COLLECTING THE DATA

KRISHNA RAJ NSASITH PAUL K

RESEARCH METHODOLOGY

OVERVIEW OF DATA COLLECTION

Virtually every management research project will involve some type of dataCollection

Data collection must be well planned and managed

Data is the raw material of problem solving and decision making

Management researcher is interested in information rather than data

Must know not only what data is required, but also approaches and techniquesfor collecting data

What are the principal types of data ?How may we classify data ?Data collection methods ?What are the principle ways ?Techniques of collecting data ?

OVERVIEW OF DATA COLLECTION

VARIABLES• The concept of a variable is basic but vitally important• A variable is anything that varies and can be measured

• The values that the variable takes will vary when measurements are made on different objects or at different times. • Variables differ in how well they can be measured, i.e., in how much

measurable information their measurement scale can provide

In general, a variable may fall into one of two types, viz., quantitative variables and qualitative variables.

• Quantitative variables are those for which the value has numerical meaning. The value refers to a specific amount of some quantity. They are also called metric variables or measurement variables. • Measurement variables tell us “how much” of something each case has.

You can do mathematical operations on the values of quantitative variables (like taking an average)• A good example would be a person's weight

• The quantitative variables can be broken down into two types, viz., discrete variable and continuous variable.

VARIABLES

• Discrete: A discrete variable is one which can only take certain fixed numerical values, there are usually gaps between the values. Discrete variables have numerical values that arise from a counting process.• Continuous: A continuous variable in one which - in

principle at least – can take any numerical value within a specific range. Continuous variables produce numerical responses that arise from a measuring process.

VARIABLES

Qualitative Variables are those for which the value indicates different groupings. They are also called Attributes or Categorical Variables

For the purpose of analysis we assign an arbitrary numerical value to such a variable. Objects that have the same value on the variable are the same with regard to some characteristic, but you can't say that one group has “more" or “less" of some feature.

Thus, the categorical variables tell us only “what kind” or category a case belongs in. It doesn't really make sense to do math on categorical variables.

VARIABLES

DEPENDENT VARIABLE AND

INDEPENDENT VARIABLE

Dependent Variable : Any variable that depend upon other factors

It represent the output whose variation is being studied

Example : Exam score It depends upon how much time you studiedHow much sleep you got last nightYour physical condition during examination etc.

Independent Variable: A variable that stands alone and isn’t changed by other variables

It represents inputs or causes for variation

Example: Someone's age , t doesn’t depend upon any variable

DEPENDENT VARIABLE AND

INDEPENDENT VARIABLE

ANTECEDENT VARIABLES AND

INTERVENING VARIABLES

• Antecedent Variables : Any variable that explains the relationship between two variables by its prior impact on the two variables

• Example : Social class affect an relationship between income and political party support

ANTECEDENT VARIABLES AND

INTERVENING VARIABLES

• Intervene Variables :They used to explain relationship between observed variables such independent and dependent variables, also called mediating variable

• Example: Income and longevity is not fully related, money cant make life longer. But people with money get high medical care than others. Here medical care is an intervening variables

DICHOTOMOUS VARIABLEAND

DUMMY VARIABLE

Dichotomous VariableA variable that has only two possible categories such as gender

Dummy VariableDichotomous qualitative variable coded as ‘1’ if the characteristic is present and ‘0’ if the characteristic is absent

DATA AND DATA SET

• Data is a collection of facts, figures and statistics related to an object. • Each time that we record information about an object we

observe a case. • We might include several different variables in the same case.

For example, we might measure the age, sex, height, weight, and hair color of a group of people in an experiment. • We would have one case for each entity

DATA AND DATA SET

•When the raw data are organized in a row-by-column format, with each row representing one case and each column representing one variable, it is called a data set.•

CLASSIFICATION OF DATA

Some common modes of classification are:

1) geographical, i.e., area wise or region-wise; 2) chronological, temporal, or historical, i.e., with respect

to occurrence of time; 3) qualitative, i.e., by character or by attributes; and4) numerical, quantitative or by magnitude.

