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Free download of Business Research Methodology by SRINIVAS R RAO. Available in PDF, ePub and Kindle. Read, write reviews and more. As of today we have 77,, eBooks for you to download for free. No annoying ads, no Research Methodology, A step-by-step guide for beginners. Summary. 18 Research methodology and practice evaluation . I have taken a very bold step in breaking down, where possible, the wall between qualitative and .. The process of investigation must be foolproof and free from any drawbacks.

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Research Methods: The Basics is an accessible, user-friendly introduction to the different collection of thousands of eBooks please go to www. .. Important is that the observations are written down or recorded in .. and value free data. No part of this ebook may be reproduced in any form, by photostat, microfilm, xerography, or . Research Methodology in all disciplines of various universities. It is hoped that . Characteristics of Distribution-free or Non-parametric Tests The Advanced Learner's Dictionary of Current English lays down the meaning of. [lyubimov.info55] Research Methodology Research Methodology lyubimov.inforthy epub. Research Methodology lyubimov.inforthy pdf download. Research.

Toggle navigation. New to eBooks. How many copies would you like to buy? Research Methodology 2nd ed. Methods and Techniques by C.

Add to Wishlist Add to Wishlist. About the Book: This second edition has been thoroughly revised and updated and efforts have been made to enhance the usefulness of the book. In this edition a new chapter The Computer: Its Role in Research have been added keeping in view of the fact that computers by now become a indispensable part of research equipment. The other salient feature of this revised edition, subject contents have been developed and restructured at several places.

New problems have also been added in various chapters. Adoption of appropriate methodology is an essential characteristic of quality research studies irrespective of the discipline with which they are related. The present book provides the basic tenets of methodological research so that researchers may become familiar with the art of using research methods and techniques.

The book contains introductory explanations of several quantitative methods enjoying wide use in social sciences. It covers a fairly wide range, related to Research Methodology.

The presentations are uniformly economical and cogent. Illustrations given are meaningful and relevant. Cumming, G. Cough that shook the world. New Zealand Herald, p. Here the. The article title which is not in italics follows immediately. This is followed by th e.

Newspaper name in italics and the page before the page number. This is used for newspapers. Use p for 1 page, pp. If page numbers are. A1, A Newspaper in electronic version: If the newspaper is in electronic form the citation in both the.

Inside the text, the citation as in printed version follows the style shown below: At the reference section, the citation follows similar style as in printed version. The style is as shown below:. New Zealand Herald. The URL of the homepage of the newspaper is used as a direct link. This is bec ause website is not a persistent link. Newspaper without author: Some articles in a newspaper do not have authors, in such case, the.

Inside the text, the title of the article is used in place of the author with a date. Here, the title is abbreviated; double quotation marks are. At the reference section, the c itation follows the style below: Drivers re ject fuel prices driven by war threat. Timaru Herald, p. Here ,. The date which i ncludes the month and.

This is followed by th e newspaper title in italics and the page. However, it is important to note that before submitting a work to a publisher, check the. A statistical hypothesis is a scientific hypothesis that is testable on the basis of observing a.

It is a method of statistical inference. A test result is said to be statistica lly significant if it has been predicted as unlikely to have. Hypothesis tests are used in determining what outcomes of a study would lead to a.

I n the. Neyman-Pearson framework the process of distinguishing between the null hypothesis and the. In making decisions on the basis of a sample , two errors are possible. We may reject H. When this happens, we. Secondly, we may accept H. This aga in is called type II error.

Research Methodology (2nd ed.)

A t est of hypothesis is considered good if both errors of. This is not always possible. However in any particular situation, it is. Suppose we have the null hypothesis that a particular drug is not poisonous, i. Type I error: Reject H. This means that we assume the drug to be. Here we tr y to minimize type II error and allow t ype I. In testing a given hypothesis, the maximum probability with w hich we would be willing to risk, a.

