134621811 Advance Research Methods

1 Muhammad Azeem Rafi NUML Lahore

Advance Research Methods

ADVANCED RESEARCH METHODOLOGY

Overview of Research and its Methodologies

• Concepts of research

• The need for research

• Types of research

• Steps in conducting research

Definition of Research

• Hunting for facts or truth about a subject.

• Organized scientific investigation to solve problems, test hypotheses, develop or invent new products.

What is Research? Research is systematic, because it follows certain steps that are logical in order. These

steps are:

• Understanding the nature of problem to be studied and identifying the related area of knowledge.

• Reviewing literature to understand how others have approached or dealt with the problem.

• Collecting data in an organized and controlled manner so as to arrive at valid decisions.

• Analyzing data appropriate to the problem.

• Drawing conclusions and making generalizations.

High Quality Research!

• It is based on the work of others.

• It can be replicated (duplicated).

• It is generalize able to other settings.

• It is based on some logical rationale and tied to theory.

• It is doable!

• It generates new questions or is cyclical in nature.

• It is incremental.

• It is apolitical activity that should be undertaken for the betterment of society.

What is bad research?

The opposites of what have been discussed.

• Looking for something when it simply is not to be found.

• Plagiarizing other people’s work.

• Falsifying data to prove a point.

• Misrepresenting information and misleading participants.

The general systematic characteristic of research is illustrated below.

Problem Identification

Reviewing Information

Data Collection

Analysis

Drawing Conclusion



• Research follows a scientific method. This means that it makes an integrated use of inductive and

deductive reasoning.

• This makes it very useful for explaining and/or predicting phenomena.

• The basic assumption of the scientific method is that every effect has a cause.

• It starts with the construction of hypotheses from casual observations and background knowledge

(inductive reasoning) to reasoning out consequences or implications of hypotheses (deductive reasoning)

followed by testing of the implications and confirmation or rejection of the hypotheses.

• Integrated use of inductive and deductive reasoning is, therefore, the essence of scientific method.

Why do we need research?

• To get PhDs, Masters and Bachelors?

• To provide solutions to complex problems

• To investigate laws of nature

• To make new discoveries

• To develop new products

• To save costs

• To improve our life

• Human desires

Classifying Research. Reviewing related past research studies is an important step in the process of carrying

out research as it helps in problem formulation, hypothesis construction and selection of appropriate research

designs. It is beneficial if you can classify a research study under a specific category because each category or

type of research uses a specific set of procedures.

There are two ways of classifying research.

• One way is to classify research on the basis of its purpose i.e. the degree to which the research findings are

applicable to an educational setting and the degree to which they are generalizable.

• The other is to classify research on the basis of the method employed in research.

Research can be classified into two types

Purpose Method



Classifying Research by Purpose

Classifying Research by Methods. The other basis for classifying research, is by the method it employs.

• Research method is characterized by the techniques employed in collecting and analyzing data.

• On the basis of method, research can be classified as historical, descriptive, correlational, ex-post facto and

experimental.

Historical Research. The purpose of historical research is to arrive at conclusions concerning trends, causes

or effects of past occurrences.

• This may help in explaining present events and anticipating future events.

• The data are not gathered by administering instruments to individuals ,but …

Rather, they are collected from original documents or by interviewing the eye-witnesses (primary source of

information).

• In case primary sources are not available, data are collected from those other than eye-witnesses

(secondary sources).

• The data thus collected are subjected to scientific analysis to assess its authenticity and accuracy.

Descriptive Research. Descriptive research studies deal with collecting data and testing hypotheses or

answering questions concerning the current status of the subject of study.

• It deals with the question “WHAT IS” of a situation.

• It concerns with determining the current practices, status or features of situations.

• Another aspect of descriptive research is that data collection is either done through asking questions from

individuals in the situation (through questionnaires or interviews) or by observation.

Correlational Research. Descriptive and historical research provides a picture of events that are

currently happening or have occurred in the past.

• Researchers often want to go beyond mere description and begin discussing the relationship that certain

events might have to one another.

