data collection (methods/ tools/ techniques), primary & secondary data, qualitative & ...

Data Collection, Assessment of Qualitative Data, Data Processing:

Key Issues

Bikash Sapkota

B. Optometry

Institute of Medicine, TU, Nepal

• Introduction to data

• Classification of data

• Collection of data

• Methods of data collection

• Assessment of qualitative data

• Processing of data

- Editing

- Coding

- Tabulation

- Graphical representation

Presentation Layout

What is data?

Data are observations or evidences about the social world

Data, the plural of datum, can be quantitative or qualitative in nature

‘data is produced, not given’; that is, researchers choose what to call data, it is not just ‘there’ to be ‘found’. (Marsh 1988)

- The Sage Dictionary of Social Research Methods

The terms 'data' and 'information' are used interchangeably

However the terms have distinct meanings

Data

Facts, events, transactions which have been recorded

Input raw materials from which information is

processed

Information

Data that have been produced in such a way as

to be useful to the recipient

Basic data are processed in some way to form

information

Data & Information

The research studies in behavioral science are mainly concerned with the characteristics or traits

Thus, tools are administered to quantify these characteristics

- but all traits or characteristics can not be quantified

The data can be classified into two broad categories:

Data

Qualitative Data or Attributes

Quantitative Data or Variables

Nature of Data

Nature of Data

1.Qualitative Data or Attributes

The characteristics or traits for which numerical valuecan not be assigned, are called attributes

e.g. gender, motivation, etc.

2. Quantitative Data or Variables

The characteristics or traits for which numerical valuecan be assigned, are called variables

e.g. height, weight etc.

Constants

A constant is all characteristic or condition that is the same for all the observed units or sample subjects of a study

Variables

The characteristic or the trait in the behavioral science which can be quantified is termed as variable

Variables

Continuous variables Discrete variables

Variables

1. Continuous variables

A characteristic whose observation can take any values over a particular range

It can assure either fractional or integral values E.g. wt. of children in kg, height of pt.

2. Discrete variables

Are those on the other hand, which exist only in units not the fractional value (usually units of one)

E.g. No. of cataract pts. in a village, WBC count

Attribute vs. Variable

Attribute Variable

A category of a characteristic, to which a subject either belongs or does not belong or property that a subject either possesses or does not possess

The attributes are becoming sick, describing blood group etc.

Variable describes a characteristic in terms of a numerical value, which is expressed in units of measurements

The variables are height, weight, blood pressure, age of pts. etc.

Qualitative Data

In such data there is no notion of magnitude of size of the characteristic

They are just categorized

The data are classified by counting the individuals having the same characteristics or attribute and not by measurement

For examples: Gender: male/femaleDisease: present/absentSmoke: smoking/not smoking

These data can be measured in nominal and ordinal scales

Quantitative Data

Anything that can be expressed as a number, or quantity or magnitude

Describes characteristics in term of a numerical value, which are expressed in units of measurements

E.g. level of hemoglobin in the blood, no. of glaucoma pts., intra ocular pressure, weight, etc.

Quantitative observations: as each individual is represented by a number

These data can be measured in interval and ratio scales

Measurement Scale

The choice of appropriate statistical technique depends upon the type of data in question

Qualitative

Data

• Nominal Scale

• Ordinal Scale

Quantitative

Data

• Interval Scale

• Ratio Scale

Nominal Scale

The least precise or crude of the 4 basic scales of measurement

Implies the classification of an item into 2 or more categories without any extent or magnitude

There is no particular order assigned to them

The frequency or numbers are used to give a name to something that may be used for determining per cent, mode

Eg. boys and girls; pass and fail; rural and urban

Ordinal Scale

The ordinal scale is more precise scale than the nominal scale

The variables has been categorized or leveled with meaningful natural order

But there is no information about the interval

Eg. Pain: none, mild, moderate, severe

Interval Scale

The interval scale is more precise and refined scale than nominal and ordinal scales

This scale has all the characteristics and relationship of the ordinal scale, besides which distances between any two numbers on the scale are known

The size of interval between two observations can be measured

Eg. The temperature of a body

Ratio Scale

It has the same properties as an interval scale as well as a true or absolute zero value

The ratio scale numerals have the qualities of real numbers, and can be added, subtracted, multiplied or divided

Eg. Mean systolic BP

Process of systematic gathering of data for a particular purpose from various sources, that has been systematically observed, recorded, organized

It is the first step of statistical study

There are several ways of collecting data

The choice of procedures usually depends on the objectives and design of the study and the availability of time, money and personnel

Collection of Data

To obtain information

To keep on record

To make decisions about important issues

To pass information onto others

For research study

Purpose of Data Collection

Data collection is an extremely important part of any research because the conclusions of a study are based on what the data reveal

How Important it is?