We summarize this. The curve below is meant for tests, involving normal. Statistical Estimation. However, if on choosing a single sample at random, it is found that Z is outside the unshaded. Thus we may sa y that H. The set of values of. Z outside the unshaded part constitute what is called the critical region or significance region or.

While the set of Z inside is called the region of acceptance of H. The points separating the acceptance region from critical region is called the critical point of the.

Thus the following decision rule can be formu lated:. One-tailed and two-tailed tests: If in a test of H. On the other. In two —tailed test, the area of the critical regions on each side of the distribution is. An alternative framework for statistical hypothesis testing is to specify a set of statistical models,. The most common selection techniques are based on either Akaike. Statistical hypothesis testing is sometimes called confirmatory data analysis.

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It can be. A hypothesis is a proposed explanation for a phenomenon. For a hypothesis to be a scientific. Scientists generally base scientific. Even though the words "hypothesis" and "theory" are often used.

A working. This is a measure of the relative quality of a. That is, given a collection of models for the data, AIC. Hence, A IC provides a. AIC is founded on information theory; it offers a relative estimate of the information lost when a. In doing so, it deals with the. It does not. AIC can tell nothing about.

If the entire candidate models fit poorly, A IC will. A statistical model embodies a set of assumptions concerning the generation of the observed. A model represents, often in considera bly. The model assumptions describe a set of probability.

A model is usually specified by mathematical equations that relate one or more random variables. As such, "a model is a formal re presentation of a.

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All statistical hypothesis tests and all statistical estimators are derived from statistical models. More generally, statistical models are part of the founda tion of statistical inference. Statistics, like all mathematical disciplines, does not infer valid conclusions from nothing. Inferring interesting conclusions about real statistical populations usually requires some.

Those assumptions must be made carefully, because incorrect. Here are some examples of statistical assumptions. Independence of observations from each other this assumption is an especially common. Independence of observational error from potential confound ing effects. Exact or approximate normality of observations.

Linearity of graded responses to quantitative stimuli, e. There are two approaches to statistical inference they are the model-based inference and the. Both approaches rely o n some statistical model to represent the data-.

In the model-based approach, the model is taken to be initially unknown, and. In the design-based approach, the. Statistical assumptions can be put into two classes, depending upon which approach the.

Model-based assumptions. These include the following thre e types:. Distributional assumptions. Where a statistical model involves terms relating to. In some cases, the distributional assumption relates to the.

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Structural assumptions. Statistical relationships between variables are often. Models often involve making a structural assumption about the.

This can be. Cross-variation assumptions. These assumptions involve the joint probability. Simple models may include the assumption that observations or errors are. Design-based assumptions. These relate to the way observations have been gathered, and. As far as statistical analysis is concerned, the confidence interval completely summarizes all the.

It enables us to make probability statement about the. Apart from giving the most likely values of the parameter, the interval can. Do you think that the ages of the ten boys above are likel y to come from a population of students. Thus, we can say that the. Sampling is concerned with the selection of a subset of individuals fr om within a defined.

Each individual variable measures. In survey sampling, weights can be applied to the data to. Results from. In all fields of research,. The sampling process comprises several stage s:. Specifying a sampling method for selecting items or e vents from the frame. In statistics, a simple random sample is a subset of individuals a sample chosen from a larger. Each individual is chosen randomly and entirely by chance hence it is.

This process and. A simple random sample is an unbiased surveying technique. Saul McLeod,. Simple random sampling is a basic type of sampling, since it can be a component of other more. The principle of simple random sampling is that every elementar y. For example, suppose N university students want.

Then, everybody is given a number in the range from 0. Numbers outside the range from 0 to N-1 are ignored, as are an y numbers previously selected. The first X numbers would identify the luc ky candidate to collect a gate pass. In small populations and often in large ones, such sampling is typically done " without.

Although simple random sampling can be conducted with replacement instead, this is less. Sampling done without replacement is no longer independent, but still satisfies. Further, for a small sample from a large population, sampling without replacement is.

