الجمعة، 10 أكتوبر 2014

Research Methodology

Research Methodology

Research Methods versus Methodology

It seems appropriate at this juncture to explain the difference between research methods and research methodology. Research methods may be understood as all those methods/techniques that are used for conduction of research. Research methods or techniques, thus, refer to the methods the researchers use in performing research operations. In other words, all those methods which are used by the researcher during the course of studying his research problem are termed as research methods. Since the object of research, particularly the applied research, it to arrive at a solution for a given problem, the available data and the unknown aspects of the problem have to be related to each other to make a solution possible. Keeping this in view, research methods can be put into the following three groups:

1.     In the first group we include those methods which are concerned with the collection of data. These methods will be used where the data already available are not sufficient to arrive at the required solution;
2.     The second group consists of those statistical techniques which are used for establishing relationships between the data and the unknowns;
3.     The third group consists of those methods which are used to evaluate the accuracy of the results obtained.

It may be understood as a science of studying how research is done scientifically. In it we study the various steps that are generally adopted by a researcher in studying his research problem along with the logic behind them. It is necessary for the researcher to know not only the research methods/techniques but also the methodology. Researchers not only need to know how to develop certain indices or tests, how to calculate the mean, the mode, the median or the standard deviation or chi-square, how to apply particular research techniques, but they also need to know which of these methods or techniques, are relevant and which are not, and what would they mean and indicate and why. Researchers also need to understand the assumptions underlying various techniques and they need to know the criteria by which they can decide that certain techniques and procedures will be applicable to certain problems and others will not. All this means that it is necessary for the researcher to design his methodology for his problem as the same may differ from problem to problem. For example, an architect, who designs a building, has to consciously evaluate the basis of his decisions, i.e., he has to evaluate why and on what basis he selects particular size, number and location of doors, windows and ventilators, uses particular materials and not others and the like. Similarly, in research the scientist has to expose the research decisions to evaluation before they are implemented. He has to specify very clearly and precisely what decisions he selects and why he selects them so that they can be evaluated by others also.

From what has been stated above, we can say that research methodology has many dimensions and research methods do constitute a part of the research methodology. The scope of research methodology is wider than that of research methods. Thus, when we talk of research methodology we not only talk of the research methods but also consider the logic behind the methods we use in the context of our research study and explain why we are using a particular method or technique and why we are not using others so that research results are capable of being evaluated either by the researcher himself or by others. Why a research study has been undertaken, how the research problem has been defined, in what way and why the hypothesis has been formulated, what data have been collected and what particular method has been adopted, why particular technique
of analysing data has been used and a host of similar other questions are usually answered when we talk of research methodology concerning a research problem or study.

The chart indicates that the research process consists of a number of closely related activities,
As shown through I to VII. But such activities overlap continuously rather than following a strictly prescribed sequence. At times, the first step determines the nature of the last step to be undertaken. If subsequent procedures have not been taken into account in the early stages, serious difficulties may arise which may even prevent the completion of the study. One should remember that the various steps involved in a research process are not mutually exclusive; nor they are separate and distinct. They do not necessarily follow each other in any specific order and the researcher has to be constantly anticipating at each step in the research process the requirements of the subsequent steps. However, the following order concerning various steps provides a useful procedural guideline regarding the research process: (1) formulating the research problem; (2) extensive literature survey; (3) developing the hypothesis; (4) preparing the research design; (5) determining sample design; (6) collecting the data; (7) execution of the project; (8) analysis of data; (9) hypothesis testing; (10) generalisations and interpretation, and (11) preparation of the report or presentation of the results, i.e., formal write-up of conclusions reached. A brief description of the above stated steps will be helpful.