Nature, scope & objective of the enquiry

Sources of information

Availability of fund

Techniques of data collection

Availability of trained persons

Factors to be considered before data collection

Example: DocumentsCreative worksInterviewsMan-made materialsSurveys

Example:Unpublished thesis and dissertationsManuscriptBooksJournals

Sources of Data

Source of Data

External

Primary Data Secondary Data

Internal

Internal sources of Data

o Many institutions anddepartments have informationabout their regular functions ,for their own internalpurposes

o When those information areused in any survey is calledinternal sources of data

o Eg. social welfare society

External sources of data

o When information is collected from outside agencies is called external sources of data

o Such types of data are either primary or secondary

o This type of information can be collected by census or sampling method by conducting survey

Internal & External Sources of Data

Data collected by investigator from personal experimental studies for a specific research goal is called primary data

The data are collected specially for a research project

Used when secondary data are unavailable and inappropriate

Data are to be unique, original, reliable and accurate in nature

Primary data hahe not been changed or altered by human beings, therefore its validity is greater than secondary data

Primary Data

Demerits

Evaluated cost

Time consuming

More number of resources

are required

Inaccurate feedback

Required lot of skill with

labor

Targeted issues are

addressed

Data interpretation is better

Merits

High accuracy of data

Greater control

Address as specific research

issues

Primary Data

Interview (direct/indirect)

Schedule

Questionnaires survey

Focus group discussion (FGD)

Community forums and public hearings

Observation

Case studies

Key informants interview

Internet/E-mail/SMS

Primary Data Collection Techniques

The data is collected by the investigator personally, he/she

must be a keen observer

He/she asks or cross-examines the informant and collects

necessary information

It is original in character

Direct personal observation

Direct personal observation is adopted in the following cases

Where greater accuracy is needed

Where the field of enquiry is not large

Where confidential data are to be collected

Where sufficient time is available

Suitability of direct personal observation

Merits

Original data

True and reliable data

Encouraging response

because of personal

approach

A high degree of accuracy

Direct personal observation

Demerits

Unsuitable in large area

Expensive & time-consuming

Untrained investigator brings

worst results

Collection of information

according to the ease of the

informant

The investigator approaches the witness or third parties,

who are in touch with the informant

The enumerator interviews the people, who are directly or

indirectly connected with the problem under the study

Generally this method is employed by different enquiry

committees and commissions

The police department generally adopts this method to

get clues of thefts, riots , murders, etc.

Indirect oral interview

It is more suitable when the area to be studied is large

It is used when direct information cannot be obtained

This system is generally adopted by governments

Suitability of indirect oral interview

Merits

Simple and convenient

Saves time, money and labor

Useful in investigation of a large area

Adequate information can be had

Demerits

Information can’t be relied as absence of direct contact

Interview with an improper man will spoil the results

To get real data, a sufficient no. of people are to be interviewed

Careless attitude of informant affects the degree of accuracy

Indirect oral interview

The local agents or correspondents will be appointed, they

collect the information and transmit it to the office or person

They do according to their own ways and tastes

Adopted by newspapers, agencies, etc.

The informants are generally called correspondents

Suitable in those cases where the information is to be

obtained at regular intervals from a wide area

Information through agencies

Merits

Demerits

Extensive information can be had

It is the most cheap and economical method

Speedy information is possible

It is useful where information is needed regularly

The information may be biased

Degree of accuracy cannot be maintained

Uniformity cannot be maintained

Data may not be original

Information through agencies

The questionnaires is sent to the respondents, there are blank

spaces for answers

A covering letter is also sent along with the questionnaire,

requesting the respondent to extend their full cooperation

Adopted by research workers, private individuals, non-officials

agencies and government

Appropriate in cases where informants are spread over a wide

area

Mailed questionnaires

Merits

Of all the methods, the mailed questionnaire is the most

economical

It can be widely used, when the area of investigation is large

It saves money, labor and time

Demerits

Cannot be sure about the accuracy and reliability of the data

There is long delay in receiving questionnaires duly filled in

Mailed questionnaires

Very similar to the questionnaire method

The main difference is that a schedule is filled by the enumerator who is specially appointed for the purpose