An unbiased random selection of individuals is important so that if a large number of samples. However, this does. Conceptually, simple random sampling is the simplest of the probability sampling techniques. Even if a complete frame is available, more efficient approaches may be possible if. Advantages are that it is free of classification error, and it requires minimum advance knowledge. Its simplicity also makes it relatively eas y to interpret. For these reasons, simple random sampling best suits situations.

If these conditions do not hold,. Table of Random Nu mbers: A table of random number is speciall y prepared for obtaining. To use this table all the units i n the population are first of all numbered as. The table is then use d to pick the desired number of units. Suppose it is required to take a random sample of 5 students from a school of 99 students.

Since the number of students in the. We then take. Any time we come across a different number between 01 and 99 we write it down. A frame is the list of all the population units from which the sample units are identified. The units are called sampling units.

I n the above example of 5 students, the school list is the. Note the difference between samples units and sampling units. The former are the objects. Common examples of frame are;. The most common form of systematic sampling is an equal-probability. In this approach, progression through t he list is treated circularly, with a r eturn to the. The sampling starts by selecting an element from the list at. Where n is the sample size, and N is the popu lation size. Using this procedure each element in the population has a known and equal probability of.

This makes systematic sampling functionally similar to simple random sampling. Systematic sampling is to be applied only if the given population is logically homogeneous,. The researcher. Any pattern would. Suppose a sup ermarket wants to stud y buying habits of their customers, then using. This is random sampling with a system. From the sampling frame, a starting point is chosen at. For example, suppose you want to sample. If the random starting point is 11, then the houses selected are.

As an aside, if every 15th house was a "corner house" then. If, as more frequently, the popu lation is not evenly divisible suppose you want to sa mple 8. On the other hand, if you take every 15th h ouse, 8 multiplied by 15 equals , so the last. The random starting point should instead be selected as a non. If the random starting point is 3. To illustrate the danger of systematic skip concealing a pattern, suppose we were to sample a.

This places houses No. If we then sample. Systematic sampling may also be used with non-equal selection probabilities. In this case, rather. We then generate. We have a population of 5 units A to E. Assuming we maintain.

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If our random start was 0. Next, we would select the interval containing 1. If instead our random start was 0. Consider a school with students, divided equally into boys and girls, and suppose that a. All their names might be put in a bucket. Not only does each person have an equal chance o f.

In the case that any g iven person can only be selected once i. In the case that any sel ected person is returned to the selection pool i. This means that every student in the school has in any case approximately a 1 in 10 chance of.

Further, all combinations of stu dents have the sa me. If a systematic pattern is introduced into random sampling, it is referred to as "systematic. An example would be if the students in the school had numbers attached to. In this sense, this technique is similar to cluster sampling, since the choice. This is no longer simple random sampling, because. In statistics, survey sampling describes the process of selecting a sample of elements from a. The term "survey" m ay refer to m any different types or.

In survey sampling it most often involves a questionnaire used to. Different ways of contacting members of a. The purpose of. A survey that measures the entire tar get population is called a census or. Survey samples can be broadly divided into two t ypes: Probability-based samples implement a sampling plan with specified probabilities. Probability-based sampling. The inferences are based on a known.

Inferences from. The following points should. Surveys that are not based on probability sampling have greater difficulty measuring their. Surve ys based on non-probability samples often fail to represent. In academic and government survey research, probability sampling is a standard. Selecting samples using generally accepted statistical methods e. Any use of non probability. Besides, random sampling and design-based inference are supplemented by other. For example, many surveys have substantial amounts of non response.

Even though the. For surveys with substantial non response, statisticians have proposed. The model-based approach is much the most commonly used in statistical inference; the d esign-.

With the model-based approached, all the. In statistical surveys, when. Stratification is the process of. The strata. The strata should also be collectively exhaustive: Then simple random sampling or systematic sampling is applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. It can. In computational statistics, stratified sampling is a method of varianc e r eduction when Monte. Carlo methods are used to estimate population statistics f rom a known population.

