BASICS OF STATISTICS- presentation.pptx

BASICS OF STATISTICS
Module Contents
• Introduction to Statistics
• Description of data
• Basics of data collection
• Basics of field work Supervision
• Data summarization
• Overview of estimation
• Hypothesis testing
• Correlation and Regression
• Expectation
Mr. Titus R. Leeyio. Bsc(MS), MSc(EB)

Introduction to Statistics
Outline
• Introduction
• Characteristics of statistics
• Limitations of Statistics
• Classes of Statistics
• Importance of Statistics
• Basic concepts of Statistics

..Introduction to Statistics..
• What is Statistics?
Statistics is a profession of dealing with
information in numbers.
• It is not an easy job, to define Statistics.
• The word Statistics has two meanings/senses:
• Statistics has been defined differently by
different Authors (Bowley, Boddington,
Croxton and Cowden, Horace Secrist etc.).
1.Statistics in singular:
Is the study of principles and methods applied
in collecting, organizing, presenting, analyzing
and interpreting the numerical data in any field
of investigation.

..Introduction to Statistics..
2. Statistics in plural
Statistics in plural sense refers to numerical facts
and figures collected in a systematic manner with a
definite purpose in any field of study. Example
production of rice in Mbeya in year 2012, Statistics
in football game, number of students enrolled at
EASTC in year 2013/2014 etc.
Statistics – as a numerical value calculated from the
sample dataset.

Characteristics of statistics..
1.Aggregate of facts
Statistics does not refer to a single figure but it refers
to a series of figures. A single weigh of 60 kg of a
first year Bachelor Degree is not statistics but a series
relating to the weight of a group of persons is called
statistics. It means, all those figures which relate to
the totality of facts are called statistics. Statistics has
got nothing to deal with what is happening to a
particular object.

..Characteristics of statistics..
2. Affected by Multiplicity of Causes
Statistics are not affected by one factor only,
rather they are affected by a large number of
factors. Example prices are affected by
conditions of demand, supply, money
supply, imports, exports and various other
factors.

3. Numerically expressed
Another characteristic of statistics is that they
are expressed in quantitative form,
qualitative expressions like young, old, good,
bad etc. are not statistics.
4. Estimated according to Reasonable
Standards of Accuracy
Exactness can not be guaranteed.

5. Collected in a Systematic Manner
For accuracy or reliability of data, the figures
should be collected in a systematic manner. If
the figures are collected in a haphazard
manner, the reliability of such data will
decrease. Thus for reasonable standard of
accuracy, the data should be collected in a
systematic manner, otherwise the results
would be erroneous.

6. Collected for a Pre-determined Purpose
The usefulness of the data collected would be
negligible if the data are not collected with some pre-
determined purpose. The figures are collected with
some objective in mind. The efforts made without any
set objective would render the collected figures
useless. Thus the purpose of collecting data must be
decided well in advance. Besides, the objective should
be concrete and specific. For example, if we want to
collect data on prices, then we must be clear whether
we have to collect whole-sale or retail prices. If we
want data on retail prices, then we have to see the
number of goods required to serve the objective.

7. Placed in relation to each other
The collection of data is generally done with the
motive to compare. If the figures collected are not
comparable in that case, they lose a large part of
their significance.
Question
Are figures calculated from ‘kipimajoto’
regarded as statistics?

Limitations of Statistics
1. Statistics does not study qualitative phenomena:
Statistics deals with facts and figures. So the quality aspect
of a variable or the subjective phenomenon falls out of the
scope of Statistics. For example, qualities like beauty,
honesty, intelligence etc. can not be studied unless they are
converted into quantitative form.
2. Statistics does not deal with isolated
measurements:
Individuals facts and figures are of importance to
individuals only, Statistics does not deals with them. It
deals with mass phenomena and therefore, throws
light on the whole of a given group. Statistics deals
with aggregates, though for purpose of analysis these
aggregates are very often reduced to single figures.

3. Statistics can be misused:
Statements supported by statistics are more
appealing and are commonly believed. For this,
Statistics is often misused. Statistical methods
rightly used are beneficial but if misused these
become harmful. Statistical methods used by less
expert hands will lead to inaccurate results. Here the
fault does not lie with the subject of statistics but
with the person who makes wrong use of it since it
requires experience and skill to draw sensible
conclusion from the data.

4. Statistics cannot express the entire story:
Statistics are presented in summaries, or reduced
form, in that way they leave out a lot of
information that may be important.

