This document provides an overview of statistical analysis of questionnaire data. It discusses topics like questionnaire construction, data entry, reliability analysis using Cronbach's alpha, descriptive statistics for Likert scale items including frequencies, medians, interquartile ranges and box plots. It also covers composite scale analysis using means, standard deviations and comparisons between groups. An example is provided on assessing student satisfaction regarding teaching using 4 questionnaire items from 60 students. Results would be reported using tables and figures with interpretations.

1. Zagazig university
Faculty of Veterinary Medicine
Session#2:
Statistical Analysis of Questionnaire Data
M.Afifi
M.Sc., Biostatistics(Co-Supervision with ISSR, Cairo University)
Ph.D., Candidate (AVC, UPEI, Canada)
E-mail: M.Afifi@zu.edu.eg, Afifi-stat6@hotmail.com
Tel: +201060658185

3.  Changing the way you look at questionnaire
 Uses of questionnaire in veterinary research!!!!!!!!!!!!!!

4.  Topics
 Questionnaire Data
 Data Entry
 Data Analysis
 Results (Tables + Figures)
 Report

5. Questionnaire Data

6.  Questionnaire Data
 Consists of group of Major
Items (Construct) assessed by
some questions in order judge
quality of those Constructs

7. Construct
Single Item
Q1

10.  Likert scale and Data Coding
 Likert items are used to measure respondents' attitudes to a particular
question or statement.
 Typical familiar five-point Likert scale

11. 5 point Likert scale

12. 3 point Likert scale

13.  Likert scale Data Coding
 Bipolar scaling method (symmetry), measuring either (+Ve) positive or (-Ve)
negative response to a statement.
 Central tendency : 1-2-3-4-5 =3
 Sometimes a four-point scale is used; since the middle option of "Neither
agree nor disagree" is not available.

14. Reverse coding
 One common validation technique for survey items
is to rephrase a "positive" item in a "negative"
way. When done properly, this can be used to check
if respondents are giving consistent answers.
 For example, concerning our SSQ
‫الحفظ‬ ‫علي‬ ‫يعتمد‬ ‫الدراسي‬ ‫المقرر‬(‫ينور‬ ‫هللا‬ ‫شغال‬)...............

16. Questionnaire Data Entry

17. Data Entry

18. Data Entry
Excel Data Sheet Scanner and OCR

19.  It is preferable to enter data firstly into excel sheet then to be uploaded to
SPSS
 Open Excel Sheet
 Give student ID’s (rows=Cases) for each questionnaire
 Question No. across (Columns=variables)

20.  Template for Data Entry
Questionnaire Questions
Respondents (Students)

21.  For Example to enter 10 question questionnaire for 40 student this will go
like as follows:

24. Upload data onto SPSS
 Open SPSS
 Click cancel on opening screen
 File > Open > Data
 After your data opens up in SPSS, save it in case you have problems later on
(File > Save as >file name)

25. Check for what can go wrong in data entry?
 Max (5)
 Min (1)
 Count (No. of questionnaires)

26. Data Analysis
Reliability Analysis, Cronbach's Alpha

27. Reliability coefficient (Cronbach's Alpha)
 Measure of internal consistency, that is, how closely related
a set of items are as a group.

28. Reliability coefficient (Cronbach's Alpha)
 Example: compute Cronbach's alpha using SPSS, use a dataset
that contains four test items - q1, q2, q3 and q4 (questionnaire.sav.)
 The alpha coefficient for the four items is 0.839, suggesting that the
items have relatively high internal consistency. (Note reliability
coefficient of .70 or higher is considered "acceptable" )

29. Interpreting Reliability coefficient (Cronbach's Alpha)
 range from zero (no reliability) -1.00 (perfect reliability).
 High reliability >>>>questions of a test tended to “pull together.” Students
who answered a given question correctly were more likely to answer other
questions correctly. If a parallel test were developed by using similar items,
the relative scores of students would show little change.
 Low reliability >>>questions tended to be unrelated to each other in terms
of who answered them correctly. The resulting test scores reflect
peculiarities of the items or the testing situation more than students’
knowledge of the subject matter.

30. NB:
If a questionnaire includes positively-keyed and
negatively-keyed items, then the negatively-
keyed items must be “reverse-scored” before
computing total scores and before conducting
reliability analysis)

31. Data Analysis
I. Simple/Basic Statistical analysis
Descriptive Statistics

32. I. Simple/Basic Statistical analysis
The data analysis decision for Likert items depends on the objective for which
questionnaire was developed development.
 If you have a series of individual questions that have Likert response
options for your participants to answer. Modes, frequencies.
 If you have a series of Likert-type questions that when combined describe
a personality trait or attitude - use means and standard deviations to
describe the scale.