• Before you collect data for a research study, consider carefully which of the four types of data you are collecting and how you will use them once you have them

• The four widely used classification of measurement scales are:

TYPES OF DATA

Nominal Ordinal Interval Ratio

The lowest measurement level you can useIn nominal measurement the numerical values are assigned to name the attribute uniquely.

In this scale, the numbers or letters assigned to objects serve only as labels or tags for identification and classification of objects.

A nominal scale simply place data into categories, without any order or structure.

The numbers do not reflect the amount of the characteristic possessed by the objects.

These are scales in name only.

NOMINAL DATA

• It is the least powerful measurement

• The counting of members in each group is the only possible arithmetic operation when a nominal scale is employed.

• No measure of dispersion can be used.

NOMINAL DATA

• An ordinal scale is next up the list in terms of power of measurement

• Ordinal data include the characteristics of the nominal scale plus an indicator of order

• In ordinal measurement the attributes can be rank-ordered

• Here, the distance between the attributes do not have any meaning

ORDINAL DATA

ORDINAL DATA• In addition to the counting operation allowable for nominal

scale data, ordinal scales permit the use of statistics based on centiles, e.g., percentile, quartile

• Median is the appropriate measure of central tendency

• A percentile or quartile measure reveals dispersion. Rank correlation and a few nonparametric tests of significance can be applied

• Interval data have the power of nominal and ordinal data plus one additional strength: they incorporate the concept of equality of interval. Thus, in interval measurement the distance between attributes does have a meaning.• The zero point on an interval scale is arbitrary and is not a true

zero. • It permits comparison of the differences between objects. Example:

when we measure temperature(in Fahrenheit) the distance from 30-40 is same as distance from 70-80.

INTERVAL DATA

RATIO DATA

• A ratio scale is the top level of measurement and is not often available in social research.• Ratio data incorporates all the powers of the interval data plus the

provision for absolute zero or origin. Ratio data represent the actual amounts of a variable.• Here, we can construct actual fractions(or ratios) with a a ratio

variable. • Examples: Height, weight, distance, etc.• All statistical techniques can be applied to ratio data.

LEVELS OF MEASUREMENT

Ordinal

interval

Ratio

NominalClassification

OrderClassification

OrderClassification Distance

Natural Origin

OrderClassification Distance

RAW DATA

When data are collected, the information obtained from each member of a population or sample is recorded in the sequence in which it becomes available. Such data, before they are grouped or ranked, are called raw data.

CROSS-SECTIONAL AND TIME SERIES DATA

• Cross-sectional data are collected at the same or approximately the same point in time.• Example: data detailing the number of road accidents in 28 Indian

states in June 2013

• Time series data are collected over several time periods.• Example: data detailing the number of road accidents in each of the 28

Indian states in the last 36 months

PRIMARY AND SECONDARY SOURCES OF DATA

• The sources from which data can be collected are divided into primary and secondary:

• Primary data is the data collected by an individual or organization to use specifically for the purpose of the investigation at hand. The primary data is collected by conducting experiments, investigations, observation, interviews, and surveys and by using questionnaires.

• Secondary data is facts and information gathered not for the immediate study at hand but for some other purpose• Secondary data has been gathered by others for their own

purposes, but the data could be useful in the analysis of a wide range of real property. In general, secondary data exists in published sources- both print and electronic.

PRIMARY AND SECONDARY SOURCES OF DATA

A population consists of all elements—individuals, items, or objects— that we are interested in studying.

When researchers gather data from the whole population for a given measurement of interest, they call it census. • A sample is a finite subset of the population, if properly

taken, is the representative of the population. • Data can be collected from a sample to answer questions

about the population.

POPULATION VERSUS SAMPLE

SAMPLE SIZE

• Large samples express greater expected variation

• Large samples represent population better than small samples

SAMPLE SIZE SELECTION

Statistical analysis plannedExpected variability within subsets of the sample

Tradition in our research

SAMPLING DESIGN

• Sampling design is a design that specifies the population frame , sample size, sample selection and estimation method in detail

• It is a definite plan for obtaining the sample from a population

SAMPLE FRAMEIt is the record of the population from which a sample can be

drawn

RANDOM SAMPLING (CHANCE SAMPLING)

• Random sampling is the purest form of probability sampling (the best sampling method available)

• Each member of the population has an equal chance of being selected

• The sample units are drawn without showing any regard to the character of the population

SYSTEMATIC SAMPLING(QUASI RANDOM SAMPLING)

• Involves ‘Ordering of the universe’

• Ordering may be in alphabetical , numerical, geographical etc.

• Every nth member is selected from the order

• Sampling interval = Size of population

Size of the sample

STRATIFIED SAMPLING

• All people in sampling frame are divided into strata

• Stratum means groups or categories

• Strata is developed on basis of homogenity

• From each stratum , random samples or systematic samples are selected

OTHER SAMPLING METHODS CLUSTER SAMPLING

• Used when no satisfactory sampling frame available• Population is divided into clusters or large groups

RANDOM ROUTE SAMPLING

Used In market research surveys (example: sampling households)An address is selected randomly from registerThen every nth address by alternate left and right turns

OTHER SAMPLING METHODS ACCIDENTAL SAMPLING

• Researcher simply contacts and pickup those cases which he come across and continue the process (e.g.: 1st 100 willing persons)

QUOTA SAMPLINGQuotas are setup according to certain characteristics

SNOWBALL SAMPLINGWe initially contact with potential respondents and ask them to refer respondents of similar characteristics

DATA COLLECTION METHODS

FOCUS GROUPSINTERVIEWS

SURVEYQUESTIONNAIRE

FOCUS GROUPS

• A focus group is a form of qualitative research

• Respondents from the target population are typically put in a single group and are asked about their perceptions, opinions, beliefs, and attitudes towards a product, service, concept, advertisement, idea, or packaging

• Questions are asked in an interactive group setting where participants are free to talk with other group members

FOCUS GROUPS

• Two-way focus group - one focus group watches another focus group and discusses the observed interactions and conclusion

• Dual moderator focus group - one moderator ensures the session progresses smoothly, while another ensures that all the topics are covered

• Mini focus groups - groups are composed of four or five members rather than 6 to 12

• Teleconference focus groups - telephone network is used

• Online focus groups - computers connected via the internet are used

INTERVIEWS

• A method for collecting primary data• The person who is interviewing is called interviewer

• Person who is giving interview is called interviewee or respondent

• Respondent is asked to provide information in form of opinions, facts and attitude etc.

• E.g.: Personal interview, mail interview, telephone interview

SURVEYS• A non-experimental, descriptive research method• Helps in collecting data on phenomena that cant be directly observed

such as opinions

Types

• Industrial and consumer survey –for industrial use• Media studies- to know effectiveness of advertising etc.• Multiple survey- Survey is conducted among several groups of people

QUESTIONNAIRES

• It is a method for collecting 10 data in which a sample of respondents are asked a list of carefully structured questions chosen after considerable testing, with a view of eliciting reliable responses

Objectives

• It can translate the information needed into specific questions• It must motivate the respondent to become involved in completing it

OPEN AND CLOSED QUESTIONS

Open questions

• An open question is likely to receive a long answer• They offer the advantage of giving the opinions as precisely as

possible in our own words

• Eg: Do you support hike in oil prices, why?

OPEN AND CLOSED QUESTIONSClosed questions

• A closed question can be answered with either a single word or a short phrase.

• They are convenient and easy to answer, since range of potential answers is limited

• Eg: Do you support hike in oil prices ?• Tick your opinion• 1. Yes• 2. No

MULTIPLE CHOICE QUESTIONS• The participant is asked a closed question and select his/her anser

from a list of predetermined responses

• E.g.:Who is the reserve bank governor?Tick your opinion• 1.Reghu ram Rajan• 2. D. Subbarao• 3. Sen Gupta• 4. L.K.Jha• 5. Dr.C. Rangarajan

RATING SCALES• These scales are used to tap preferences between two or among more

objects or items• Rating scales are used quite frequently in research, especially in

surveys• Typically, an itemized rating scale asks subjects to choose one response

category from several arranged in hierarchical order.Eg: Please rate our service (out of 10)• > 4 - Poor• 5 to 7 - Average• 8 to 9 - Good• 10 - Outstanding

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