Measurement in Research

The core of any research is measurement. It can be defined as the method of assigning numbers to things. It is essential in research as everything has to be reduced to numbers.

Assigning numbers to properties of things is easy. However, it is quite difficult in other cases. Measuring social conformity or intelligence is much complex than measuring weight, age or financial assets, which can be directly measured directly with some standard unit of measurement. Measurement tools of abstract/qualitative concepts are not standardized, and the results are not very accurate.

A clear understanding of the level of measurement of variables is important in research because it is the level, which determines what type of statistical analysis has to be conducted. The collected data can be classified into distinct categories. If there are limited categories, then they are known as discrete variables. If there are unlimited categories, they are known as continuous variables. The nominal level of measurement describes these categorical variables. Nominal variables include demographic properties like sex, race, religion, etc. This is considered as the most basic level of measurement. No ranking or hierarchy is present in this level.

The variables that can be sequenced in some order of importance can be described by the ordinal level. Opinions and attitude scales or indexes in the social sciences are ordinal in nature. Ex.: Upper, middle, and lower class. In this case, the order is known; however, the interval between the values is not meaningful.

Variables that have more or less equal intervals are described by the interval level of measurement. Crime rates come under this measurement level. Temperature is also an interval variable. Here, the interval between variables can be interpreted; but, ratios are not meaningful.

Ratio level describes variables that have equal intervals and a reference point. Measurement of physical dimension such as weight, height, distance, etc. falls under this level.


Random Sample From an Infinite Universe

It is relatively difficult to explain the concept of random sample from an infinite population. However, a few examples will show the basic characteristics of such a sample. Suppose we consider the 10 throws of a fair dice as a sample from the hypothetically infinite population that consists of the results of all possible throws of the dice. If the probability of getting a particular number, say 7, is the same for each throw and the 10 throws are all independent, then we say that the sample is random. Similarly, it would be said to be sampling from an infinite population if we sample with replacement from an infinite population and our sample would be considered as a random sample if in each draw all elements of the population have the same probability of being selected and successive draws happen to be independent. In brief, one can say that the selection of each item in a random sample from an infinite population is controlled by the same probabilities and that successive selections are independent of one another.

In other words, if we have to take a sample of grain from a bag, it is not possible to assign a number to each grain or particle constituting the universe and as such the methods of constructing card population or of random sampling numbers cannot be used. In such cases a thorough mixing of the grain may be done and by dividing and sub-dividing the lot in parts, a sample of an adequate size can be obtained. The contents of the bag after thorough mixing may be divided in two equal parts of which one may be selected and this may further be divided in two parts after mixing. In this way the process can be continued till one of the sub-divisions is equal to the size of the desired sample.


Selecting a Random Sample

Random sample is the basic sampling method. Its main advantage is that, each member of the group is given an equal chance of being chosen. Thus, the statistical conclusions deduced from a random sample analysis are deemed to be valid. Though it sounds easy, the process of selection of a random sample is quite complex.

Lottery Method: This is the most commonly used method. Every member is assigned a unique number. These numbers are put in a jar and thoroughly mixed. After that, the researcher picks some numbers without looking at it and those people are included in the study.

Random Number Table: This table consists of a series of digits (0-9) that are generated randomly. The numbers are arranged in rows and columns and can be read in any direction. All the digits are equally probable.

Computer: In case of large population, selecting random samples manually becomes tedious and very time-consuming. In these cases, specific computer softwares are used to generate numbers randomly. This process is very fast and easy.

With and Without Replacement: When a population element is given the chance to be chosen more than once, it is known as sampling with replacement; when it can be chosen only once, it is known as sampling without replacement.


Types of Sample Designs

Basically, there are two different types of sample designs, namely, non-probability sampling and probability sampling. Each of the two is described below.

(1) Non-probability sampling: This type of sampling is also known as deliberate sampling, purposive sampling, or judgement sampling. In this sampling procedure, the organisers of the inquiry deliberately choose the particular units of the universe to compose a sample on the basis that the small mass selected out of a large one would represent the whole. For example, if economic conditions of the population living in a state are to be studied, a few cities and towns can be deliberately selected for intensive study on the principle that they can represent the entire state. Besides, the investigator may select a sample yielding results favorable to his point of view. In case that happens, the entire inquiry may get vitiated. Thus, there exists the danger of bias entering into this type of sampling technique. However, if the investigators are impartial, work without bias and have the necessary experience so as to take sound judgement, the obtained results of an analysis of deliberately selected sample may be tolerably reliable.

Quota sampling is also an example of non-probability sampling. In this type of sampling the interviewers are simply given quotas to be filled from the different strata, with some instructions regarding filling up the quotas. Moreover, this type of sampling is relatively inexpensive and quite convenient.

(2) Probability sampling: This type of sampling is also known as random sampling or chance sampling. This sampling procedure gives each element in the population an equal chance of getting selected for the sample; besides, all choices are independent of one another. The obtained results of probability sampling can be assured in terms of probability. In other words, we can measure the errors of estimation or the significance of obtained results from a random sample. In fact, due to this very reason probability sampling design is superior to the deliberate sampling design. Probability sampling ensures the law of Statistical Regularity, which states that if the sample chosen is a random one, the sample will have the same composition and characteristics as the universe. Hence, probability sampling is more or less the best technique to select a representative sample.


Characteristics of a Good Sample Design

In a field study due to time and cost involved, generally, only a section of the population is studied. These respondents are known as the sample and are representative of the general population or universe. A sample design is a definite plan for obtaining a sample from a population. It refers to the technique or the procedure for obtaining a sample from a given population.

Following are the characteristics of good sample design:

1. Sample design should be a representative sample: A researcher selects a relatively small number for a sample from an entire population. This sample needs to closely match all the characteristics of the entire population. If the sample used in an experiment is a representative sample then it will help generalize the results from a small group to large universe being studied.

2. Sample design should have small sampling error:  Sampling error is the error caused by taking a small sample instead of the whole population for study. Sampling error refers to the discrepancy that may result from judging all on the basis of a small number.Sampling error is reduced by selecting a large sample and by using efficient sample design and estimation strategies.

3. Sample design should be economically viable: Studies have a limited budget called the research budget. The sampling should be done in such a way that it is within the research budget and not too expensive to be replicated.

4. Sample design should have marginal systematic bias: Systematic bias results from errors in the sampling procedures which cannot be reduced or eliminated by increasing the sample size. The best bet for researchers is to detect the causes and correct them.

5. Results obtained from the sample should be generalized and applicable to the whole universe: The sampling design should be created keeping in mind that samples that it covers the whole universe of the study and is not limited to a part.


Steps for Sample Design

The researcher must keep in mind the following points while preparing a sample design.

(i) Universe: While preparing a sample design, it is foremost required to define the set of objects to be studied.

Technically, it is also known as the Universe, which can be finite or infinite. In case of a finite universe, the number of items is limited. Whereas, in an infinite universe the number of items is limitless.

(ii) Sampling unit: It is necessary to decide a sampling unit before selecting a sample. It can be a geographical one (state, district, village, etc.), a construction unit (house, flat, etc.), a social unit (family, club, school, etc.), or an individual.

(iii) Source list: In other words, it is called the ‘sampling frame’ from which the sample is drawn. It comprises the names of all items of a universe (finite universe only). If source list/sampling frame is unavailable, the researcher has to prepare it by himself.

(iv) Sample size: This is the number of items, selected from the universe, constituting a sample. The sample size should not be too large or too small, but optimum. In other words, an optimum sample accomplishes the requirements of efficiency, representativeness, reliability and flexibility.

(v) Parameters of interest: While determining a sample design, it is required to consider the question of the specific population parameters of interest. For example, we may like to estimate the proportion of persons with some specific attributes in the population, or we may also like to know some average or other measure concerning the population.

(vi) Budgetary constraint: Practically, cost considerations have a major impact upon the decisions concerning not only the sample size but also the sample type. In fact, this can even lead to the use of a non-probability sample.

(vii) Sampling procedure: The researcher, at last, decides the techniques to be used in selecting the items for the sample. In fact, this technique/procedure stands for the sample design itself. Apparently, such a design should be selected, which for a provided sample size and cost, has a smaller sampling error.



Research Design in Hypothesis-Testing Research Studies

In hypothesis-testing research studies, also known as experimental studies, the researcher generally tests the hypotheses of causal relationships among variables. Besides, such type of studies needs those kinds of procedures, which will not only reduce the bias and increase reliability, but will also approve the drawing inferences about causality. Hence, when we discuss about the research design in such studies, we usually mean the experimental designs.

Experimental designs were discovered and developed by Professor R. A. Fisher, who was working at the Rothamsted Experimental Station, at the Centre for Agricultural Research in England. In fact, the study of experimental designs originated in agricultural research. Professor Fisher divided the agricultural fields/plots into different blocks and conducted experiments in each of them. Consequently, whatever information was collected from this, he found them to be very reliable. In this way, he was inspired to develop certain experimental designs to test the hypotheses about scientific investigations. In recent time, the experimental designs are being used in researches related to phenomena of several disciplines. Besides, since experimental designs originated in the context of agricultural operations, we still use, although in a technical sense, several agricultural terms, such as, treatment, yield, plot, block, etc., in the experimental designs.