33. Construct
Likert Scale
Single Item
Q1

34. I. Likert-type question (item) Single-item :
 Each single questions

35.  Frequencies and Distribution each alternative
 The number and percentage of students who choose each
alternative are reported. i.e. (% that agree, disagree etc)
 Use mode the most frequent
 The bar graph on the right shows the percentage choosing each
response

36. Pooled respondents’ opinions on the statements

37. Pooled respondents’ opinions on the statements (Questions)

38. Clustered Bar Chart

39. Stacked Bar Chart

41.  Medians and Interquartile range
 Medians: number found exactly in the middle of the distribution
 a measure of central tendency
 roughly speaking, it shows what the ‘average’ respondent might
think, or the ‘likeliest’ response.
 IQR :a measure of dispersion: it shows whether the responses are
clustered together or scattered across the range of possible
responses.

42.  Example
 Question of 5 point scale, ranging from “1=strongly disagree” to
“5=Strongly agree”. Were filled by 60 students
 The number of respondents was as follows
 How do I interpret this data???????????????

43.  Data:

44.  Calculating the median
 This ‘middle’ number is your data ( In case of Odd No.)
 Two middle numbers the median is half-way between them (In
case of even No.).
 Median = 3

45.  Calculating the IQR
 Use same arrangement of responses that we used above. When you
divide this line into four equal parts, the ‘cut-off’ points are called
quartiles. (IQR = 4 – 3 = 1)
1st Q 3rd Q2nd Q

46. Interpretation: Reporting the data
Consensus and dissonance
‫والتنافر‬ ‫التوافق‬
 A relatively small IQR (0-1), as was the case above, is an
indication of consensus.
 larger IQRs suggest that opinion is polarised, i.e., that your
respondents tend to hold strong opinions either for or against this
topic (dissonance)

47.  For Example
 Mdn=4, IQR=0 most respondents indicated agreement with the
statement
 Mdn=3, IQR=3 If we report that the respondents are,
on average, undecided, that would be a statistical distortion of the data.
 report more accurately: “Opinion seems to be divided with regard to… .
Many respondents (N=28, 47%) expressed strong disagreement or
disagreement, but a roughly equal number (N=26, 43%) indicated that they
agreed or strongly agreed

48. Averages (mean)
 Average = 3.3 something between ‘undecided’ and ‘disagreement’.
 ‘Our study revealed mild disagreement regarding this Q.
 This is statistical nonsense not an optimal interpretation. Such an
argument relies on the assumption that the psychological distance
between ‘strong agreement’ and ‘agreement’ is the same as that
between ‘agreement’ and ‘no opinion’..
 Don’t use “Ordinal data cannot yield mean values”

49. Box-plots

50. Box-plots

52. II. Composite (summated) scales:
 Composed of a series of four or more Likert-type items that are combined
into a single composite
 Measure concept, e.g. the feeling (social presence) can not be measured
directly also called latent variable. To measure such "soft" implicit
variables with questionnaires, several questions are asked. They then can
be combined into a single composite variable,
 Created by adding up all the values with a potential score from min (no
amenities) to max (all amenities).
 Let us look at the central tendency and dispersion of the index

53. II. Composite (summated) scales:
 Mean : characterize the center of the data
Standard Deviation: measures of variability of the data around the mean
Coefficient of Variation:
No. and (%) below and above the average

55.  Data Analysis
 II. More Elaborate analysis comparison between genders,
 Factors impacting student satisfaction
 Academic achievement pre-enrolment
 Social factors
 Financial factors
 External factors
 Work commitments
 Institutional factors

56.  Worked Example
 Assume that we want to asses student satisfaction regarding teaching
 4 Questions
 60 student

57.  Report (Results )
 Tables
 Figures
 Interpretation

58.  Frequencies and Distribution each alternative
 Considering our Questionnaire.sav
 Analyze >>> Descriptive Stat >>> Frequencies

59.  Frequencies and Distribution each alternative
 Considering our Questionnaire.sav
 Analyze >>> Descriptive Stat >>> Frequencies

60.  Frequencies and Distribution each question

61.  Frequencies and Distribution each Q

62.  To get the medians and IQR

63.  Keep in mind your code book

64.  Report (Results )