• The most likely type of research to answer the relationship among variables or events is called

correlational research.

• A correlation study aims at determining the degree of relationship between two or more quantifiable

variables.

• Secondly, the relationship thus determined could be used for making predictions.

• A high value of relationship, however, does not signify a cause and effect relationship which must be

verified through an experimental study.

Type of Research

Basic Applied / Developmental

Type of Research

Historical Descriptive Co relational Ex Post Facto Experimental



• Correlational researches are studies that are often conducted to test the reliability and predictive validity

of instruments.

• Used for division making concerning selection of individuals for the likely success in a course of study or a

specific job.

• Some authors consider this research as a type of descriptive research, since it describes the current

conditions in a situation.

• However, the difference lies in the nature of conditions studies.

• A correlational study describes in quantitative terms the degree to which the variables are related.

Ex-Post Facto Studies. There is some research where both the effect and the alleged cause have already

occurred and are studied by the researcher in retrospect.

• Such research is referred to as Ex-Post Facto (after the fact).

• Systematic empirical inquiry in which the scientist does not have direct control of independent variables

because their manifestations have already occurred or because they are inherently not manipulable”.

• In ex-post facto or causal-comparative research the researcher has no control on the variables or he

cannot manipulate the variables (independent variables) which cause a certain effect (dependent

variables) being measured.

Experimental Research. Correlational research can help establish the presence of a relationship among

variables but not give us any reason to believe that variables are causally related to one another.

• How does one find out if the characteristics or behaviors or events are related in such a way that the

relationship is a causal one?

• Two types of research can answer this:-

• Experimental Research is where participants are assigned to groups based on some selected criterion

often called treatment variable.

• Quasi-Experimental Research is where participants are pre-assigned to groups based on some

characteristic or quality such as differences in sex, race, age, neighborhood, etc.

• These group assignments have already taken place before the experiment begins, and the researcher has

no control as to what the people will belong to each group

• The primary characteristic of experimental research is manipulation of at least one variables and control

over the other relevant variables so as to measure its effect on one or more dependent variables.

• The variable (s) which is manipulated is also called independent variables, a treatment, an experimental

variables or the cause.

• Some of the examples of independent variables could be: temperature, pressure, chemical concentration,

type of material and conductivity.

Applied vs. Basic Research

The most basic distinction between the two research is that basic research is research that has no immediate

application, whereas applied research is research that does.

• However, such distinctions are somewhat ambiguous as almost all basic research eventually results in some

worthwhile application in the long range.

Steps in Conducting Research. Irrespective of the type of a research study, the steps followed in

conducting it are the same. These are:-



1 Selecting and Defining a Problem. This marks the beginning of a research study and is

the most difficult and important step. This involves:

a. Identifying and stating the problem in specific terms;

b. Identifying the variables in the problem situation and defining them adequately;

c. Generating tentative guesses (hypotheses) about the relation of the variables or in

other words the solution of the problem, or writing explicitly the questions

(research questions) for which answers are sought; and

d. Evaluating the problem for its research ability.

All this is not done in a vacuum. To achieve this, you review the literature related to the

problem to know what other researchers have done and discovered and to identify the

possible methodology for conducting the research.

2 Describing Methodology of Research. You need to state the purpose of the study

and to define the problem clearly. This guides you in deciding the methodology of

research which involves :

a. Identifying the method of research;

b. Specifying the subjects of study (e.g. heat flow problem, etc.);

c. Selecting an adequate representative sample of subjects;

d. Selecting/constructing valid and reliable instruments for measuring the variables in the problem;

e. Selecting a research design and describing the procedure to be employed for conducting the research

study.

3 Data Collection

4 Analysis and Interpretation

CHAPTER 4 – THE MEANING AND METHODOLOGY

The three Approaches

Positivist Social Science

It is defined as the approach of the natural sciences.

Most people never even heard of alternative approaches.

Use of this approach has declined sharply in European Journals, but it is still very dominant in North

American Journals.

Positivism says that “there is only one logic of science”.

Social Sciences and natural Sciences should use the same method.

Positivism sees social science as an organized method for combining deductive logic with precise empirical

observations of individual behavior in order to discover and confirm a set of probabilistic causal laws that

can be used to predict general patterns of human activity.

Positivists believe hold that social and physical reality is real. It exists “out there” and is waiting to be

discovered.

The reality is outside and objective.

We can learn about people by observing their behaviour, what we see in external reality. It is more

important than what happens in internal, subjective reality.

Human events can be explained with reference to causal laws.

Positivists see a clear separation between science and non science.



Scientific knowledge is better and will eventually replace the inferior ways of gaining knowledge (e.g.,

magic, religion, astrology, personal experience, and tradition).

People can recognized truth and distinguish it from falsehood by applying reason.

In positivism, explanations must meet four conditions: they must:

Have no logical contradictions.

Be consistent with observed facts.

Replication is also needed.

The findings should be generalizable (generalizability is very important condition).

Positivism is dualist:

It assumes that the cold, observable facts are fundamentally distinct from ideas, values, or theories.

Empirical facts exist apart from personal ideas or thoughts.

Interpretive Social Science

It is a systematic analysis of socially meaningful action through the direct detailed observation of people in

natural settings in order to arrive at understanding and interpretations of how people create and maintain

their social worlds.

It is also called qualitative method of research.

Interpretive social science is related to hermeneutics, a theory of meaning that originated in the

nineteenth century.

Hurmeneutics emphasizes a detailed reading or examination of text, which could refer to conversation,

written words, or pictures.

Interpretive researchers often use participant observation and field research.

Other ISS researchers analyse transcripts of conversations or study videotapes of behaviors in

extraordinary details.

It has practical orientation.

Interpretive researchers study meaningful social action, not just the external or observable behavior of

people.

Social action is the action to which people attach subjective meaning: it is activity with a purpose of intent,

e.g. a physical reflex such as eye blinking.

The interpretive approach notes that human action has little inherent meaning. It acquires meaning among

people who share meaning system that permits them to interpret the action as a socially relevant sign or

action, e.g. raising one finger.

Interpretive approach holds that social life is based on social interactions and socially constructed meaning

systems.

For interpretive researchers, social reality is based on people’s definition of it.

The interpretive approach is ideographic and inductive. Ideographic means the approach provides a

symbolic representation or “thick” description of something else.

An interpretive research report may read like a novel or a biography than like a mathematical proof.

An interpretive theory gives the reader a feel for another’s social reality.

For ISS, a theory is true if it makes sense to those being studied and it allows others to understand deeply

or enter the reality of those being studies.

Critical Social Science

The purpose of social science is to reveal what is hidden to liberate and empower people.



Social reality has multiple layers.

People have unrealized potential and are mislead by reification; social science is relational.

The bounded autonomy stance is towards human agency.

Scientific knowledge is imperfect but can fight false consciousness.

Abduction is used to create explanatory critiques.

Explanations are verified through praxis.

All evidence is theory dependent, and some theories reveal deeper types of evidence.

A reflexive-dialectic orientation is adopted towards knowledge that is used from a transformative

perspective.

Social reality and the study of it necessarily contain a moral political dimension, and moral political

positions are unequal in advancing human freedom and empowerment.

CHAPTER 6 – STRATEGIES OF RESEARCH DESIGN

Introduction. Quantitative researchers are more concerned about issues of design, measurement, and

sampling because their deductive approach emphasizes detailed planning prior to data collection and analysis.

Qualitative researchers are more concerned about issues of the richness, texture, and feeling of raw data

because their inductive approach emphasizes developing insights and generalizations out of the data collected.

Triangulation. By observing something from different angles or viewpoints, the researchers get a fix on its

true location. This process, called triangulation, is used by quantitative and qualitative social researchers.



Applied to social research, it means it is better to look at something from several angles than to look at it in

only one way. There are four types of triangulation.

Triangulation of Measures. Researchers take multiple measures of the same phenomena.

Triangulation of Observers. Multiple observers or researchers add alternative perspectives,

backgrounds, and social characteristics and will reduce the limitations.

Triangulation of Theory. It occurs when a researcher uses multiple theoretical perspectives in the

planning stages of research, or when interpreting the data.

Triangulation of Method. It means mixing qualitative and quantitative styles of research and data.

Qualitative and Quantitative Orientations toward Research

Qualitative and Quantitative approaches differ in the nature of the data. Soft data, in the form of

impressions, words, sentences, photos, symbols, and so forth, dictate different research strategies and

data collection techniques than hard data, in the form of numbers.

Qualitative and Quantitative researchers often hold different assumptions about social life and have

different objectives.

Qualitative researchers often rely on interpretive or critical social science. They use a transcendent

perspective, apply “logic in practice”, and follow a nonlinear research path.

Qualitative researchers speak a language of “cases and contexts”.

Quantitative researcher uses a technocratic perspective, apply “reconstructed logic”, and follow a linear

research path. They speak a language of “variables and hypotheses”. Quantitative researchers s emphasize

precisely measuring variables and testing hypotheses that are linked to general causal explanations.

Reconstructed Logic and Logic in Practice

The way social researchers learn and discuss research usually follows one of two logics: reconstructed logic

or logic in practice.

Quantitative researchers apply more of the reconstructed logic, whereas qualitative researchers tend to

apply logic in practice.

Reconstructed logic means the logic of research based on reorganizing, standardizing, and codifying

research knowledge and practices into explicit rules and procedures, and techniques.

Logic in practice is the logic of research based on an apprenticeship model and the sharing of explicit

knowledge about practical concerns and specific experiences.

Linear and Non Linear Paths

The path is a metaphor for the sequence of things to do: what is finished first or where a researcher has

been, and what comes next or where he or she is going.

Quantitative researchers follow more linear path than do qualitative researchers. A linear research path

follows a fixed sequence of steps.

Qualitative research is more nonlinear and cyclical. A nonlinear research path makes successive passes

through steps, sometimes moving backward and sideways before moving on.

Objectivity and Integrity

Qualitative researchers emphasize the human factor and the intimate firsthand knowledge of the research

setting; they avoid distancing themselves from the people or events they study.

Quantitative researchers stress objectivity and more “mechanical” techniques. They use principle of

replication, adhere to standardized methodological procedures, measure with numbers, and then analyze



the data with statistics, an area of applied mathematics. Quantitative research eliminates the human

factor.

Qualitative researchers emphasize trustworthiness as a parallel idea to objective standards in quantitative

research design. This ensures that research activities are dependable and credible. Quantitative research

addresses the issue of integrity by relying on an objective technology such as precise statements, standard

techniques, numerical measures, statistics, and replication.

Qualitative researchers ensure that their research accurately reflects the evidence and have checks on

their evidence.

Preplanned and Emergent Research Questions

All research begins with a topic but a topic is only a starting point that researchers must narrow into a

focused research question.

Qualitative researchers often begin with vague or unclear research questions. The topic emerges slowly

during the study.

Quantitative researchers narrow a topic into a focused question as a discrete planning step before they

finalize study design. They use it as a step in the process of developing a testable hypothesis and to guide

the study design before they collect any data.

The qualitative researcher begin data gathering with a general topic and notions of what will be relevant.

Major limitations include time, costs, access to resources, and approval by authorities, ethical concerns,

and expertise.

Techniques for Narrowing a Topic into Research Question

Examine the Literature. Published articles are excellent sources for ideas for research questions. They

are usually at an appropriate level of specifying and suggesting research questions that focus on following:-

Replacing a previous research project exactly or with slight changes.

Exploring unexpected findings discovered in previous research.

Following suggestions an author gives for future research at the end of the article.

Extending an existing explanation or theory to a new topic or setting.

Challenging the findings or attempting to refute a relationship.

Specifying or intervening the process and considering any linking relations.

Talk Over the Idea with Others

Ask people who are knowledgeable about the topic or questions it that they have thought of.

Seek out those who hold opinions that differ from yours on the topic and discuss possible research

questions with them.

Apply to a Specific Context

Focus the topic onto a specific historical period, or time period.

Narrow the topic to a specific society or geographic unit.

Consider which subgroups or categories of people/ units are involved and whether there are

differences among them.

Define the Aim and Desired Outcome of the Study

Will the research question be for an explanatory, explanatory or descriptive study?

Will the study involve applied or basic research?



Qualitative Design Issues

The Language of Case and Contexts. Qualitative researcher use a language of cases and contexts,

employ bricolage, examine social processes and cases in their social context, and look at interpretations or

the creation of meaning in specific settings.

Grounded Theory. A qualitative researcher develops theory during the data collection process.

This more inductive method means that theory is built from data or grounded in the data.

The Context is Critical. Qualitative researchers emphasize the social context for understanding the

social world. They hold that the meaning of a social action or statement depends, in an important way, on

the context in which it appears.

Bricolage. Qualitative researchers are bricoleurs; they learn to be adept at doing many things, drawing on

a variety of sources, and making do with whatever is at hand. A bricolage technique means working with

one’s hands and being pragmatic at using an assortment of odds and ends in an inventive manner to

accomplish a specific task.

The Case and Process. In quantitative research, cases are usually the same as a unit of analysis, or the

unit on which variables are measured. Qualitative researcher tends to use a “case oriented approach

places cases, not variables, center stage”. The passage of time is integral to qualitative research.

Quantitative researchers typically measure variables of their hypotheses across many cases.

Interpretation. A qualitative researcher interprets data by giving them meaning, translating them, or

making them understandable. Quantitative research is expressed in numbers, and a researcher gives

meaning to the numbers and tells how they relate to hypotheses.

Quantitative Design Issues

The Language of Variables and Hypotheses

Variation and Variables

The variable is a central idea in quantitative research.

A variable is a concept that varies.

Quantitative research uses a language of variables and relationships among variables.

The values or categories of a variable are its attributes.

The attribute of one variable can itself become a separate variable with a slight change in

definition.

Quantitative researchers redefine concepts of interest into the language of variables.

Types of Variables

Researchers who focus on causal relations usually begin with an effect, and then search for its

causes.

Variables are classified into three basic types, depending on their location in causal relationship.

The cause variable is the independent variable.

The effect variable is the dependent variable.

Independent variables come before other type. Independent variables affect or have an impact on

other variables. Research topics are often phrased in terms of the dependent variables because

dependent variables are the phenomenon to be explained.

A third type of variable is the intervening variable, appears in more complex causal relations. It

comes between the independent and dependent variables and shows the link or mechanism



between them. In a sense, the intervening variable acts as a dependent variable with respect to

the independent variable and acts as an independent variable toward the dependent variable.

Simple theories have one dependent and one independent variable, whereas complex theories can

contain dozens of variables with multiple independent, intervening, and dependent variables.

Causal Theory and Hypotheses

The hypothesis and Causality. A hypothesis is a proposition to be tested or a tentative statement of a

relationship between two variables.

Five Characteristics of Causal hypotheses

Has at least two variables

Expresses a causal or cause effect relationship between the variables

Can be expressed as a prediction or an expected future outcome

Logically linked to a research question and a theory

Falsifiable and can be tested against empirical evidence and show to be true of false

Words that can be used to stated causal relations

Causes Leads Is associated with Produces

Is related Influences Results If then The higher, the lower Reduces

Testing and refining hypothesis. Knowledge develops over times as researchers throughout the

scientific community test many hypotheses. A hypothesis needs several tests with consistent and

repeated support to gain broad acceptance. The strongest contender or the hypothesis with the

greatest empirical support is accepted as the best explanation at the time.

Types of Hypotheses

It is not a must that a hypothesis is always proved, it can be disproved.

Because a hypothesis make predictions, negative and disconfirming evidence shows that the

predictions are wrong.

Positive or confirming evidence for a hypothesis is less critical because alternative hypotheses may

make the same prediction.

Researchers test hypotheses in two ways: a straightforward way and a null hypothesis way.

Null hypotheses. A hypothesis stating that there is no significant effect of an independent

variable on a dependant variable. Researchers use the null hypothesis with a corresponding alternative

hypothesis or experimental hypothesis.

Alternative Hypothesis. A hypothesis paired with the null hypothesis that says an independent

variable has a significant effect on the dependant variable. When null hypothesis testing is added to

confirming evidence, the argument for an alternative hypothesis can grow stronger over time.

Double Barreled Hypothesis. It is a confusing and poorly designed hypothesis with two independent

variables in which it is unclear whether one or the other variable or both in combination produce

effect. A double barreled hypothesis puts two relationships in one hypothesis.

Clarity about Units and Levels of Analysis

A level of analysis is the level of social reality to which theoretical explanations refer. A level of analysis can

be macro or micro.

The level of analysis delimits the kinds of assumptions, concepts, and theories that a researcher uses.



The unit of analysis refers to the type of unit a researcher uses when measuring.

Common units are individual, group, organization, social category, social institution, and the society.

The units of analysis determine how a researcher measures variables or themes.

Researchers use levels and units of analysis to design research projects, and being aware of them helps

researchers avoid logical errors.

Potential Errors in Causal Explanation

Tautology. A tautology is a form of circular reasoning in which someone appears to say something new

but is really talking in circles and making a statement that is true by definition. Tautologies cannot be

tested with empirical data, e.g. IT project failure causes low funding of IT project.

Teleology. A teleology is something directed by an ultimate purpose or goal, and it takes several forms.

Teleology cannot be empirically measured. They violate the temporal order requirement of causality and

they lack a true independent variable because the “causal factor” is so extremely vague, e.g. If we invest a

lot of funding in IT project, it will be successful

Ecological Fallacy. The ecological Fallacy arises from a mismatch of units of analysis. It refers to a poor fit

between the units for which researcher empirical evidence and the units for which he or she wants to

make statements. Ecological fallacy occurs when a researcher gathers data at a higher or an aggregated

unit of analysis but wants to make a statement about a lower or disaggregated unit, e.g. majority of IS

project are unsuccessful, therefore this case study fails.

Reductionism. Reductionism or fallacy of nonequivalence occurs when a researcher explains macro

level events but has evidence only about specific individuals. It occurs when a researcher observes a lower

or disaggregated unit of analysis but makes statements about the operations of higher or aggregated units.

Spuriousness. Spuriousness occurs when two variables are associated but are not causally related

because there is actually an unseen third factor that is the real cause.

From the Research Question to hypotheses

Hints about hypotheses are embedded within a good research question.

Hypotheses are tentative answers to research question.

Several hypotheses can be developed for one research question.

A researcher can formulate a tentative research question, and then develop possible hypotheses; the

hypotheses then help the researcher state the research question more precisely.

Researchers use general theoretical issues as a source of topics.

Theories provide concepts that researchers turn into variables as well as the reasoning or mechanism that

help researchers connect variables into research question.

CHAPTER 7 – QUALITATIVE AND QUANTITATIVE MEASUREMENT

The Measurement Process

• Conceptualization. The process of developing clear, rigorous, systematic conceptual definitions for

abstract ideas/ concepts. It is a process of thinking through the various possible meanings of a construct.

• Conceptual Definition. A careful, systematic definition of a construct that is explicitly written down.

• Operationalization. The process of moving from the construct’s conceptual definition to specific activities

that allows a researcher to observe it empirically.

• Operational Definition. A variable in terms of the specific actions to measure or indicate it in the

empirical world.



• Steps

• Identify concept of interest

• Develop conceptual definition. Decide on units of analysis

• Operationalize to create a variable. Links concept to measurement technique

Quantitative Conceptualization and Operationalization

• Rules for Correspondence. Standards that researchers use to connect abstract constructs with

measurement operations in empirical social reality.

• Conceptual Hypothesis. Type of hypothesis that expresses variables and the relationship among them

in abstract, conceptual terms.

• Empirical Hypothesis. A type of hypothesis in which the researcher expresses variables in specific

empirical terms and expresses the association among the measured indicators in observable empirical

terms.

Qualitative Conceptualization and Operationalization

• Conceptualization. It is a process of forming coherent theoretical definitions as we organize the

data and our preliminary ideas about it.

• Operationalization. It involves developing a description of how we use working ideas while making

observations.

Empirical Observations Working Ideas Concepts Generalizations/Theories

Reliability and Validity

Reliability. The consistency or dependency of a measure. Does the measure consistently give the same

results?

• Reliability and Validity in Quantitative Research. Measurement reliability is the dependability and

consistency of a measure of variables. It means that the numerical results an indicator produces do not

vary because of characteristics of measurement process or measurement instrument itself. There are

three types of reliability.

• Stability Reliability. Measurement reliability across time, the measure that yields consistent results

at different time points assuming what is being measured does not itself change. Does the measure

deliver the same answer when applied in different time periods?



• Representative Reliability. Measurement reliability across groups, a measure that yields

consistent results for various social groups. Does the indicator deliver same answers when applied to

different groups?

• Equivalence Reliability. Measurement reliability across indicators, a measure that yields

consistent results using different specific indicators, assuming that all measure the same construct.

Does the measure give consistent results across different indicators?

• How to Improve Reliability. It is rare to have perfect reliability. Four things can improve reliability:-

• Clearly conceptualize constructs.

• Use of most precise (or highest) level of measurement possible.

• Use multiple indicators of a variable.

• Use of pretests, pilot studies, replication.

Validity. The truthfulness of a measure. Is it measuring what the researcher thinks it is measuring?

Measurement validity is how well an empirical indicator and the conceptual definition of a construct that the

indicator is supposed to measure ‘fit’ together. There are four types of measurement validity:-

• Face Validity. A type of measurement validity in which an indicator makes sense as a measure of a

construct in the judgment of others, especially in the scientific community. ‘On the face of it’.

• Content Validity. A type of measurement validity that requires that a measure represents all aspects of

the conceptual definition of a construct. Is the full content of definition represented in the measure?

• Criterion Validity. Measurement validity that relies on some independent, outside verification.

• Concurrent Validity. Measurement validity that relies on a preexisting and already accepted

measure to verify the indicator of the construct.

• Predictive Validity. Measurement validity that relies on the concurrence of future event or

behavior that is logically consistent to verify the indicator of the construct.

• Construct Validity. The type of measurement validity that uses multiple indicators.

• Convergent Validity. A type of measurement validity for multiple indicators based on the idea that

indicators of one construct will act alike or converge.

• Discriminant Validity. A type of measurement validity for multiple indicators based on the idea that

indicators of different constructs diverge.

Reliability and Validity in Qualitative Research

• Reliability. A wide variety of techniques such as interviews, participation, photographs and document

studies is conducted. We use range of data sources and employ multiple measurement methods.

• Validity. It means offering a fair, honest and balanced account of social life from the view point of the

people who live it every day instead of realizing a single version of ‘Truth’.

Relationship between Reliability and Validity



Internal Validity. It means we have not made errors internal to the design of a research project that

might produce false conclusions.

External Validity. It refers to whether we can generalize a result that we found in a specific setting with

a particular small group beyond that situation or externally to a wider range of settings and many different

people.

Statistical Validity. It means that we have used the proper statistical procedure for a particular purpose

and have met the procedure’s mathematical requirements.

Levels of Measurement. A system of organizing information in the measurement of variables into four

levels, from nominal to ratio level.

Continuous and Discreet Variables

• Continuous Variables. Variables that are measured on a continuum in which an infinite number of

finer graduations between variable attributes are possible.

• Discreet Variables. Variables in which the attributes can be measured with only a limited number

of distinct, separate categories.

Four levels of measurement

• Nominal Level Measurement. The lowest, least precise level of measurement for which there is a

difference in type only among categories of a variable, e.g. religion: Islam, Christianity, Judaism etc

• Ordinal Level Measurement. The level of measurement that identifies a difference among

categories of a variable and allows the categories to be rank ordered as well. Letter grades A, B, C, D, E etc

• Interval Level Measurement. The level of measurement that identifies differences among variable

attributes, rank categories and measure distance between categories but has no true zero, e.g. Celsius

temperature: 50C, 100C, 150C etc

• Ratio Level Measurement. The highest, most precise level of measurement; variable attributes can be

rank ordered, the distance between them precisely measured, and there is no absolute zero, e.g. money

income: $ 100, $ 500, $ 1000 etc

Principles of Good Measurement

• Mutually Elusive Attributes. The principle that the variable attributes or categories in a measure

are organised so that the responses fit into only one category and there is no overlap.

• Exhaustive Attributes. The principle that attributes or categories in a measure should provide a

category for all possible responses.

• Unidimensionality. The principle that when using multiple indicators to measure a construct, all

indicators should consistently fit together and indicate a single construct.

Scales and Indexes

• Index. The summing or combining of many separate measures of a construct or variable to create a

single score.

• Standardization. Procedures to adjust measures statistically to permit making an honest comparison by

giving a common basis to measures of different units.

• Scales. A class of quantitative data measures often used in survey research that captures the intensity,

direction, level or potency of a variable constructs along a continuum; most are at the ordinal level of

measurement.



• Likert Scaling. A scale often used in survey research in which people express attitudes or other

responses in terms of ordinal level categories (e.g. agree, disagree) that are ranked along a continuum.

• Thurstone Scaling. Measuring in which the researcher gives a group of judges many items and

asks them to sort the items into categories along a continuum and then considers the sorting results to

select items on which the judges agree. It is also called method of equal appearing intervals.

• Bogardus Social Distance Scale. A scale measuring the social distance between two or more

social groups by having members of one group indicate the limit of their comfort with various types of

social interaction or closeness with members of the other group(s).

• Semantic Differential. A scale that indirectly measures feelings or thoughts by presenting people a

topic or object and a list of polar opposite adjectives or adverbs and then having them indicate feelings

by marking one of several spaces between the two adjectives and adverbs.

• Guttman Scaling. A scale that researchers use after data are collected to reveal whether a

hierarchical pattern exists among responses so that people who give responses at a ‘higher level’ also

tend to give ‘lower level’ ones.

CHAPTER 8 – QUALITATIVE AND QUANTITATIVE SAMPLING

Reasons for Sampling

• Sample. A small set of cases a researcher selects from a large pool and generalizes to the population.

• Population. The abstract idea of large group of many cases from which a researcher draws a

sample and to which the results from a sample are generalized.

• Sampling Element. The name for a case or single unit to be sampled.

• Sampling Frame. A list of cases in a population, or the best approximation of them.



Sampling Strategies

Probability Sampling Technique

• Random Sample. A sample using a mathematically random method, such as random number table or

computer program, so that each sampling element of the population has an equal probability of being

selected into the sample.

• Simple Random. A random sample in which a researcher creates a sampling frame and uses a

pure random process to select cases so that each sampling element in population will have an equal

probability of being selected.

• Systematic Sampling. A random sample in which a researcher selects every kth(e.g. 3rd or 12th) case

in the sampling frame using a sampling interval.

• Stratified Sampling. A random sample in which the researcher first identifies a set of mutually

elusive and exhaustive categories, divides the sampling frame by the categories, and then uses random

selection to select cases from each category.

• Multi Stage Cluster Sampling. A type of random sample that uses multiple stages and is often used

to cover wide geographic areas in which aggregated units are randomly selected and then samples are

drawn from the sampled aggregated units or clusters.

Non Probability Sampling Techniques

• Convenience Sampling. A non random sample in which the researcher selects anyone he or she

happens to come across.

• Snowball Sampling. It uses a small pool of initial informants to nominate, through their social

networks, other participants who meet the eligibility criteria and could potentially contribute to a specific

study. The term “snowball sampling” reflects an analogy to a snowball increasing in size as it rolls downhill.

• Quota Sampling. A non random sample in which researcher first identifies general categories into which

cases or people will be placed and then selects cases to reach the predetermined number in each category.

• Theoretical Sampling. A non random sample in which the researcher selects specific times, locations,

or events to observe in order to develop a social theory or evaluate theoretical ideas.

Documents

134621811 Advance Research Methods