This document provides an overview of how to use SPSS to conduct basic statistical analysis and present results. It outlines expectations for the workshop, including learning how to prepare an SPSS file, display and summarize data, and create graphical presentations. The document then covers key SPSS concepts like variables, data types, and examples. It also demonstrates how to perform descriptive statistics, frequency tables, crosstabs, measures of central tendency and dispersion. Finally, it discusses different methods of graphical presentation in SPSS like bar charts, histograms, box plots and more.

3. Expectations

4. ILOs
To describe how to use SPSS for conducting basicstatistical analysis
and interpret the output of the analysis.
 Preparing SPSS file for data entry
 Displaying data
 Methods of presenting and Summarizing data
 Graphical presentation of data
By the end of this workshop you will be able to:
‫االهداف‬

6. SPSS made easy

7. SPSS
•STANDS FOR STATISTICAL PACKAGE FOR SOCIAL
SCIENCE
‫اإلجتماعية‬ ‫العلوم‬ ‫في‬ ‫اإلحصائية‬ ‫الحزم‬

8. Variable , Data
• Variables ‫:متغيرات‬
These are observations, which vary from one person to
another or from one group of members to others as: age,
weight, blood pressure, sex.
• Data ‫البيانات‬: Value of the variable.
• Body weight= 70 KG

9. Example:
You are conducting a research to see if proper
infection control training for nurses decreases
prevalence of needle stick injury.
Dependent variable (DV): Decreases prevalence
of needle stick injury.
Independent variable (ID): proper infection
control training

11. Data
1.Quantitative (numerical):
 Continuous quantitative: e.g. age, weight, height
 Discrete quantitative: e.g. no of patients, number of
children per family.
2.Qualitative (non numerical)/categorigcal:
Nominal qualitative: e.g.
(blood grouping A, B, AB, O),(sex: male and female)
Ordinal qualitative: e.g. (mild, moderate, severe)

12. An icon next to each variable provides information about data type
Scale (Continuous)
Ordinal
Nominal

13. Session 1 cont.:
How to design questionnaire in SPSS
How to enter data in SPSS
•SPSS Windows
Variable view
Data view
• Coding and Design questionnaire in SPSS
• Data entry
Copy, paste from excel into SPSS
From SPSS, open existing excel file
Create new file directly from SPSS

14. SPSS opening window

15. 15
SPSS Variable View

16. 16
SPSS Data View

17. 17

18. Practical training 1: (10 min.)
•Please each one design the questionnaire with
you on SPSS and enter data

19. N.B How to Code questions with more than one answer
What are risks, adverse effects do you know of oral Isotretinoin therapy? (You can
choose more than one answer):
1. Teratogenicity
2. Dryness
3. Constipation
4. Lipid profile disturbance
5. Hepatic side effects
6. Depression
7. Anemia
8. Others………………….
9. I don’t know
Each item in answer enter as a separate
variable with yes, no answer

20. How to Code open ended questions
• Read through responses
• Create a preliminary code based on
responses
• Put responses into category and code it
• Try not to have more than 10 categories,
with no individual category receiving less
than 5% of responses.
• Also, there is software that can be used to
help you code open-ended responses.

21. Session 2:
How to perform data cleaning/manipulation?
Use data set 1
• Data files are not always organized to meet specific user needs, user
may need to select specific group, split data file into separate group
for analysis
• Copy, paste: age, duration, HbA1C
From Data menu
1. Select case: first 20 cases, male only, Saudi, age <45ys
2. Split file: by sex, nationality
3. Sort cases: duration, HbA1C (ascending, descending)
From SPSS dialog box, go to:
Data
Select cases, Sort cases,
Split file

22. Practical training 2
Data transformation
Use data set 1
From transform menu
1. Recode into different variable:
Age: from number to groups (1- <25y, 2- ≥25y)
Duration 1- <5, 2- ≥5
HBA1C 1- <6.5, 2- ≥6.5
1. Recode into same variable
2. Compute variable: Mean, %, BMI ‫ضرورى‬ ‫االقواس‬
From SPSS dialog box, go to:
Transform
Recode
Into Same variables
Into different variables

24. Methods of presentation
Numerical
Graphical
Qualitative
Bar
Pie
Quantitative
Histogram
Frequency polygon
Box plot
Tabular
• Frequency tables
• Cross tabulations
Mathematical
• Mean
• Median
• Mode
• Rang, IQR
• SD

25. Session 3:
Descriptive statistics, Data presentation
• After data entry, it can be analyzed using descriptive statistics
Purpose:
• To find wrong entries, have basic knowledge on the sample,
summarize data
•Tabular presentation
 Frequency analysis (simple frequency table)
 Crosstabs (2 X 2 table and C x R)

26. 1- Simple frequency table
From the menu choose:
Analyze > Descriptive Statistics > Frequencies...
e.g. (sex, nationality, compliance and comments)

27. Output

28. So you can do frequency to filter data, detect
missed one
Missed data
Missing Values for a Numeric Variable
• Type 999 in the Value field.
Missing Values for a String Variable
• Type NR (no response) in the Value field.
A value is missing may be important to your analysis. For example, you may
find it useful to distinguish between those respondents, and non respondents

29. Characteristics of good table:
1. Simple
2. Self explanatory
3- Explaining abbreviation
4- Columns and rows labeled clearly
5- Unites of measures should be written
6- Title: Every table should have a title, above the
table, which is clear and answers, as possible as you
can, four questions
(what, who, where and when).

30. 2- Crosstabs
• Crosstabs are used to examine the relationship between two
variables Analyze > Descriptive Statistics >crosstabs
• e.g. 2x2 (sex & nationality)

31. Statistics iconCells icon

32. Output

33. Odds Ratio (OR)
• The odds ratio is the odds of outcome occurrence in
one group divided by the odds of outcome
occurrence in the comparison group
• Analysis of case-control studies
• If the OR = 1 there is no difference between the two
groups
• If the OR >1 this exposure is risk factor for
occurrence of disease
• If the OR <1 this exposure is protective factor for
occurrence of disease
Relative risk (RR)
 RR indicates how many times those
exposed are likely to develop the disease
relative to non-exposed.
• Analysis of cohort studies
RR= 1: the exposure is not associated with the
disease.
RR > 1: the exposure is a risk
RR < 1: the exposure is a protective

34. Practical training 3:
•Using data set 1 exercise
•Descriptive statistics: Simple frequency table for
sex, compliance
•Crosstabs: 2 X 2 table: relation between gender and
nationality.
•C x R: Association of compliance to treatment and
gender

35. Statistical tests
A-Parametric tests
1-Normal
distribution data
B-Non parametric
tests
Not normally distributed
data

36. Methods of data presentation
Central Tendency
Mathematical
Dispersion
Mean
Median
Mode
Range
Interquartile range
Standard Deviation

37. Central Tendency

38. Mean, Median, Mode
Analyze > Descriptive Statistics >Descriptives

39. Output

40. Mean, Median, Mode
Analyze > Descriptive Statistics >Frequencies

41.  1- The Range
 2- Interquartile range:
Max – Min = Range
 3- Standard deviation (SD):
Measures of
Dispersion/scatter/spread

42. Quartiles
2nd quartile
median
3rd quartile
75th percentile
75%
50%
1st quartile
25th percentile
25%

43. For calculating dispersion measures:
Analyze > Descriptive Statistics >Frequencies
Analyze > Descriptive Statistics >explore
Exercise
Calculate
Mean, SD of:
Age

44. Practical Training 3
Perform descriptive analysis for the variables:
• Age and HbA1C (mean, median, SD, range, min, max ) and write the
comment on table