Enumerator goes to the respondents, asks them the questions from the Performa in the order listed, and records the responses in the space provided

Enumerators must be trained in administering the schedule

Data Collection Through Schedules

A detailed study of geographical area to gather data, attitudes, impressions, opinions, satisfaction level etc., by polling a section of the population

Census Survey

• Conducted regularly at large interval of time

Continuous Survey

• Conducted regularly and frequently

Ad-hoc Survey

• Conducted at specific times for specific need

• ‘as and when’ required

Survey

Types

Merits

Cover large population

Less expensive

Information is accurate

Demerits

On small scale survey avoided

Time consuming

Information does not penetrate deeply

Researcher must have good knowledge

Survey

It is the method of comprehensive study of social unit which may be a person, a family, an institution, an organization or a community

Merits

Direct behavioral study

Real & personal experience record

Make possible the study of social change

Increase analysis ability & skills

Demerits

One case almost different from another case

Personal bias

Use only in limit sphere

More time & money consuming

Case Study

Useful to further explore a topic, providing a broader understanding of why the target group may behave or think in a particular way

And assist in determining the reason for attitudes and beliefs

Conducted with a small sample of the target group and

Used to stimulate discussion and gain greater insights

Focus Group Discussion

Merits

Useful when exploring cultural values and health beliefs

Can be used to explore complex issues

Can be used to develop hypothesis for further research

Do not require participants to be literate

Demerits

Lack of privacy/anonymity

Potential for the risk of ‘group think’

Potential for group to be dominated by one or two people

Group leader needs to be skilled at conducting focus groups, dealing with conflict, drawing out passive participants

Time consuming to conduct and analyse

Focus Group Discussion

Application and combination of several research methods in the study of the same phenomenon

Researchers can hope to overcome the weakness or intrinsic biases and the problems that come from single method, single-observer and single-theory studies

The purpose of triangulation in qualitative research is to increase the credibility and validity of the results

Triangulation

Types (Denzin 1978)

Data Triangulation

Investigator Triangulation

Theory Triangulation

Methodological Triangulation

Beating the Bias

Secondary data are those data which have been already

collected and analysed by some earlier agency for its own

use and later the same data are used by a different agency

Published Sources Unpublished Sources

Sources of Secondary Data

Secondary Data

Various governmental, international and local agencies

publish statistical data, and chief among them are:

International publications: They are UNO, WHO, Nature, etc.

Official publications of Government: Department of Drug

Administration, Central Bureau of Statistics

Semi-Official publications: Semi-Govt. institutions like

Municipal Corporation, District Board, etc. publish reports

Published Sources

Publications of Research Institutions: Nepal Development

Research Institute, Nepalese Journal of Ophthalmology etc.

publish the finding of their research program

Journals and Newspapers: Current and important materials

on statistics and socio-economic problems can be obtained

from journals and newspapers like, Swasthya Khabar Patrika,

Health Today Magazine, The Sight, etc.

Published Sources

Records maintained by various government and private

offices

Researches carried out by individual research scholars in

the universities or research institutes

According to Prof. Bowley “It is never safe to take published statistics

at their face value without knowing their meaning and limitations and

it is always necessary to criticize arguments that can be based on

them.”

Unpublished Sources

Before using the secondary data, the investigators should

consider the following factors:

Precautions in the use of Secondary Data

Suitability of data

Adequacy of data

Reliability of data

Reliability of data – may be tested by checking:

Who collected the data?

What were the sources of the data?

Was the data collected properly?

Suitability of data

Data that are suitable for one enquiry may not be necessarily suitable in another enquiry

Objective, scope and nature of the original enquiry must be studied

Adequacy of data – data is considered inadequate, if they are related to area which may be either narrower or wider than the area of the present enquiry

Secondary Data must possess the following characteristics

Primary data

o Real time data

o Sure about sources of data

o Help to give results/ finding

o Costly and time consuming

process

o Avoid biasness of response

data

o More flexible

Secondary data

o Past data

o Not sure about of sources of

data

o Refining the problem

o Cheap and no time

consuming process

o Can not know in data

biasness or not

o Less flexible

The characteristics or traits for which numerical value can not be assigned, are called qualitative data (attributes)

e.g. gender, color, honesty etc.