Different Research Designs

Research Design in Exploratory Research Studies

Exploratory research studies, also known as formulative research studies, are conducted when there are very few or no earlier studies for reference. In this type of research design, a vague problem is chosen, which is followed by an exploratory research to find a new hypothesis. It lays emphasis on discovery of ideas and possible insights that help in identifying areas for future experimentation.


1) It provides information to form a more precise problem definition or hypothesis.

2) It establishes research priorities.

3) It gives the researcher a feel of the problem situation and familiarizes him with the problem.

4) It collects information about possible problems in carrying out the research using specific collection tools and specific techniques for analysis.

In exploratory studies, the following three methods are generally used:

1) Survey of relevant literature

2) Survey of experienced individuals

3) Analysis of selected examples

Survey of Relevant Literature

Published literature are very good sources for the purpose of hypothesis generation and problem definition. Much of the published and unpublished data is available through books, journals, newspapers, periodicals, government publications, individual research projects, and data collected by the trade associations. Some of it could be relevant to the given problem situation. An analysis of existing literature may not provide the solution to the research problem, but, it surely gives a direction to the research process.

Survey of Experienced Individuals

Talking to individuals who have expertise and ideas about the research subject can be very useful for the study. Attempt should be made to gather all possible information about the subject of research from people who have specific knowledge about it. In this case, the experimenter must prepare a systematic interview schedule to collect information from the respondents. The success of this survey depends upon the freedom of response given to the respondent, expertise and communication skills of the respondents, and the conversational skills of the experimenter in extracting maximum information from the respondents.

Analysis of Selected Examples

This method involves the selection of examples, which reflect the problem situation. A thorough analysis of the examples is conducted. In certain cases, such type of study helps in identifying the possible relationships that exist between the variables. The relationships, their extent, and direction are then measured using conclusive research designs.


Features of a Good Research Design

When a researcher has formulated a research problem, he/she has to focus on developing a good design for solving the problem. A good design is one that minimizes bias and maximizes the reliability of the data. It also yields maximum information, gives minimum experimental error, and provides different aspects of a single problem. A research design depends on the purpose and nature of the research problem. Thus, one single design cannot be used to solve all types of research problem, i.e., a particular design is suitable for a particular problem.

A research design usually consists of the following factors:

(i) The means of obtaining information;

(ii) The availability and skills of the researcher and his staff, if any;

(iii) The objective of the problem to be studied;

(iv) The nature of the problem; and

(v) The availability of time and money for the research work.

If a research study is an exploratory or formulative one, i.e., it focuses on discovery of ideas and insights, the research design should be flexible enough to consider different aspects of the study. Similarly, if the study focuses on accurate description or association between variables, the design should be accurate with minimum bias and maximum reliability. However, in practice, it is difficult to categorize a particular study into a particular group. A study can be categorized only on the basis of its primary function and accordingly, its design can be developed. Moreover, the above mentioned factors must be given due weightage while working on the details of the research design.


Need for Research Design

Research design has a significant impact on the reliability of the results obtained. It thus acts as a firm foundation for the entire research. It is needed because it facilitates the smooth functioning of the various research operations. It makes the research as efficient as possible by giving maximum information with minimal expenditure of effort, time and money. For construction of a house, we need to have a proper blueprint prepared by an expert architect. Similarly, we need a proper research design or plan prior to data collection and analysis of our research project. Preparation of research design should be done carefully as even a minute error might ruin the purpose of the entire project. The design helps the researcher to organize his ideas, which helps to identify and correct his flaws, if any. In a good research design, all the components  with each other or go together with each other in a coherent manner. The theoretical and conceptual framework must  with the research goals and purposes. Likewise, the data collection strategy must fit with the research purposes, conceptual and theoretical framework and approach to data analysis.

The need for research design is as follows:

  • It reduces inaccuracy;
  • Helps to get maximum efficiency and reliability;
  • Eliminates bias and marginal errors;
  • Minimizes wastage of time;
  • Helpful for collecting research materials;
  • Helpful for testing of hypothesis;
  • Gives an idea regarding the type of resources required in terms of money, manpower,  time, and efforts;
  • Provides an overview to other experts;
  • Guides the research in the right direction.