45. Percentiles
 A percentile or (centile) is the value below which a certain
percentage of observations fall.
 For example, the 10th percentile is the value below which 10
percent of the observations may be found.
 Often used to compare an individual value with a norm. e.g. physical
growth charts for children e.g. weight for age chart

46. SD, SEM
• SEM measures the variability of the
mean of the sample as an estimate
of the true value of the mean of the
population from which the sample
was drawn
‫يستخدم‬‫البعض‬‫الخطأ‬‫المعيارى‬‫للمتوسط‬‫كأحد‬
‫مقاييس‬‫التشتت‬‫وهو‬‫ما‬‫يعتبر‬‫من‬‫األخطاء‬
‫الشائعة‬‫حيث‬‫ال‬‫يعبر‬‫الخطأ‬‫المعيارى‬‫عن‬‫التباين‬
‫وال‬‫عن‬‫مدى‬‫االختالف‬‫الموجود‬‫داخل‬‫البيانات‬

47. Practice: Try calculate SD,SEM for age

48. Graphical presentation of data

50. How to present data by
graph ?
Graphical
Qualitative
Bar
Pie
Quantitative
Histogram
Frequency polygon
Box plot

51. Bar chart
• This type of graph is suitable to represent data of the two subtypes of
qualitative and quantitative discrete type
• Analyze> descriptive statistics> frequency> chart> bar chart
• Graphs > legacy dialogs>bar

52. Types of bar charts:
Simple bar chart
Multiple/grouped bar chart
Segmented/stacked bar chart

53. Histogram
Continuous quantitative data
Pie chart
For all the four types of variables
 Angle =
𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐨𝐟 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐲 𝐨𝐫 𝐢𝐧𝐭𝐞𝐫𝐯𝐚𝐥
𝐭𝐨𝐭𝐚𝐥 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲
× 360

54. Box plot ( often called box and whisker plot)
• This is a vertical or horizontal rectangle, with the
ends of the rectangle corresponding to the upper
and lower quartiles of the data values.
• A line drawn through the rectangle corresponds
to the median value.
• Whiskers, starting at the ends of the rectangle,
usually indicate minimum and maximum values.
54

55. Box plot
Graphs > legacy dialogs> box plot

56. Scatter plot
 The dependent variable on the
vertical axis (the y-axis)
 The independent variable on the
horizontal axis (the x-axis).

57. The value of (r) ranges between ( -1) and ( +1)

58. Pareto chart (Analyze> quality control> pareto)
• Is a vertical bar graph in which values
are plotted in decreasing order of
relative frequency from left to right.
• Is one of the seven basic tools of quality
control. Useful for analyzing what
problems need attention first.
• Pareto principle (80/20 rule), is a theory
maintaining that 80 percent of the
output from a given situation or system
is determined by 20 percent of the
input.
• Pareto chart guiding how to solve 80%
of the problem.

59. Quiz: which cause could solve 80% of problem
Pareto chart ranking perceived problems of food service providers at
dietary department

60. Area chart
• A way to quickly and easily
visualize how well the students in
your class were doing over the
course of the year.
• A way to show the average exam
scores throughout the course on
an area chart.
• Show a trend over time

61. Practical 3:
• Using data set 2 exercise to make:
• Pie graph for sex , Bar chart for compliance
• Bar graph for compliance with sex
• Bar chart for compliance of females only
• Histogram of age, duration
• Histogram of female height/ male height
• Box plot for age

62. References
• SPSS for the Classroom: the Basics
https://www.ssc.wisc.edu/sscc/pubs/spss/classintro/spss_stud
ents1.htm
• California state university. IBM SPSS statistics 20. part 1
descriptive statistics.
• IBM SPSS Statistics 20 Brief Guide.
• www. spsstests.com.
‫صالحا‬ ‫كله‬ ‫عملنا‬ ‫اجعل‬ ‫اللهم‬

63. ‫اللهم‬‫به‬ ‫ينتفع‬ ‫علما‬ ‫اجعله‬