Classes/Divisions of Statistics
Statistics as a science can be divided into two main
classes/divisions.
1. Statistical Methods
Statistical methods are those devices by which complex and
numerical data are so systematically treated in order to
present a comprehensive and intelligible view of them. These
methods are: Collection, Organization , Presentation, Analysis
and Interpretation.
2. Applied Statistics
Applied Statistics deals with the application of statistical
methods to some specific problems example agricultural
statistics, industrial statistics, labor statistics etc.

Statistical Methods
1.Collection of data
Collection of data constitutes the first step in a statistical
investigations. Utmost care must be taken in collecting data
because they form the foundation of statistical analysis. If data
are faulty, the conclusion drawn can never be reliable. Data
may be available from existing published sources or else may
be collected by the investigator.
2. Organization of data
Organization of data can be done in three ways:
• Editing,
• Classification, and
• Tabulation

Statistical Methods
3.Presentation of data
3.1 Textual
Statistics are expressed in words.
3.2 Tabular presentation
A statistical table can usually be considered to consist of the
following seven basic parts
1. Table number
This number, which precedes the title, serves to identify the
table in case many tables are presented. It is usually a serial
number with suitable sub-numbers to indicate the concerned
main topic and sub-classification.
2. Title
3. Column caption
4. Stub or row caption
5. Body of the table
6. Footnote (if any)
7. Source (if any)

Statistical Methods
3.3 Graphical presentation
• Pictographs
• Pie-charts
• Bar and column charts
• Rectilinear charts
4.0 Analysis
A major part of this is devoted to the methods used
in analyzing the presented data. Methods used in
analyzing data are numerous ranging from simple to
complicated. (Descriptive Statistics and Inferential
Statistics)

Statistical Methods
5.0 Interpretation
The last stage in statistical investigation is
interpretation , drawing conclusions from the
data collected and analyzed. This is a difficult
task and necessitates a high degree of skills
and experience. If the analyzed data are not
properly interpreted then the whole objective
of investigation may be defeated.

Importance of statistics
• Enable to describe/understand the current
situation in any socio-economic set up
• Enable to make evidence/informed decisions –”
you can not measure it, you can not manage it”,
“Statistics are the eyes and ears of any planner
and decision taker”, “Without statistics you have
no right to speak”.
• Enable to make predictions for the future – “A
ship without radar is likely to anchor anywhere”
• Used in planning – “Planning without statistics is
like a blind man groping in a dark trying to locate
a black cat that is not there”.

Basic concepts of Statistics
• Population
A complete set of items under discussion. It is
important for the investigator to carefully and
completely defines the population before
collecting the sample. Population can be finite or
Infinite.
• Universe
It is sometimes used to mean population, but at
times it mean special reference to a population
whose limits are not known.

•Sampling
Sampling is the process of selecting units from a
population of interest so that by studying the
sample we may fairly generalize our results back
to the population from which they were chosen.
Sampling can be probability sampling or non
probability sampling. With probability sampling
we get the so called a random sample.
•Sample
Sample is a subset of population dataset.

• Sampling frame
Is a complete list of all items of a population. In order for
the procedure of sampling to be correct, the sampling
frame must also be correct. The good sampling frame does
not contain inaccurate units, it is free from duplications
and omission, it is not out of date.
• Elementary unit
Each individual member of a population giving an
observation. It is the smallest unit yielding information
which by suitable aggregation leads to the population
under discussion.
Example: If an age distribution is to be estimated from the
sample of households then the person is the elementary
unit but again if the size of the household is to be estimated
then the household will be the elementary unit.

• Attribute
A qualitative changing characteristics that can not
be measured numerically. Example hair color.
• Parameter
A parameter is a value, usually unknown (and
which therefore has to be estimated), used to
represent a certain population characteristic.
Within a population, a parameter is a fixed value
which does not vary. Parameters are often
assigned Greek letters.

• Statistic
Is a numeric value calculated from a sample data set. It is
used to give information about unknown values in the
corresponding population. It is possible to draw more than
one sample from the same population and the value of a
statistic will in general vary from sample to sample.
Statistics are often assigned Roman letters.
• Characteristic
Is a common mark of elementary units of the population in
which we are interested to take our observations. Example
mean, standard deviation.

• Survey Sample Survey
Is a method of collecting detailed information
relating to a representative groups.
• Census (100% sample survey)
Is the process by which information about every
member of a population is collected .example
animal census, population and housing census etc.
• Variable
Variable is any thing that can be assigned different
values in different situations.