Proportionate allocation uses a sampling fraction in each of the strata that is proportional. For instance, if the population X consists of m in the male. Optimum allocation or disproportionate allocation - Each stratum is proportionate to the. Larger samples are taken in the. Stratified sampling ensures that at l east one observation is picked from each of the strata, even if. Hence the statistical properties of the population.

A rule of thumb that is used to ensure this is that the. A real-world example of using stratified sampling would be for a political survey. If the. A stratified survey could thus claim. Below are some of the advantages a nd disadvantages of stratified sampling. If population density varies greatly within a region, stratified sampling will ensure that estimates. For example, in Ontar io a survey taken. Randomized stratification can also be used to improve population representativeness in a study.

Stratified sampling is not useful when the population ca nnot be exhaustively partitioned into. It would be a misapplication of the technique to make subgroups' sample. The problem of stratified sampling in the case of unknown class priors ratio of. In that regard, minimax sampling ratio can be used to. In general the size of the sample in each stratum is taken in proportion to the size of the stratum.

This is called proportional allocation. Suppose tha t in a company, the following staff exists:. If we are required to take a sample of 40 staff, stratified according to the above categories; the. Another easy way without having to calculate the percentage is to multiply each group size b y.

Cluster Sampling: In many situations the population units belong to some natural group. These natural groups consisting of the units of interest are called Clusters. A sample of. Multi-Stage Sampling: Suppose a study is carried out which will use the all the states in the. The investigator may first randomly select six states 1.

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This is. He may decide to have 3-stage b y going to zones. Notice that each stage. Any sampling procedure where chance devices are not used to select the required sample. Examples of non-random sampling scheme a re:. Systematic Sampling: This sampling procedure as earlier discussed is the one in which the. Example, instead of selecting a. The main disadvantage of this is that one ma y end up having units that resemble each.

In practice,. Quota Sampling: Here one attempts to represent different classes that may exist in a given. It is commonly used in public opinion and market research surveys. In such surveys,. The big difference between. I n stratified.

With quota sampling the se units are not known in advance.

Research Methodology (2nd ed.) by C.R. Kothari (ebook)

Haphazard Sampling: Here the selector thinks that he is making a random selection. A good. Press agents. Business promoters. Who interview people anyhow, seeking their views about something that is of interest to them?

No chance device is however used in this type of selection. You can think of many possible. In any study, an investigator may have a choice of collecting the relevant data himself or of.

The former is called the. Primary Sources while the latter is called Secondary Sources of data. For example, in a stud y. However, if schools. Whether primary or secondary, data may be published or unpublished. Data in their original form such as number of births, and deaths in a locality, names of.

These departments. To obtain data from these sources require a. The following are the main sources of published data:. A data here. The data here may or may be reliable; hence caution is re quired in using them. The following consideration may be borne in mind w hen embarking on any data collection. Some of the sampling techniques will be treated later in the text.

Errors come into collection of data, for instance, when the wrong type of data not suitable. Bias is introduced, for example , when the. Such knowledge and the above criteria should enable him to decide on.

Igwenagu, The guidelines listed above come for consideration at various stages of an investigation. A survey is the observation of either the entire population this is known as census , or the.

We can recognize the. In surveys, the investigator c annot control any of the irrelevant factors. In trying to collect data, either through experiments or surveys, the following stages should be. Before starting to collect data, th e investigator must first of all determine the problem to. As an illustration of problem statement, suppose a government Ministry of Education is. Before making. If all the above facts are not we ll known we might de cide to find out more about them.

To find out the present level of pocket money among boarding school children, how. With the problem clearly stated, we can now c onsider the next stage. Planning the Study: He has to plan how best. In fact his plan will include. Areas to be c overed and types of objec t to be observed are determined. If it is finite, the investigator will.

