problem. ( Your research
question!)

38. Overview of Data Analysis
• Hence, it is crucial to thoroughly investigate the
context behind the project before a proper plan and
design of a quantitative study is conducted.
• The common aspects related to the contexts behind
the study other than the intended quantitative
measurements that need to be addressed may
include, but not limited to:
- External environmental conditions
- Background of the subjects
- Possible factors associated with the
intended measurement.
- Common sense and logic