• Homogeneity
The property of elementary units with very similar or very
related qualities (characteristics).
• Heterogeneity
The property of elementary units with different qualities
(characteristics).
• Data
Data is a collection of facts such as values or measurements
(are raw information from which statistics are created).

Description of data
• Classification of statistical data according to
source
1. Internal data
These are data that an organization collects within
itself so as to know how it is being run from day to
day. They mainly arise as a by-product of
administration or management.
These data may be non-statistical eg. Data collected
for accounting purpose.

Description of data
2.External data
These are data about an organization but compiled
by an agency outside the organization. This outside
agency may be Government as part of its work
(statistical unit outside the organization) or just
another organization which needs the data about
this organization as an interested part.
It is good as a source of data because sometimes
some data have been overlooked by the organization
itself. Although great care should be taken by the
organization since the purpose of collecting the data
may be different

Description of data
• Classification of statistical data by purpose
1. Primary data
Data which are specially collected to solve a specific
problem, they are collected directly from the field of
enquiry and hence are original in nature. This is
done when the data required for a particular study
can be found neither in the internal records of
enterprises nor in published sources.

Classification of statistical data by
purpose
1. Secondary data
Data collected to solve a problem at one time,
but are being used to solve another problem.
However, secondary data must be used with
utmost care. The reason is such data may be
full of errors because of bias, inadequate
sample size, error of definition.

Description of data
Statistician compile different types of statistics.
Statistics compiled can be grouped into three major
groups.
1. Economic statistics
These covers the economic aspects of people’s life.
Statistics on industrial, infrusture etc.
2. Social statistics
These cover all social aspects of the conditions of life
and work of population. Statistics on housing,
health, education, cultural activities, crime statistics
etc.

Description of data
3. Population statistics
Is the use of statistics to analyze characteristics or
changes to a population.
Exercise:
Identify the type of data for the following cases:
1. Price of food products.
2. Statistics on entertainment and ceremonies.
3. Number of EASTC students in 2013.
4. Enrolment capacity of Primary one.
5. Total number of children.

Description of data
There are mainly two types of data:
1. Qualitative/categorical data
Are data expressing the qualities of units involved in
the investigations. They deal with description and
they can be observed but not measured.
Example: colors, smell, nationality, gender etc.
Qualitative data can be binary, ordinal or nominal
• Binary data
These are data which involve only two categories.
Example: gender, Smoking status, attendance etc.

Qualitative data
• Ordinal data
These are data which involve more than two ordered
categories.
Example: degree of illness, students’ opinions about
Basics of Statistics class.
• Nominal data
These are data which involve more than two
unordered categories.
Example: Nationality.

Quantitative data
2. Quantitative data
These are data on quantities. The figures involved
have their intrinsic values. They deal with numbers
and they can be observed as well as measured.
Examples: Length, weight, number of lecturers etc.
Quantitative data can be discrete or continuous.
• Discrete data
These are data that take only specific values within
the given range. Example number of people, number
of doctors etc.

Quantitative data
• Continuous data
These are data that may take any value within the
given range. Example weight, height, volume etc.
Data can also be classified according to levels of
measurement. The level of measurement of the data
dictates the calculations that can be done to
summarize and present the data. It will also
determine the statistical tests that should be
performed.

Levels of measurement
There are actually four levels of measurement:
nominal, ordinal, interval, and ratio. The
lowest, or the most primitive, measurement is
the nominal level. The highest, or the level that
gives us the most information about the
observation, is the ratio level of measurement.

• Nominal data
Data values serve as labels, but the labels have no
meaningful order. Nominal has no order, distance or
origin. This is the simplest and lowest level of
measurement. Nominal data are qualitative only. Eg
Gender – 1- Male, 2- Female etc
Data presentation: pie- chart and bar chart.
Analysis: Frequency tabulation

• Ordinal data
Ordinal data can be qualitative or quantitative. Data
values serve as the labels but the labels have natural
meaningful order. However, different between values
are meaningless. In other words ordinal data have
order, but no distance or origin. Example rating of
Mathematics lecturer – Strongly like, Like, Dislike,
Strongly Dislike, Statistics grade – A, B,C, D, position
of a student in class etc.

• Interval data
They are always quantitative, data values have
natural meaningful order, the difference between
data values are meaningful. In other words, interval
measurements have both order and distance but no
origin. There is no absolute (true) zero. Example,
temperature, year of birth, time. Manipulation of
numbers is possible.

• Ratio
Has order, distance and origin. It is the highest and
most ideal level of measurement. It is suitable in
measuring properties which have natural zero points.
They are always quantitative, data values are
numerical with natural meaningful order, and the
differences between data values are meaningful and
the ratios between values are meaningful. Zero
measurement indicates absence of the quantity being
measured. Example number of children, distance etc.
Data presentation: histogram, line graph.
Manipulation is possible.

Basics of data collection
Data production cycle
Information/
Data needed to
solve a particular
problem
Data collection
Data processing,
analysis and
interpretation
Dissemination
Report writing

Basics of data collection
There are different methods of data collection
1. Physical observation/measurement (primary data)
2. Interviews (primary data).
3. Questionnaires (primary data).
4. Focus group (primary data).
5. Registration/administrative records (Primary and
secondary).
6. Transcription from records (secondary).

Physical Observation/measurement
• This is the method that involves enumerators to
take observations or measurement on units selected
in the study in order to discover particular
information.
• Observation involves viewing the phenomenon,
recording something about it, video taping, taking
notes about what was seen, counting occurrences of
a phenomenon.
• Observation can be done as structured or non
structured, directly or indirectly, participative or
non participative, obtrusive or non obtrusive.

Physical observation is considered to be the best
method of data collection when a researcher :-
• is trying to understand an ongoing process or
situation,
• gathering data on individual behaviors,
• in need of knowing about a physical setting,
• need to collect data from unwilling respondents.

• Advantages
1. Free from non response error,
2. Free from language problem,
3. Free from the errors of memory failure,
4. Free from errors of prestige.

• Disadvantages
1. Susceptible to observers’ bias,
2. It is not always feasible (realistic),
3. It does not increase observers’ understanding of
why people behave the way they do,
4. Can be very laborious and time consuming,
5. Can be affected by transportation and accessibility
problems.

Interviews
• An interview is a series of questions a researcher
addresses personally to respondents for the
purpose of obtaining research relevant information.
Interviews can be structured or un structured.
• Interviews are useful method to investigate issues
in an in depth way, to discover how individual feel
about a topic and why they hold certain opinion. It
is also useful for investigating sensitive topic where
people may feel uncomfortable discussing them in
a focus group.

Interviews
They can be conducted as:
1. Face to face interview
In face to face interview, an interviewer is physically
present to ask the survey questions and to assist the
respondent in answering them. It is also known as
personal interview. It is probably the most popular
and oldest form of survey data collection. With face to
face interview the interviewer can supplement the
information given by the respondents with personal
observation.

Interviews
2. Telephone interview
Telephone interviewing stands out as the best method
for gathering quickly needed information. Responses
are collected by the researcher on telephone. It is a
very fast method of data collection but it is limited on
supplementing respondents’ explanation with non
verbal communication given by the respondent.

Interviews
3. Computer Assisted Personal Interview (CAPI)
CAPI is usually conducted using portable personal
computer. A researcher conducts face to face
interview using the portable computer. After the
interview the interviewers send the data to a central
computer. With CAPI, routing problems is eliminated
and interviewers cannot miss questions and this is
rarely achieved by face to face interviewers.
However, it takes considerable time to construct and
programme a questionnaire of CAPI, and in
experience interviewers may direct much of their
attention to their portable computers.

Interviews
• Advantages of interviews
1. They usually achieve a high response rate.
2. Respondents own word are recorded.
3. Ambiguities can be clarified.
4. Interviewees are not influenced by others in the
group.

Interviews
• Disadvantages of interviews
1. They can be limited by memory failure.
2. They can be limited by language problem.
3. They can be costly.
4. They can be affected by interviewers’ biases – in
asking and recording answers.

Questionnaires
A standard list of questions relating to the particular
investigation is prepared. This list of questions is
called questionnaire. The data are collected by
sending the questionnaires to the respondents and
requesting them to return the questionnaire after
answering the questions. In order to make this
method successful, a very polite letter is sent to the
respondents emphasizing the need and usefulness of
the problem under investigation.

Questionnaires
Questionnaires can be conducted as paper pencil
questionnaire as well as web based questionnaire.
• Paper pencil questionnaire
Questions are presented on printed paper. The
respondent fill in his/her responses and return the
paper questionnaire to the researcher.
• Web based questionnaire
Questions are presented on the computer. The method
is limited to some people who have no computer,
internet or could not use computer. However, the
method eliminates the need to print the questionnaires
and manually deliver and collect them.

Questionnaires
Questionnaires is a useful method of data collection
when:-
• resources are limited and you need data from many
people.
• it is important to protect the privacy of participants.
(Questionnaires are helpful in maintaining
participants’ privacy because participants’ responses
can be anonymous).

Questionnaires
• Advantages
1. A large number of the population can be contacted
at a relatively low cost.
2. The responses are gathered in a standardized way.
3. They can be used in a sensitive topics.
4. Respondents have time to think about their
responses.
5. They are free from interviewers’ bias.

Questionnaire
• Disadvantages
1. Call backs is not feasible when questionnaires are
anonymous.
2. It is sometime difficult to attain a high response
rate.
3. Respondents may misinterpret questions, and
therefore giving wrong responses.

Focus group
• A focus group is a small group discussion guided by
a trained leader. The group composition is carefully
planned to create a non threatening environment in
which people are free to talk about a focused topic.
• It is useful in understanding why people hold
certain opinion about a certain issue and not
concerned with making statements about a
population.

Focus group
• Advantages
1. Obtaining depth responses, since group members
can often stimulate new thoughts for each other,
2. Provide data more quickly than if individuals were
interviewed separately ,
3. It is relatively cheap,
4. Researcher can gain information from non – verbal
responses to supplement verbal responses.

Focus group
• Disadvantages
1. Some group members may feel hesitant to speak
regardless of how much the leader encourages
team members to contribute.
2. Members’ opinion can be influenced by other
members.
3. Small number and convenience sampling severely
limit ability to generalize about a large population.
4. The leader may knowingly or unknowingly bias
results by providing cues about what type of
responses are desirable.

STEPS OF PRIMARY DATA
COLLECTION
In collecting primary data one has to follow the
following steps/stages.
1. Objective/purposes and resources.
• Laying down the census/survey's objectives (these
are in most cases given by the users or sponsoring
agent).
• The statement(s) of the objective should be precise
and not giving statements of broad aims.
• At this stage one has to ascertain the availability of
reasonable resources to the work.

COLLECTION
2. Coverage
• The population to be covered should be specified:
its geographical, demographic and other
boundaries - and to decide whether it should be
fully (census) or only partially covered.
• The type of sampling design to be used.
• The appropriate sampling unit (administrative
wards, districts, household, family or individual).
• Is the sampling frame available or has to be
developed?
• What is the required size of the sample, to give the
required accuracy, and how big a sample is
sometime depends on available resources.

COLLECTION
3. Questionnaire design
• The framing and arrangement of questions is
perhaps the most substantial planning tasks.
• The scope of the questionnaire, its layout and
printing, the definitions and instructions to go
with it and on the wording and order of the
questions should be carefully watched out.
• Possible tables to be produced using the collected
data should be thought of during this stage of
questionnaire design.
• In practice, design of questionnaire involves more
than one person, including statistician(s), subject
specialist(s), computer specialist(s) and user(s) or
the sponsoring agent.

COLLECTION
4. Questionnaire pre-test
• After the questionnaire has been designed, it is
tested using few units in different places to check
for its feasibility and worthiness to the objectives of
the survey also for the correctness of the various
questions and their order, i.e. if they are easily
understandable by the interviewers as well as
respondents.
• After pre-test there may be a need to restructure the
questionnaire to accommodate all the important
issues which might have been missing and/or the
irrelevant ones are omitted.
• Pretesting the questionnaire may be done more than
once.

COLLECTION
5. Pilot survey
• This is similar to questionnaire pre-test, the only
difference is that the aim here is to try to estimate,
say, the work load of every enumerator and thus
estimating the timing and costs in the real survey.
6. Supervisors and interviewers recruitment and
training
7. Data collection (field work).

TOOLS FOR DATA COLLECTION
The various methods of data gathering involve the use
of appropriate recording forms. These are called tools
or instruments of data collection. They consists of:-
1. Observation schedule /observationnaire
This is a form on which observations of an object or a
phenomenon are recorded.
2. Interview guide
This is used for non- directive and depth interviews. It
does not contain a complete list of items on which
information has to be elicited from a respondent. It
contains only the broad topics or areas to be covered in
the interview.

TOOLS FOR DATA COLLECTION
3. Rating Scale
4.Check list
5. Opinionnaire
6. Document schedule
7. Schedule for institutions
8. Inventories
9. Interview schedule and mailed questionnaire
They are both used widely in surveys. They are both
complete lists of questions on which information is
elicited from the respondents. The basic difference
between them lies in recording responses. A schedule
is filled out by the interviewer while questionnaire is
completed by the respondent.

QUESTIONNAIRE DESIGN
There are no hard-and-fast rules about how to design a
questionnaire, but there are a number of points that
can be borne in mind:
1. A well-designed questionnaire should meet the
research objectives. This may seem obvious, but
many research surveys omit important aspects due
to inadequate preparatory work, and do not
adequately probe particular issues due to poor
understanding.
2. It would keep the interview brief and to the point
and be so arranged that the respondent(s) remain
interested throughout the interview.

3. It should obtain the most complete and accurate
information possible. The questionnaire designer
needs to ensure that respondents fully understand the
questions and encourage respondents to provide
accurate, unbiased and complete information.
4. A well-designed questionnaire should make it easy
for respondents to give the necessary information and
for the interviewer to record the answer, and it should
be arranged so that sound analysis and interpretation
are possible.

Before an investigator starts to constructs questions to
be included in his/her questionnaire he/she should
first decide on what information should be collected,
what will be the target respondents, and what will be
the proper method of data collection. After designing
the draft questionnaire, he/she will need to evaluate
the draft questionnaire (relevance, appropriateness,
clarity and ambiguity, practicability, validity, the
logical order, the length of the instrument and other
aspects).

The revised draft must be pretested to test whether:
i. the instrument would elicit responses required to
achieve research objectives,
ii. the content of the instrument is relevant and
adequate,
iii. wording of question is clear,
iv. the instrument has the required quality – question
structure and sequence.
After pre testing the questionnaire, the procedures or
instructions relating to its use must be prepared and
the format of the questionnaire is designed.

Question construction
This involve four major decision areas:-
1. Question relevance and content
2. Question wording
3. Response form/Types of questions
4. Question order or sequence

1. Question relevance and content
Any question to be included in the instrument should
pass certain tests.
Relevance test: Is it relevant to the research objective?
Can it yield significant information for answering
research questions?
Coverage test: If the question has passed the relevance
test, we should then consider its coverage. Is it double
barreled question that require splitting? Does the
question provide the information needed to interpret
the response fully? Does the question include technical
words?

2. Question wording
The designer should look for words that have the
following characteristics:
i. Shared vocabulary
ii. Uniformity of meaning
iii. Exactness
iv. Simplicity
v. Neutrality
vi. Presumptions
vii. Questions with no embarrassing matters.

3. Response form/Types of questions
The third major area in question construction is to
decide types of questions to be included. The
questions included my be classified as open questions
or closed questions.
The choice between open and closed questions
depends on the situations like Objective of the
interview, Respondents’ level of information about the
topic, investigator’s knowledge about the topic etc.

Advantages of closed ended questionnaires:
• It provides the respondent with an easy method of
indicating his answer - he does not have to think
about how to articulate his answer.
• It 'prompts' the respondent so that the respondent
has to rely less on memory in answering a question.
• Responses can be easily classified, making analysis
very straightforward.

Disadvantages of closed ended questionnaires:
• It force a statement of response in researcher’s terms
rather than respondent’s.
• They often do not reveal things which were not
known by the investigator.

Advantages of open ended questionnaires:
• They allow the respondent to answer in his own
words, with no influence by any specific alternatives
suggested by the interviewer.
• They often reveal the issues which are most
important to the respondent, and this may reveal
findings which were not originally anticipated when
the survey was initiated.

Disadvantages of open ended questionnaires:
• Respondents may find it difficult to properly and
fully explain their attitudes or motivations.
• Data collected has to be coded and reduced to
manageable categories. This can be time consuming
for analysis.
• Respondents will tend to answer open questions in
different 'dimensions'.

4. Question order or sequence
The order in which questions are arranged is
important as question wording. The questions should
begin with simple and general items to more complex
and specific items.

• Common problems with questions
i. Ambiguous terms that are not understood by
respondents. Are you interested in small house?
ii. Questions that beg a certain response. You don’t eat
ladies finger do you?
iii. Questions that embarrass the respondent, have you
stopped beating your wife?

iv. Assigning improper response scales. How often
does your family dine out?
A. Very frequently
B. Once in a while
C. 2-3 times in a week
D. Constantly.
v. Double barreled questions. Do you support the
Competence based system and examination
regulations of ABC institute?
vi. Long questions.

BASICS OF STATISTICS- presentation.pptx

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