Test of Practicality of a measuring instrument

Test of Practicality of a measuring instrument

The practicality attribute of a measuring instrument can be estimated regarding its economy, convenience and interpretability. From the operational point of view, the measuring instrument needs to be practical. In other words, it should be economical, convenient and interpreted.

Economy consideration suggests that some mutual benefit is required between the ideal research project and that which the budget can afford. The length of measuring instrument is an important area where economic pressures are swiftly felt. Even though more items give better reliability, in the interest of limiting the interview or observation time, we have to take only few items for the study purpose. Similarly, the data-collection methods, which are to be used, occasionally depend upon economic factors.

Convenience test suggests that the measuring instrument should be easily manageable. For this purpose, one should pay proper attention to the layout of the measuring instrument. For example, a questionnaire with clear instructions and illustrated examples is comparatively more effective and easier to complete than the questionnaire that lacks these features. Interpretability consideration is especially important when persons other than the designers of the test are to interpret the results. In order to be interpretable, the measuring instrument must be supplemented by the following:

  1. detailed instructions for administering the test,
  2. scoring keys,
  3. evidence about the reliability, and
  4. guides for using the test and interpreting results.

Test of Reliability

Reliability is an essential element of test quality. An instrument for measurement is reliable if it provides consistent results. But a reliable instrument need not be valid. For example, if a clock shows time nonstop then it is reliable, but that does not mean it is showing the correct time. Reliability deals with consistency, or reproducibility of similar results in a test by the test subject, if a test is administered on two occasions; the same conclusions are reached both times. While a test with poor reliability will have remarkably different scores each time with the same test and same examinee.

If a test is then it has to be reliable, but the vice versa is not true. Although, reliability might is not as valuable as validity, but nonetheless reliability it is easier to assess than validity for a test. Reliability has two key aspects: stability and equivalence. The degree of stability can be located comparing the results of repeated measurements with the same candidate and the same instrument. Equivalence means the probability of the amount of errors getting introduced by various investigators or different sample items being studied during the repetition of the test. The best way to test for reliability of a test is that two investigators should compare their observations of the same events. Reliability can be improved in the following ways:

(i) By standardizing the measurement conditions to reduce external factors such as boredom, fatigue, etc. which leads to achievement of stability.

(ii) By detailed directions for measurement which can be generalized and used by trained and motivated persons to conduct research and also by increasing the purview of the sample of items used, this lead to equivalence.


Techniques Involved in Defining a Problem

As a researcher, you must have often read that defining a problem is the first step in a research process. But, have you ever wondered what is meant by defining a problem. Well, it simply means that the researcher has to lay down certain boundaries within which he/she has to study the problem with a pre-defined objective in mind.

Defining a problem is a herculean task, and this must be done intelligently to avoid confusions that arise in the research operation. Try to follow the below steps systematically to best define a problem:

 i.  State the problem in a general way:

First state the problem in general terms with respect to some practical, scientific or intellectual interest. For this, the researcher may himself read the concerned subject matter thoroughly or take the help of the subject expert. Often, the guide states the problem in general terms; it depends on the researcher if he/she wants to narrow it down to operational terms. The problem stated should also be checked for ambiguity and feasibility.

ii.  Understand the nature of the problem:

The next step is to understand the nature and origin of the problem. The researcher needs to discuss the problem with those related to the subject matter in order to clearly understand the origin of the problem, its nature, objectives, and the environment in which the problem is to be studied.

iii. Survey the available literature:

All available literature including relevant theories, reports, records, and other relevant literature on the problem needs to be reviewed and examined. This would help the researcher to identify the data available, the techniques that might be used, types of difficulties that may be encountered during the study, possible analytical shortcomings, and even new methods of approach to the present problem.

iv.  Go for discussions for developing ideas:

The researcher may discuss the problem with his/her colleagues and others related to the concerned subject. This helps the researcher to generate new ideas, identify different aspects on the problem, gain suggestions and advices from others, and sharpen his focus on certain aspects within the field. However, discussions should not be limited to the problem only, but should also be related to the general approach to the problem, techniques that might be used, possible solutions, etc.

v.  Rephrase the research problem into a working proposition:

Finally, the researcher must rephrase the problem into a working proposition. Rephrasing the problem means putting the problem in specific terms that is feasible and may help in the development of working hypotheses. Once the researcher has gone through the above steps systematically, it is easy to rephrase the problem into analytical and operational terms.


Data Analysis

The very first task of the researcher, after the collection of data, is to analyse them. The process of data analysis demands a variety of closely related operations, namely:

  • The establishment of different categories,
  • The application of these categories to the raw data through coding,
  • Making tabulations and then drawing statistical inferences.


The unmanageable data should be, necessarily, condensed into a few manageable groups and tables to develop the chances of further analysis. Therefore, for this reason, the researcher should classify the collected raw data into some purposeful and usable categories. Usually at this stage, the Coding operation is conducted, which transforms the categories of data into symbols that may be arranged in a tabular form and then counted.


The quality of the data is improved and polished by the procedure of editing. The thus improved data is then coded, and is made ready for tabulation. Tabulation is that part of the technical procedure where the classified data are arranged in the form of tables. Computers are a great help in this area of research. Especially in the cases of large inquiries, a great deal of data is tabulated by the computers. This procedure not only helps in saving time, but also makes it possible to study a large number of variables affecting a problem simultaneously. After the process of tabulation, the analysis work is generally done by the computation of various percentages, coefficients, etc., by employing various well-defined statistical formulae.


During the process of data analysis, relationships or differences supporting/conflicting with the original or new hypotheses should be subjected to tests of significance, in order to determine with what validity the data can be said to indicate any conclusion/s. For example, take the samples of two monthly wages, each of the samples being drawn from the companies located in different parts of the same town. Now, if the samples give two different mean values, then our problem will be concerned about whether the two mean values are genuinely different, or the difference is just a matter of chance. By using the methods of statistical tests, we can find whether such a difference is an authentic one, or is merely the result of some random fluctuations. If the difference was found to be valid, then it can be concluded that the two different samples came from two different universes. On the other hand, if the difference was found to be due to by chance, then it can be concluded that the two different samples came from the same universe. Similarly, the technique of variance analysis can help us to analyse whether three or more varieties of plants, harvested on specific fields, yield considerably different results or not. Hence, we can conclude that the researcher can analyse his/her collected data with the help of various statistical measures. Now it is up to the researcher, which analysis technique will he/she find most appropriate for his data analysis.


Collecting the Data

Collecting data forms a key aspect of any type of research study. Data are mainly collected to obtain information regarding a specific topic. These data can be documented for future use, can be shared as information, and help in making decisions about important issues. Inaccurate data collection can have a negative impact on the results of a research study, and eventually make the study invalid. The primary data that is collected should be relevant to the study and research problem. Primary data can be collected either through experiments or through surveys.


In this, an independent variable is changed or manipulated to see how it affects a dependent variable, keeping in control the effects of some extraneous variables. Here, the emphasis is on specific hypotheses about the influence of one variable over another. There are two types of experiments:

  • Laboratory Experiments: Here the variables are manipulated and measured in an artificial setting.
  • Field Experiments: Here the variables are manipulated and measured in a natural setting.


Surveys are generally used to know about the trends in opinions, experiences, and behavior of people. It includes the following methods:

  • Observation: It is a fundamental and highly important method in all qualitative inquiry. In this case, the researchers take note of peoples behavior, objects, etc. through their own investigations without interviewing or communicating with them. Observation as a method includes both seeing and hearing. The obtained data is relevant to the present only. It is not complicated by the past behavior or future attitudes of the participants. But, this method has its limitations. It can be used only when there are fewer participants. Also, the information gathered is very limited.
  • Interviews: This is particularly used when detailed information is required from certain people. The one-to-one interviews yield the highest response rates in survey research. To get the best results, the researcher needs to establish rapport with potential participants by gaining their confidence. The researcher first puts forth a few general topics to uncover the participants views, and then goes ahead with systematic questioning pertaining to the research topic. It depends a lot on the skills of the interviewer. But, these interviews can yield biased results also: the interviewer may misinterpret some response; the interviewee may not give his/her true opinion or avoid difficult questions; the interviewer might unintentionally provoke the interviewee; the surroundings might be creating discomfort to the interviewee, etc. It is also very time consuming. This method includes two types of interviews: Personal and Telephonic.
  • Questionnaires: A questionnaire is a set of systematically structured questions used by a researcher to obtain the required information from the participants. It may include check lists, attitude scales, projective techniques, rating scales and a variety of other research methods. Questionnaires can be paper-based or electronic. Through this method, accurate and relevant data can be obtained very quickly and easily. Participants feel free to respond as they remain anonymous. But, at the same time, data processing and analyzing for large number of responses can be time consuming.
  • Schedules: Schedule is a set of questions, which are asked and filled by the interviewer or enumerators in a face-to-face situation. The specially appointed enumerators go to the respondents, put forward their questions and record their responses. They also explain the objective of the research, and clear doubts regarding the questions, if any. This method is very useful for extensive enquiries. It is usually adopted by governmental agencies or big organizations. Population census is usually done through this method.

Types of Sampling

Sampling can be basically categorized into probability and non-probability sampling. In probability sampling, each and every element of the population has a probability of being selected in the sample, i.e., the probability can be accurately measured. Whereas, in non-probability sampling, not all elements have a chance of being selected in the sample, i.e., their probability cannot be accurately measured.

The commonly used sampling methods are given below:

»  Deliberate sampling: It is a non-probability sample design in which the researcher purposively or deliberately selects certain units of the universe to form a sample that would represent the universe. In other words, it is a sampling with a purpose. It is also known as purposive sampling.


»  Simple random sampling: It is a probability sample design where each and every element has an equal probability of being selected in the sample. It is also known as chance sampling.


»  Systematic sampling: In this method, elements from a large population are selected at periodic intervals according to a random starting point, i.e., every nth element is selected for the sample, where n can be any random position of an element.


»  Stratified sampling: In this method, the researcher divides the entire population into different subgroups or strata, and then randomly selects elements proportionally from the strata to include in the sample.


»  Quota sampling: It is a non-probability sample in which the researcher selects random units for a sample according to certain given criteria or quota. In other words, elements are selected according to pre-specified criteria in such a way that the sample represents the same characteristics of the population under study.



A sample is a small portion or unit of something that is measured to make a general statement about the whole thing. Technically speaking, items that researchers collect for performing their research work are known as samples. These units or samples represent the larger group from which they are selected. The basic process of selecting samples is called sampling. Sampling makes studying different characteristics of a large, heterogeneous population possible. It reduces the study population to a reasonable size, thus, greatly reducing the research expenses and speeding up the research process. It helps to finish any research within a reasonable period of time. Sampling provides true, valid, accurate and reasonable data. It also saves the consumption of all data resources. Sampling targets a specific small population and provides precise and typically accurate results.


Conceptual vs. Empirical Research

Conceptual research is that related to some abstract idea(s) or theory. It is generally used by philosophers and thinkers to develop new concepts or to reinterpret existing ones. On the other hand, empirical research relies on experience or involves observation alone, often without due regard for system and theory. It is a data-based research, with analyses coming up with conclusions, which are capable of being verified by observation or experiment. We can also call it as ‘experimental type of research. In such a research it is necessary to get at facts at the firsthand, at their source, and actively to go about doing certain things to usually stimulate the production of the desired information. In such a research, the researcher must first provide himself with a working hypothesis or guess as to the probable results. He then works to get enough facts (data) to prove or disprove his hypothesis. He then sets up his experimental designs, which he thinks will manipulate the persons or the materials concerned so as to bring forth the desired information. Such research is thus, characterized by the experimenter’s control over the variables under study and his deliberate manipulation of one of them to study its effects. Empirical research is appropriate when proof is sought that certain variables affect other variables in some way. Evidence gathered through experiments or empirical studies is today considered to be the most powerful support possible for a given hypothesis.


Abstract writing style

     You should maintain certain writing standards while drafting your research paper. Your abstract should   be as brief as possible but quite meaningful. Here are some common writing styles for your abstract.

  • Your abstract should be a single paragraph, and concise
  • As a summary of work done, it is always written in past tense
  • An abstract should stand on its own, and not refer to any other part of the paper such as a figure or table
  • Focus on summarizing results – limit background information to a sentence or two, if absolutely necessary
  • What you report in an abstract must be consistent with what you reported in the paper
  • Corrrect spelling, clarity of sentences and phrases, and proper reporting of quantities (proper units, significant figures) are just as important in an abstract as they are anywhere else

Writing an Abstract

Write your summary after the rest of the paper is completed. After all, how can you summarize something that is not yet written? Economy of words is important throughout any paper, but especially in an abstract. However, use complete sentences and do not sacrifice readability for brevity. You can keep it concise by wording sentences so that they serve more than one purpose. For example, “In order to learn the role of protein synthesis in early development of the sea urchin, newly fertilized embryos were pulse-labeled with tritiated leucine, to provide a time course of changes in synthetic rate, as measured by total counts per minute (cpm).” This sentence provides the overall question, methods, and type of analysis, all in one sentence. The writer can now go directly to summarizing the results.

Summarize the study, including the following elements in any abstract. Try to keep the first two items to no more than one sentence each.

  • Purpose of the study – hypothesis, overall question, objective
  • Model organism or system and brief description of the experiment
  • Results, including specific data – if the results are quantitative in nature, report quantitative data; results of any statistical analysis shoud be reported
  • Important conclusions or questions that follow from the experiment(s)