Of course, there are obvious. For instance, cost of collecting data is reduced, time spent. Greater accuracy in the data is ensured. Since a few observations are. Search for Available Information: This information may exist in the form of written reports. It enables the investigator to know what is available and. It also helps him to learn from mistakes or constraints of the previous. Recording of the Data: The investigator at this stage will have to decide how the data are to b e. A questionnaire consists of questions and answers.

It is usually used when studying human being. The questions and answers may be written or oral. When oral answers. Written questions can be sent b y mail or.

Both me thods have their merits and demerits. The merits of. On the other hand, mailed questionnaire ha s got its disadvantage. Some of which are:. In the case of using interviewer to collect data directly, g reater returns are assured, especially if. The interviewer has the opportunity of explaining difficult questions. On the other hand,. In the direct observation. This is commonly. It is worthwhile top. Pilot Survey: In some cases the investigator m ay dis cover that the available information is so.

It b ecomes desirable then to carry out the preliminary study. Such a study is called Pilot Survey or Study and it is normally done on a. The variation may e nable the investigator to determine the t ype of sampling method and the. If we pretest our questionnaire, on a few respondents, any unsuspected. Designing the Study: After the pl anning and possibly a pilot survey, the following final touches. Discussion on. A sample questionnaire will be provided later.

Experimental procedures will include the dec ision on:. Observation units are identifiable physical entities on w hich a variable s is measured. In the study of heights and weights of school children, t he children themselves are the. To minimize biases. Many examples of the.

In the former, plots of lands are the. Simil arly in the later, patients are the observation units wh ile the drugs constitute the. The following terms are commonly used in questionnaire: He can help to promote coll ection of data by co-.

An observation unit, which can be defined a s an identifiable physical entit y on which. One or. For example, in the pocket money sur vey, the. However, in a study to find out the most popular brand of baby milk among babies of a. But the respondent must necessarily be. Having selected the topics to be included in the survey, we must formulate questions to cover. The number, order and type of questions constitute the main elements of design of a.

The following are the guidelines for the de signing of a good questionnaire:. The number of questions depends on the number of topics or variable.

Ideally they should be as few as possible. Too many questions, resulting in a. Repetition of questions should be. A well-arranged and wel l-ordered sequence. Simple, clear and unambiguous questions are more likely to have. For example, a study in. Divorcees and widowers are left out. As an. Where difficult. For instance, in a study involving families,. Does it consist of man, his wife, his children or does it. Questions leading to definite answers are to. A question framed in such.

These questions lead to the answers desired b y. They are meant for lawyers in. These are personal and. Igwena gu Opcit. Accessed 23 February, Accessed 23rd February, Simple Random Sampling and other Sampling Methods. Sta tistical Concepts and Reasoning.

James La ni. Research Methodology: An Introduction Google A ccessed. Howell, K. Irny, S. Katsicas, Sokratis K. In Vacca, John. Compute r and Information. Security Handbook. M organ Kaufmann Publications. Elsevier Inc. ISBN Lodico, Marguerite G. Methods in. Educational Research: From Theory to Practice. Igwenagu C. M Collection of Data. Basic Statistics and Probability First Ed. Basic Statistics and Pro bability Anambra State,. Citations 2. References To define and categorize research methods, we first distinguish the key concepts of research methodology, approach, methods, and techniques, and then elucidate the process and criteria of scientific research.

Walliman, , research methodology is the philosophical framework within which the research is conducted or the foundation upon which the research is based. It is a way to systematically solve the research problem. Apr TIAN De-xin. Research Proposal. Full-text available. Feb An Introduction to the Philosophy of Methodology. Jan A draft methodology was documented through an analysis synthesis of existing SISP methodologies and assessed by SISP experts based on the criteria of suitability, accuracy, understandability, usability and completeness.

Methods in Educational Research: Dissertation workshop. Mar James Lani. C Igwenagu. Chapter 35 Computer and Information Security Handbook. Sokratis K Katsicas. Computer and Information Security Handbook. Morgan Kaufmann Publications. Sampling Methods http: