Economic Research Methods (FULL)

8/2/2019 Economic Research Methods (FULL)

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Economic Research Methods

IFS surveyimp for data

Research Strategies

Introduction

Two main questions

- How is research in this area conducted?- What does the research process involve?

Once research is completedthis should show that the individual can systematically handle

and analyse a problem arriving at valid conclusions

Research is this a professional training process through which we can learn to think and work

systematically

Why do we research?

- To learn to work systematically- To learn to critically analyse issues/matters before believing in them or acting on

them.

Theory and Research

We characterise the link between theory and research: what form of theory one is talkingabout and is the data collected to test or to build theories

What type of theory -

This term is used in a variety of ways but most common meaning is as an explanation of

observed regularitiesdistinguish between grand theory and middle range theories.

The term theory is in most cases used to refer to the background literature in an area of

social enquiry. In most cases the relevant background literature relating to a topic fuel the

focus of an article and thereby acts as the equivalent of theory.

In most cases it is the literature that informs the researcher of research questions in relation to

what the authors perceive to be neglected topic. Background literature influences the focus of

research

- The researcher might spot a neglected aspect of a topic- Certain ideas may not preciously have been tested- The researcher might think that existing approaches being used for research on a topic

are deficient.

Social scientists are sometimes prone to being somewhat dismissive of research that has no

obvious connection with theoryEmpiricisma general approach to the study of reality that

suggests that only knowledge gained through experience and the senses is acceptable. Thus

ideas must be subjected to the rigours of testing before they can be considered knowledge.


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The definitions of Empiricism raises consideration to another question: in so far as any pice

of research is linked to theory. What was the role of that theory?

We thus distinguish between deduction and induction.

Deduction theory

Deduction theory is the commonest view of the nature of the relationship between theory and

research

The researcher on the basis of what is known about a particular domain and of theoretical

considerations in relation to that domain deduces a hypothesis that must then be subjected to

empirical scrutiny

By deduction we mean that we draw conclusions through logical reasoning

The research builds or deduces hypothesis from existing literature which can be subject to

empirical scrutiny and thus can be accepted or rejected.

In this type of research theory and hypothesis built on it, come first and influence the rest of

the research process.

The process of deduction

Theory

|

Hypothesis

|

Data collection

|

Findings

|

Hypotheses confirmed or rejected

|

Revision of theory

Induction theory

Through induction we draw general conclusions from our empirical observations

The process foes from observation to findings to theory building, as findings are incorporated

back into existing knowledge to improve theories.

It is important to note that we can never be 100 per cent sure about the inductive conclusions

obtained as these conclusions are based on some empirical observations. Sometimes

conclusions based on hundreds of observations can go wrong.

Theory is thus the outcome of research as something that emerges out of it (mostly associated

to qualitative research)

The process of induction

Compare


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Develop theory

Look for patterns

Form categories

Ask questions

Gather information

Deduction and induction

The difference between induction and deductionfacts acquired through observations lead

us to theories and hypotheses, while with deduction we accept or reject these theories and

hypotheses. This acceptance and rejection the helps us to explain or predict.

In the research process, methods begin with ideas and facts that lead us to propositions,

theories and predictions. When we utilise observed facts in generation a theory that is

consistent with these facts we are doing induction.

What comes first theory or data?

It is often assumed that theory should precede data that is observations. This impression issupported by the way in which we will illustrate the research process and research often

takes place this way.

Example: after carefully reviewing the relevant literature, the researcher sees a research

opportunity a gap. A weakness or unanswered question in present insights. The researcher

will thus have a clear research problem, do variations in X explain variations in Y. Or else,

the researcher observes something which he does not understand.. Why? Why does this

happen? Thus one seeks to come up with an adequate explanation.

When doing research interactions between data and theory take place constantly.

Quantitative or Qualitative

It is helpful to distinguish between quantitative and qualitative research. This is a useful

means of classifying different methods of research

Quantitative research employs measurement and qualitative research does not.

Many writers have suggested that the difference foes deeper

Quantitative research can be seen as a research strategy that emphasis quantification in the

collection and analysis of data...

Qualitative research can be seen as a method which emphasises words and not quantification.


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Influences on the conduct of research

Research is influenced by a number of factors.

VALUES

- Can reflect the beliefs or feelings of a researcher- Can produce bias at any or all points in the social research process e.g.

o Choice of research area and methodso Formulation of research question research design and data collection

techniques

o Implementation of data collectiono Analysis and interpretation of datao Conclusions

- Can produce affinity or sympathy especially to underdog groups- Can be antithetical to values of many managers.

Practical Considerations

May influence or determine choices on

- Research strategy- Design- Method- Resources and costs

May be influenced or determined by:

- Nature of the topic- People being investigated- Political acceptability

Research and Ethics

We do research because we want to know more about ourselves and the world around us.

Ethics are moral principles and values that influence the way a researcher or a group of

researcher conduct their research activities.

Ethics apply to all situations and activities in which there can be actual or potential harm or

any kind to anybody.

Researcher has responsibility to explain and fins answers to questions honestly and

accurately. They have to point out strengths of methods and models used and

weaknesses/reliability of their results.

Ethical responsibility starts with problem formation.


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Moreover you can find yourself in a moral dilemma if the study which you undertake shows

drastic results. We have to ensure that research did not cause embarrassment or any other

disadvantage to people who have provided you with the data.

Lesson 2

- [email protected] to be given.

Lesson 3

Same

- Literature review

Lesson 4 - Data Sources

Introduction

What do we mean by data collection?

What are the sources of data collection?

Where do we find the right data?

Data sources are the carriers of data (information)

A distinction is made between primary and secondary data.

Primary data is data collected by us for the research problem at hand.

Secondary data is information collected by others for purposes that can be different from

ours.

Se co ndar y da ta

The natural inclination of researchers in seeking to answer their questions is to gather new

data on the topic at hand.

This may seem appropriate, though one should think whether data is already available to

answer the research question.

Searching for existing data may save time, effort, expenses. The high cost of collecting data

makes it important to see if data already collected is available.

This should be done whether the project lends itself to quantitative/qualitative approach.

We will thus define secondary data, describe the sources and type of secondary data and

comment on the advantages/disadvantages and the quality of secondary data.

Definition

- Secondary data is data used for research that was not gathered directly andpurposefully for the project under consideration.
mailto:[email protected]:[email protected]:[email protected]


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Sources and types

- Economists have been the leading users of secondary data within the broader field ofbusiness research. Statistics gathered by stock markets, bond markets, government

agencies, international agencies, IMF, World Bank, national statistics offices are used

extensively.

- Secondary data has also been used extensively in research studies ranging fromeducation/healthcare/corporate governance/ethics/social responsibilityFrom the demand side, popularity has been driven by an expanded interest in large

comparative studies undertaken nationally/internationally.

From the supply side, technology has enhanced the capacity of data providers to provide data

on commercial basis.

A useful way to categories secondary data is by source, format and type.

Source: internal/external

- Whilst in most cases data is obtained from third-party or external sources, howeverthey can also come from within a companyan internal source.

- When obtained from outside the companyexternal sourceorganisations orindividuals that are the primary fathers o the data. Data is obtained either from an

external single source or from multiple external sources.

- External sources: legal/government/industry/EU/non-government organisations.Format: needs processing/ready to use.

- In most cases secondary data is not available in a format that is ready for immediateuse.

- Increasingly today, most sources are making their data available electronically via theinternet or though CD/DVD.

Type: Ad hoc / time series / cross sectional

- Ad hocdata can be sourced from investigations undertaken for a specific purposewith no initial intention to expand the research over time or across markets.

- Alternatively data can come from a source/s that repeatedly collects data as part oflongitudinal studies or to compare variables across countries. E.g. EU database

Eurostatdata is collected according to a regular schedule and across time e.g. GDP,

interest rates, etc.

- Data can also be collected for specialised one off reports.Locating secondary data- The list of potential sources of secondary data is virtually endless.

- Today most sources are increasingly accessible and searchable- The library has access to several search engines that can identify relevant research

articles/data

- Exampleo Worldbanksocial issues usually commone.g. agingo Financial timeso Business weeko EUo Oecdo IMFo Eurostat


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o National statistics agenciesAdvantages Secondary data:

- Cost savings: time and money savings. The time and money needed to design andexecute a field study can be considerable.

- Evaluating secondary sources: secondary data sources can be evaluated before beingintegrate into a research project. Most of the data collected by internationalorganisations/governments are of high quality and reliable as they are collected and

compiled by experts using rigorous methods. The reputation of the research entity

itself can be relied on as an indication of data quality e.g. world bank, Eurostat.

- Conducting comparative analysis: this type of analysis might be considered crucial fora particular research. Achieved either through longitudinal analysis or cross-sectional

analysis.

Secondary data can be used to suggest suitable methods or data to handle a particular

research problem. Moreover, secondary data provides a comparison instrument with which

we can easily interpret and understand our primary data.

Such an approach, of using secondary data to supplement direct survey research and to

collaborate its findings is referred to as triangulation. Confirms primary data via secondary

data.

Secondary data avoids respondents fatigue. Using secondary sources we reduce the scale

and frequency of questionnaire usage without diminishing the quality of the collected data.

Disadvantages of Secondary data

- Misalignment of purposeo The data was probably collected for another purpose other than your own.

- Access complicationso Cost complication, lack of familiarities with parties providing the data, the

way the data are reported or presented.

- Lack of familiarity with the initial motivation and processes followed when gatheringthe data

- Potential quality concerns with secondary data relate to:o Sourceo Definition of terms and constructso Nature of collection methods.

All of the above can have an impact on the overall reliability and validity of your research.

Evaluating the quality of Secondary data:

- Evaluation of secondary data is important so that advantages can be captured and theirdisadvantage controlled

- Start with an investigation of the original provider of the data. Reputation andexpertise are crucial

- Evaluate the research design: data is always collected with one or more objectives inmind, appreaceating this is important in establishing whether the data may be

appropriate for an alternative application.

- Evaluate data collection methods. Examine the sample faram, the response rate andmeasurement techniques.


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Ethical issues when using secondary data:

Inappropriately using data sources when you should not and inappropriately not using them

when you should.

Attempting to use data when the specificity of the research question requires that primary

data are obtained.

Insisting on using the primary data collection methods when appropriate secondary data areinexpensive or perhaps at no charge

Using secondary data fathered under guarantees of anonymity in a manner that may

undermine that initial promise.

Using secondary data that have been collected using questionable methods.

Primary data

When secondary data are not available or are unable to help answers our research questions,

we must ourselves collect the data that are relevant to out particular study/research.

What we should look for, ask about and collect depends upon our research problem andresearch design.

We have several choices as regards means of collecting primary data: observations,

experiments, surveys (questionnaires) and interviews.

Advantages/disadvantages of primary data

- The main advantage is that primary data is collected for the particular project andneed.

- The main disadvantage is that the process usually takes a long time and will be quitecostly. It can also be difficult to get information from some companies for example.

Also people will be reluctant to answer some sensitive question for example about

income.

- The researcher has to be very careful in using the proper tools as this can jeopardisethe reliability/applicability of the study

- Researcher is fully dependant on the willingness and ability of respondents.


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Lesson 5

SPSStoday PASW

Sampling Approaches

Introduction

When the research problem is specified and an appropriate research design and datacollection instrument developed, the next step in the research process is to select those

elements from which the information will be collected.

One possibility would be to collect the data from all members of the population. This is acensus. In most cases it is not feasible.

Thus a sample of the population is drawn. On the basis of this sample we then infersomething about the larger groups.

Samples are drawn for a number of reasons in particular costs and time constraints. Very careful consideration should be given to sampling design issues in selecting the sample.

Of prime importance is the sample size and that those selected should be representative of

the whole group.

Samples are drawn either using probability or non probability procedures. Probability sampling is typically used in quantitative research. This involves a selection of a

representative sample from the population using a random procedure to ensure objectivity

in selecting the sample. The findings from the sample data can then be generalised to the

population with a specified degree of accuracy.

Non-probability sampling is typically used in qualitative research. Judgement is used toselect a sample in qualitative research. Findings can be used to describe, discover and

develop theory. Whilst findings can be used to generalise to the population, this cannot be

done with a specified degree of accuracy.

Sampling design is part of the basic business research process. The sampling design processinvolves answering the following questions: should a sample or a census be used, if asample, then which sampling approach should be used and how large a sample is necessary?

The main aim in answering these questions is to minimise as much as possible the error thatmight occur due to sampling.

Representative samples (samples which mirror the characteristics of the population) aregenerally obtained by following a set of well-defined procedures. These include:

1. Defining the target population2. Choosing the sampling frame3. Selecting the sampling method4. Determining the sample rate


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5. Implementing the sampling plan.

Defining the target population

The target population is the complete group of objects or elements relevant to the researchproject. They are relevant because they possess the information the research project isdesigned to collect

Other practical factors may influence the definition of the target population. These include:knowledge of the topic of interest, access to elements (individuals, companies) availability of

elements and time frame.

The element or objects available for selection during the sampling process are known as thesampling unit. The sampling unit can be people, households, businesses or any logical unit

relevant to the studys objective.

When the sampling plan is executed, sampling units are drawn from the target population touse in making estimated of population characteristics.

Choosing the sampling frame

The sampling frame provides a working definition of the target population. It is a completelist of all the elements in the population from which the sample is drawn.

The sample frame is a comprehensive list of the elements from which the sample is drawn.Examples: yellow pages listing of restaurants, telephone directory for individuals, company

internal database listing employees, university registration lists.

Problems associated with such lists: not up to date, may include elements that do not belongto the target population, it may not include multiple/duplicate elements and may not

include elements that belong to the target population.

Such issues must be checked prior to drawing the sample.

Selecting the sampling method

Selection of sampling method to use in a study depends on a number of related theoreticaland practical issues: the nature of the study, the objectives of the study, the time and

budget available.

Sampling methods are usually divided into two broad categories: probability and non-probability sampling.

Probability methods are based on the premise that each element of the target populationhas a known, not necessarily equal, probability of being selected in a sample. In probability

sampling, sampling elements are selected randomly and the probability sampling ensures

that the sampling is representative.


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In non probability sampling the inclusion or exclusion of elements in a sample is left to thediscretion of the researcher. Thus not every element of the target population has a chance

of being selected into the sample. Despite this, a skilful selection process should result in a

reasonable representative sample.

The most common types of sampling methods: Probability

- Simple random- Systematic- Stratified- Cluster- Multi-Stage

Non-Probability- Convenience- Judgement- Snowball- Referral- Quota

Probability sampling

Selection is based on some random procedure that gives elements a known and nonzerochance of being selected thereby minimising selection bias.

Findings based on a probability sample can be generalised to the target population with aspecified level of confidence.

Simple random sampling: each element of the target population has an equal opportunity ofbeing selected. E.g. drawing names from a hat or drawing winning ticket in raffle, random

digit dialling with telephone survey.

Systematic sampling: this involves selecting every nth unit after a random start. In universityexample, if you want 500 out of the 10000, then sampling interval is 20. To draw the sample

you select randomly a number between 1 and 20 e.g. 7 as a starting point and thus you

select 7, 27, 47, 67, etc. For this to work your population has to be ordered in some way.

Stratified sampling: requires the researcher to partition the sampling frame into relativelyhomogenous subgroups that are distinct and non-overlapping, called strata. A simple

random sample of units is then chosen independently from each subset.

When done properly, stratified sampling increases the accuracy of the sample information. Ithelps to reduce variability and thus reduce the standard error of the estimates.

A stratified sample is selected in one of these two ways: proportional stratified sampling ordisproportionally stratified sampling.

In proportional stratified sampling, the number of elements chosen from each strata isproportional to the size of a particular strata relative to the overall sample size.


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In disproportionally stratified sampling, the sample elements are chosen in one of the twoways, either choosing the elements from each stratum according to its relative importance

or by considering the variability of the data within each stratum.

Age Group Proportional Disproportional18 25 600 20% 40 50 25%

26 34 900 30% 60 50 25%

35 49 270 9% 18 50 25%

50 59 1020 34% 68 30 15%

60 + 210 7% 4 20 10%

Total 3000 100% 200 200 100%

Cluster Sampling: the target population is viewed as made up of heterogeneous groupscalled clusters. Examples of clusters are: ethnic groups, companies, households, business

units. The most frequently used type of cluster sampling is geographic area.

Multi-stage sampling: this involves a sequence of stages. E.g. we want the view of medicalpractitioners in the UK with regards to a particular drug. First select a random sample of

regions in the UK. The regions would be the clusters. The second stage would be to select a

random sample of hospital from the selected regions, and then either collect information

from all medical practitioners from the chosen hospital or a random hospital from within

each of the chosen hospital.

Non-Probability Sampling

Easy to draw, but may give mis-leading results (unrepresentative). The selection of the sample elements are not necessarily made with the aim of being

statistically representative of the population.

The researcher uses subjective methods such as personal experience, convenience, andexpert judgement.

The probability of any element of the population being chosen is not known and there willbe no statistical method for measuring the sampling error.

The researcher cannot thus generalise the findings to the target population with anymeasured degree of confidence, which is possible with probability samples.

The most common non-probability sampling methods are: Convenience Sampling: selecting sample respondents that are most readily available

to participate in the study and who can provide the information required. Used mostly

because they enable the researcher to complete a large number of interviews quickly

and cost effectively. Often we have problems of selection bias. It is thus difficult and

dangerous to generalise to the target population when such sampling method is used.


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Judgement sampling: referred to also as purposive sample. This involves selectingelements in the sample for a specific purpose. It is a form of convenience sample in

which the researchers judgement is used to select the sample elements. Elements are

chosen because the researcher believes that they represent the target population.

Advantages ofconvenience, speed and low cost.

Quota Sampling: very similar to stratified random sampling. The objective is for thetotal sample to have proportional representation of the strata of the target

population. The selection is though done on a convenience basis. The researcher

defines the strata of the target population, determines the total sample size, set quota

for the sample elements for each stratum. The researcher also specifies the

characteristics of the elements to be selected but the choice of elements is left to the

discretion of the person collecting the information.

While this ensures that proportionate representation of each stratum is obtained, thefinding cannot be generalised because the choice of elements is not done using a

probability sampling method.

Snowball Sampling: also called referral sample. The initial respondents are chosenusing probability methods. The researcher then uses the initial respondents to help

identify the other respondents in the target population. This continues until the

required sample size is reached.

Determining sample size

Why is the sample size needed? It depends on the desired precision from the estimate. In determining the sample size, a number of factors have to be taken into account:

variability of elements in the target population, the type of sample required, time available,

budget, required estimation precision, are the findings to be generalise, the degree of

confidence.

Formulas based on statistical theory can be used to compute the sample size. Irrespective of how the sample size is determined, it is essential that it is of sufficient size

and quality to yield results that are seen to be credible in terms of their accuracy and

consistency.

Read extra notes given.Implementing the sampling plan

The researcher implements the sampling plan after all details of the sampling design havebeen agreed upon.

Once target population has been defined, the sampling frame has been chosen, thesampling method has been selected, and the appropriate sample size determined then the

plan can be implemented.


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All details must be clear before a final sample plan is accepted and implemented becauseonce the data is collected; it is too late to change the sampling design.

10/12/2010

Borg

07/01/2011

Measurement and Scaling

- Measurement is of crucial importance in research work- We must carefully measure the concepts we are examining. Otherwise our

interpretations and conclusions will not be accurate. We are thus concerned with howto measure and whether our measures are valid and reliable.

- What is a concept?it is the mental abstraction or idea formed by the perception ofsome phenomena. The idea is a combination of a number of similar characteristics of

the concept. The characteristics are the variables that collectively define the concept

and make measurement of the concept possible

- Conceptthe following is a list of variables listed to measure the concept ofcustomer interaction the customer was easy to talk, the customer likes to talk to

people, the customer was interested in socialising, the customer was friendly, the

customer tried to establish a personal relationship. By obtaining scores on each of the

variables you can indirectly measure the overall concept of customer interaction. All

individual scores are then combined into a single score according to a predefined setof rules. The resulting score is often referred to as a scale, an index or a summated

rating scale. E.g. 5 point scale, 5 = strongly agree 1= strongly disagree.

- A scale or index or a summated rating scale is often used to measure the concept- To understand a concept we must be able to measure it without measurement we

cannot comment on behaviour or phenomena.

- Other examples of concepts: job satisfaction, job commitment, motivation, servicequality, age and income. For age and income there are usually no problems because

there is agreement on how to measure. But for the others there is unlikely to be a

common interpretation of their meaning and they can be only measured indirectly.

- The measurement process involves specifying the variables that serve as proxies forthe concepts. (also referred to as indicators).

- Concepts that are relatively concrete in nature, such as gender, age, height, householdincome, food prices are relatively easy to define and thus can be measure objectively

and in a fairly precise manner. This done through observation, questioning or use of a

calibrated instrument like a ruler

- Concept that are complec and abstract in nature, such as wealth, satisfaction, arerelatively difficult to measure and researchers use a number of subjective measure to

describe such concepts


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Measuring concepts- Measurement involves assigning numbers to a variable accorfing to certain rules. The

assigned numbers must reflect the characteristics of the phenomenon being measured

- There are four levels of measurement. The levels determine the sophistication of themeasurement employed.

- Measurement is achieved through a use of scales. A scale is a measurement tool thatcan be used to measure a question with a predetermined number of outcomes.

Outcome can be directional (yes/no, agree/disagree) or categorical (labelsnumber of

distinct outcomes can be more than two. E.g. question on industry type might include

more than two categories.

- A scale can also be continuous. It not only measures direction/classification but alsointensity. E.g. the time to complete a task, age of an investment project. A continuous

scale is used to measure intensity of agreement. E.g. strongly agree and somewhat

agree.

- The four levels of measurement are represented by scales:o Nominalo Ordinaryo Intervalo Ratio

- Variables measured at the nominal or ordinary level are discrete and referred to aseither categorical or non metric

- Variables measured at the inter cal or ration are continuous and referred to as eitherquantitative or metric

Nominal Scale- Uses numbers as labels to identify and classify objects, individuals or events. Eah

number is given to only one object (individual), numbers thus serve as a lable for

identification

- Nominal scales are the lowest level of measurement. Data analysis is restricted tocounts of the number of respondents in each category, calculation of the mode or

percentage for a particular question

o E.g. are you happy with the service in the restaurant? Yes/noo E.g. measuring occupation or ownership type

- A requirement on a nominal scale is that categories are mutually exclusive andexhaustive of all possibilities

Ordinary Scale:- An ordinary scale is a ranking scale. It places objects into a predetermined category

that is rank ordered according to some criterion such as preference, age, income

group, importance etc.

- This scale enables the researcher to determine if an object has more or less of acharacteristic than some other object bt not how much more less of the characteristic

- The points on an ordinary scale do not indicate equal distance between rankings- A high level of analysis can be carried out. We can calculate the median as well as

percentages. Can also use the Spearman rank order correlation statistic.


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Example: Regarding your visits to restaurant in the last month, please rank the following

attributes from 1 to 4 (4 being the most important) reason for selecting the restaurant, 1 the

least important

- Food quality- Atmosphere- Prices- Employees

Consider that the results show that 40% of the respondents assigned a 4 to food quality, 30%

a 4 to atmosphere, 20% to process and 10% to employees. This shows that relatively making

food quality is the most important reason for choosing the restaurant.

The difference between a ranking of 4 and 3 is not necessary the as between 1 and 2. But we

know 4 is better than 3.

Interval Scale- This method uses numbers to rate objects or events so that the distances between the

numbers are equal. Differences between points on the scale can the interpreted and

compared meaningfully. The difference between 3 and t is the same as between 1 and

- The interval scale has all the qualities of nominal and ordinary scales, plus thedifferences between the scale points is considered to be equal. Therefore in addition

you can compare the differences between objects.

- Interval scales are mostly used to measure concepts of attitudes, perceptions, feelings,opinions and values through the use of RATING SCALES.

- Rating scales involve the use of statements on a questionnaire accompanying a pre-coded category which are selected to indicate the extent of agreement/disagreementwith a given statement.

- Can carry out calculations like previous to scales and also the mean, standarddeviation, Pearsons product moment correlation coefficient.

Example: Restaurant A is a fun place to go: 1 to 5 from strongly disagree to Strongly Agree.

The location f the zero point is not fixed. If individual answers 1 and another answer 2 we

can say that second is one unit away from 1 but cannot conclude that the rating point is twice

the intensity of rating point 1 in terms of strength of agreement.

The researcher arbitrarily chooses the origin or anchor point on the scale.

Ratio Scale- This provides the highest level of measurement- A distinguishing characteristic of a ratio scale is that it possesses a unique origin or

zero point, which makes it possible to compute ratios of points on the scale

- Ratio scales possess all the properties of the other scales plus an absolute zero point.We can compute the coefficient of variation as well as the standard deviation and

product-moment correlation

- E.g. Bathroom scale or weighting machines are examples of ratio scales because theyhave absolute zero points


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- E..g. How many children are there in your household? A response of 0 can only beinterpreted in one way. If one answer 2 and one answers 4 then we can state that the

first household has fewer children than the second by two children. The second has

twice the children as the first

Frequently used measurement scales- There are two types of scales: metric (quantitative) and nonmetric (qualitative)- Nominal and ordinary are non metric- Interval and ratios are metric- Metric - summated ratings (likert), numerical scales, semantic differential and graphic

ratings.

- Non-metriccategorical, rank order, sorting and constant sumMetric Scales

Summated Ratings Scale:- Attempts to measure attitudes or opinions- Summated scales often use a five/seven point scale to assess the strength of agreement

about a group of statements. For each point on the scale you develop a label to

express the intensity of the respondents feelings. When you sum the scales for all the

statements it is referred to as a summated rating scale. When you use the scale

individually it is referred to as a Likert scale. ( the more points you use the more

precision with regard to...

Numerical scales

- Numerical scales have numbers as response options rather than verbal descriptions.The numbers corresponds with categories (response options). Used to assess level ofagreement/disagreement. Used to measure behavioural intentions

Semantic Differential Scale- Used to measure attitudes. Use bipolar end points with the intermediate points

typically numbered. The end points are chosen to describe individuals, objects or

events with opposite adjectives or adverbs

Graphic ratings Scale:- This is a scale that provides measurement on a continuum in a form of a line with

anchors that are numbered and named.

Non Metric Scales- Also referred to as comparative scales- A distinguishing feature is that responses to the questions are evaluated relative to

each other rather than independently


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Categorical Scale- Are nominally measured opinion scales that have two or more response categories.

Often used to measure respondent characteristics such as gender, age, education level,

product type, industry sector.

Rank order Scale- Individuals often place items or alternatives in a rank order. A rank order scale is and

ordinary scale that asks respondents to rank a set or objects or characteristics in terms

of preference, similarity, importance or similar objectives.

Sorting Scale- Sorting scales ask respondents to indicate their beliefs or opinions by arranging

objects (items) on the basis of preceded similarity or some other attribute

Constant Sum Scale- The respondents are asked to divide a constant sum over several categories to

indicate, for example, the relative importance of attributes

Practical decision when developing scales- Number of scale categoriesthe larger the number of categories the greater the

precision of the measurement scale. With a lot of scales it is more difficult for

respondents to distinguish between the levels.

- Odd or even number of categories: use odd when the mid-point is important, when itis believed that some portion of the sample is likely to eel neutral about the issue

being examined. If researcher feels or does not want nay neutral responses then fog oreven number of categories

- Use of balanced or unbalanced scales: The numbers of favourable and unfavourablecategories are equal. Unbalanced scales used if researcher expects skewed results.

This can though create bias.

- Category labels for scales: verbal labels, numerical labels and unlabelled choices.

NOTE

- reference programso refworks (library)o Endnoteo Reference Manager

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Economic Research Methods (FULL)