Methods of collecting qualitative data

Methods of Qualitative Data Collection

Direct Observation

In-depth Interview

Case Study TriangulationUse of

Secondary Data

Assessment of Qualitative Data

Classification of Qualitative data

Qualitative Data

Geographical Classification

Chronological Classification

Qualitative Classification


Tabulation of Qualitative Data

Qualitative data values can be organized by a frequency distribution

A frequency distribution lists

– Each of the categories

– The frequency/counts for each category


Frequency Table

A simple data set is: cataract, cataract, keratoconus, glaucoma, glaucoma, cataract, glaucoma, cataract

A frequency table for this qualitative data is

The most commonly occurring eye condition is cataract

Eye condition Frequency

Cataract 4

Keratoconus 1

Glaucoma 3


What Is A Relative Frequency? The relative frequencies are the proportions (or percents)

of the observations out of the total

A relative frequency distribution lists– Each of the categories– The relative frequency for each category

Relative frequency = Frequency/Total


Relative Frequency Table

A relative frequency table for this qualitative data is

A relative frequency table can also be constructed with percents (50%, 12.5% and 37.5% for the above table)

Refractive Error Relative Frequency

Cataract .500 (=4/8)

Keratoconus .125 (=1/8)

Glaucoma .375 (=3/8)


Graphical representation Of Qualitative Data

Bar Diagram

Pie or Sector Diagram

Line Diagram

Pictogram

Map Diagram or Cartogram


Data Processing

The data, after collection, has to be prepared for analysis

Collected data is raw and it must undergo some processing before analysis

The result of the analysis are affected a lot by the form of the data

So, proper data processing is must to get reliable result

Data Processing

Checking the questionnaires and schedules

Reduction of mass data to manageable proportion

Sum up the materials so as to prepare tables, charts,graphs and various groupings and breakdowns forpresenting the result

Minimizing the errors which may creep in at various stageof the survey

Objectives of Data Processing

1. Manual Data Processing

Involves human intervention

Implies many chances for errors, such as delays in data capture, high amount of operator misprints

Implies higher labor expenses in regards to spending for equipment and supplies, rent, etc.

Types of Data Processing

2. Mechanical Data Processing

Different calculations and processing are performed using mechanical machines like calculators etc.

The use of mechanical machines makes data processing easier and less time- consuming

The chances of errors also become far less than manual data processing


3. Electronic Data Processing

Processing of data by use of computer and its programs


4. Real Time Processing

There is a continual input, process and output of data

Data has to be processed in a small stipulated time period (real time)

Eg, when a bank customer withdraws a sum of money from his or her account it is vital that the transaction be processed and the account balance updated as soon as possible


5. Batch Processing

In a batch processing group of transactions collected over a period of time is collected, entered, processed and then the batch results are produced

Batch processing requires separate programs for input, process and output

It is an efficient way of processing high volume of data

Eg, Payroll system, examination system and billing system


QUESTIONNAIRE CHECKING EDITING CODING CLASSIFICATION

TABULATIONGRAPHICAL

REPRESENTATIONDATA CLEANINGDATA ADJUSTING

The processing of data involves activities such as

Important Steps in Data Processing

When the data is collected through questionnaires, the first steps of data process is to check the questionnaires if they are accepted or not

Not accepted if:

Gives the impression that respondent could not

understand the questions

Incomplete partially or fully

Answered by a person who

has inadequate knowledge

Questionnaire Checking

Process of examining the data collected in questionnaires/schedules

to detect errors and omissions

to correct these when possible

to make sure the schedules are ready for tabulation

Data Editing

Editor is responsible for seeing that the data are;

Accurate as possible

Consistent with other facts secured

Uniformly entered

As complete as possible

Acceptable for tabulation and arranged to facilitate coding tabulation

Data Editing

• Data form complete

• Free of bias, errors, inconsistency and dishonesty

Editing for quality

• Modification to facilitate tabulation,

• Ignoring extremely high/low

Editing for tabulation

• Translating or rewritingField editing

• Wrong and replacementCentral editing

Types of Editing

To gather information

To make data relevant and appropriate for analysis

To find errors and modify them

To ensures that the information provided is accurate

To establish the consistency of data

To determine whether or not the data are complete

To obtain the best possible data available

Necessity of Editing

Process of assigning numerals or other symbols to answers so that responses can be put into limited number of categories or classes

Translating answers into numerical values or assigning numbers to the various categories of a variable to be used in data analysis

Coding is done by using a code book, code sheet, and a computer card

Coding is done on the basis of the instructions given in the codebook

The codebook gives a numerical code for each variable

Coding of Data

72

• A codebook contains coding instructions and the necessary information about variables in the data set

• A codebook generally contains the following information:

- column number

- record number

- variable number

- variable name

- question number

- instructions for coding

Codebook

To organize data code

To form structure for coding

For interpretation of data

For conclusions of data coded

To translating answers into numerical values

To assign no. to the various categories for data analysis

It is necessary for efficient analysis

Necessity of Coding

The process of arranging the primary data in a definite pattern and presenting it in a systematic way

The crude data obtained from experiment or survey is classified according to their properties

Classification cab be done by qualitatively or quantitatively

Classification of Data

The classified data is more easily understood

It presents the facts into a simpler form

It facilitates quick comparison

It helps for further statistical treatment such as average, dispersion etc.

It detects the error easily

Objectives of classification

Qualitative classification

Geographical classification

Chronological classification

Qualitative classification

Quantitative classification

Discrete classification

Continuous classification

Types of classification

Geographical Classification

Data are classified by location of occurrence (i.e. area, region) eg cataract pts. district wise

Chronological classification

Data are classified by time of occurrence of the observations, events

The categories are arranged in chronological order

eg, no. of trachoma pts. recorded from 2000 to 2010


Qualitative classification (Classification according to attributes)

Data are classified according to some quality such as religion, literacy, sex, occupation etc.

Simple classification

Classification is made into 2 classes, such as classification by male or female

Manifold classification

2 or more than 2 attributes are studied simultaneously

Eg. Classification according to sex, again marital status and again literacy


Process of systematic organization and recording of long series of data for further analysis and interpretation into rows and columns

It is concise, logical & orderly arrangement of data in a columns & rows

Tabulation

It presents an overall view of findings in a simpler way

To identify trends

It displays relationships in a comparable way between parts of the findings

It conserves space and reduces explanatory and descriptive statement to a minimum

It facilitates the process of comparison

It provides a basis for various statistical computations

Usefulness of Tabulation

Graphical Representation

Graphs help to understand the data easily

A single picture is worth a thousand words-so goes a common saying

The non statistical minded people also easily understands the data and compares them

Most common graphs are bar charts and pie charts in qualitative study and histogram in quantitative study


Advantages

It is easier to read

Can show relationship between 2 or more sets of observations in one look

Universally applicable

Has high communication power

Simplifies complex data

Has more lasting effect on brain

Presentation of Qualitative data

1. Bar Diagram

• Consists of equally spaced vertical (or horizontal) rectangular bars of equal width placed on a common horizontal (or vertical) base line

• The categories are placed on X-axis and their frequencies on Y-axis



0

100

200

300

400

BPH MBBS B.Optom B.Pharma

NO

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STU

DEN

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HEALTH PROGRAM

Health Program at IOM

Simple Bar diagram

Component Bar diagram

Multiple Bar diagram


2. Pie Chart

• Circular diagram divided into segments and each segment represent frequency in a category


Production of health manpower yearly

PictogramLine diagram

Cartogram


Presentation of Quantitative Data

1.Histogram

• Graphical representation of a set of contiguously drawn bars

• Most popular graph for continuous variable


Frequency Polygon

Frequency Curve

Scatter Diagram Time Plot


Stem-leaf Display

Box-and-whisker Plot

Includes consistency checks and treatment of missing responses

Although preliminary consistency checks have been made during editing, the checks at this stage are more thorough and extensive, because they are made by computer

Computer packages like SPSS, SAS, EXCEL and MINITAB can be programmed to identify out-of-range values for each variable

Data Cleaning

If any correction needs to be done for the statistical analysis, the data is adjusted accordingly

Data Adjusting

Data adjusting is not always necessary but it may improve the quality of analysis sometimes

Data Analysis

• Biostatistics by Prem P. Panta

• Fundamentals of Research Methodology and Statistics by Yogesh k. Singh

• Research Design by J. W. Creswell

• Internet

References

Thank

you

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