1. Formulating the research problem:

There are two types of research problems, viz., those which relate to states of nature and those which relate to relationships between variables. At the very outset the researcher must single out the problem he wants to study, i.e., he must decide the general area of interest or aspect of a subject-matter that he would like to inquire into. Initially the problem may be stated in a broad general way and then the ambiguities, if any, relating to the problem be resolved. Then, the feasibility of a particular solution has to be considered before a working formulation of the problem can be set up. The formulation of a general topic into a specific research problem, thus, constitutes the first step in a scientific enquiry. Essentially two steps are involved in formulating the research problem, viz., understanding the problem thoroughly, and rephrasing the same into meaningful terms from an analytical point of view. The best way of understanding the problem is to discuss it with one’s own colleagues or with those having some expertise in the matter. In an academic institution the researcher can seek the help from a guide who is usually an experienced man and has several research problems in mind. Often, the guide puts forth the problem in general terms and it is up to the researcher to narrow it down and phrase the problem in operational terms. In private business units or in governmental organisations, the problem is usually earmarked by the administrative agencies with whom the researcher can discuss as to how the problem originally came about and what considerations are involved in its possible solutions.

The researcher must at the same time examine all available literature to get himself acquainted with the selected problem. He may review two types of literature—the conceptual literature concerning the concepts and theories, and the empirical literature consisting of studies made earlier which are similar to the one proposed. The basic outcome of this review will be the knowledge as to what data and other materials are available for operational purposes which will enable the researcher to specify his own research problem in a meaningful context. After this the researcher rephrases the problem into analytical or operational terms i.e., to put the problem in as specific terms as possible. This task of formulating, or defining, a research problem is a step of greatest importance in the entire research process. The problem to be investigated must be defined unambiguously for that will help discriminating relevant data from irrelevant ones. Care must, however, be taken to verify the objectivity and validity of the background facts concerning the problem. Professor W.A. Neiswanger correctly states that the statement of the objective is of basic importance because it determines the data which are to be collected, the characteristics of the data which are relevant, relations which are to be explored, the choice of techniques to be used in these explorations and the form of the final report. If there are certain pertinent terms, the same should be clearly defined along with the task of formulating the problem. In fact, formulation of the problem often follows a sequential pattern where a number of formulations are set up, each formulation more specific than the preceeding one, each one phrased in more analytical terms, and each more realistic in terms of the available data and resources.

2. Extensive literature survey:

Once the problem is formulated, a brief summary of it should be written down. It is compulsory for a research worker writing a thesis for a Ph.D. degree to write a synopsis of the topic and submit it to the necessary Committee or the Research Board for approval. At this juncture the researcher should undertake extensive literature survey connected with the problem. For this purpose, the abstracting and indexing journals and published or unpublished bibliographies are the first place to go to. Academic journals, conference proceedings, government reports, books etc., must be tapped depending on the nature of the problem. In this process, it should be remembered that one source will lead to another. The earlier studies, if any, which are similar to
the study in hand should be carefully studied. A good library will be a great help to the researcher at this stage.

3. Development of working hypotheses:

After extensive literature survey, researcher should state in clear terms the working hypothesis or hypotheses. Working hypothesis is tentative assumption made in order to draw out and test its logical or empirical consequences. As such the manner in which research hypotheses are developed is particularly important since they provide the focal point for research. They also affect the manner in which tests must be conducted in the analysis of data and indirectly the quality of data which is required for the analysis. In most types of research, the development of working hypothesis plays an important role. Hypothesis should be very specific and limited to the piece of research in hand because it has to be tested. The role of the hypothesis is to guide the researcher by delimiting the area of research and to keep him on the right track. It sharpens
his thinking and focuses attention on the more important facets of the problem. It also indicates the type of data required and the type of methods of data analysis to be used. How does one go about developing working hypotheses? The answer is by using the following approach: (a) Discussions with colleagues and experts about the problem, its origin and the objectives in seeking a solution; (b) Examination of data and records, if available, concerning the problem for possible trends, peculiarities and other clues; (c) Review of similar studies in the area or of the studies on similar problems; and (d) Exploratory personal investigation which involves original field interviews on a limited scale with interested parties and individuals with a view to secure greater insight into the practical aspects of the problem. Thus, working hypotheses arise as a result of a-priori thinking about the subject, examination of the available data and material including related studies and the counsel of experts and interested parties. Working hypotheses are more useful when stated in precise and clearly defined terms. It may as well be remembered that occasionally we may encounter a problem where we do not need working hypotheses, specially in the case of exploratory or formulative researches which do not aim at testing the hypothesis. But as a general rule, specification of working hypotheses in another basic step of the research process in most research problems.

4. Preparing the research design:

The research problem having been formulated in clear cut terms, the researcher will be required to prepare a research design, i.e., he will have to state the conceptual structure within which research would be conducted. The preparation of such a design facilitates research to be as efficient as possible yielding maximal information. In other words, the function of research design is to provide for the collection of relevant evidence with minimal expenditure of effort, time and money. But how all these can be achieved depends mainly on the research purpose. Research purposes may be grouped into four categories, viz., (i) Exploration, (ii) Description,
(iii) Diagnosis, and (iv) Experimentation. A flexible research design which provides opportunity for considering many different aspects of a problem is considered appropriate if the purpose of the research study is that of exploration. But when the purpose happens to be an accurate description of a situation or of an association between variables, the suitable design will be one that minimises bias and maximises the reliability of the data collected and analysed. There are several research designs, such as, experimental and non-experimental hypothesis testing. Experimental designs can be either informal designs (such as before-and-after without control, after-only with control, before-and-after with control) or formal designs (such as completely randomized design, randomized block design, Latin square design, simple and complex factorial designs), out of which the researcher must select one for his own project. The preparation of the research design, appropriate for a particular research problem, involves usually the consideration of the following: (i) the means of obtaining the information; (ii) the availability and skills of the researcher and his staff (if any); (iii) explanation of the way in which selected means of obtaining information will be organized and the reasoning leading to the selection; (iv) the time available for research; and (v) the cost factor relating to research, i.e., the finance available for the purpose.

5. Determining sample design:


What is a sample?

A sample is a finite part of a statistical population whose properties are studied to gain information about the whole (Webster, 1985). When dealing with people, it can be defined as a set of respondents (people)  selected from a larger population for the purpose of a survey.

What is a population?

A population is a group of individual persons, objects, or items from which samples are taken for measurement for example a population of students, teachers, trainees, examination papers, textbooks, etc..

What is sampling?

Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters (things that decide or limit the way in which something can be done) or characteristics of the whole population.

What is the purpose of sampling?

To draw conclusions about populations from samples, we must use inferential statistics which enables us to determine a population`s characteristics by directly observing only a portion (or sample) of the population.

We obtain a sample rather than a complete enumeration (a census) of the population for many reasons. Obviously, it is cheaper to observe a part rather than the whole, but we should prepare ourselves to cope with the dangers of using samples. 

Types of samples

There are three primary kinds of samples: the convenience sample, the judgement sample, and the random sample. They differ in the manner in which the elementary units are chosen.

The convenience sample

A convenience sample results when the more convenient elementary units are chosen from a population for observation.

The judgement sample

A judgement sample is obtained according to the discretion of someone who is familiar with the relevant characteristics of the population.

The random sample

This may be the most important type of sample. A random sample allows a known probability that each elementary unit will be chosen. For this reason, it is sometimes referred to as a probability sample. This is the type of sampling that is used in lotteries and raffles. For example, if you want to select 10 teachers randomly from a population of 100, you can write their names, fold them up, mix them thoroughly then pick ten. In this case, every name had any equal chance of being picked. Random numbers can also be used.


A simple random sample

A simple random sample is obtained by choosing elementary units in search a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias.  However, using a random number table to choose the elementary units can be cumbersome. If the sample is to be collected by a person untrained in statistics, then instructions may be misinterpreted and selections may be made improperly. Instead of using a list of random numbers, data collection can be simplified by selecting say every 10th or 100th unit after the first unit has been chosen randomly. Such a procedure is called systematic random sampling.

A systematic random sample

A systematic random sample is obtained by selecting one unit on a random basis and choosing additional elementary units at evenly spaced intervals until the desired number of units is obtained. For example, there are 100 students in your class. You want a sample of 20 from these 100 and you have their names listed on a piece of paper may be in an alphabetical order. If you choose to use systematic random sampling, divide 100 by 20, you will get 5. Randomly select any number between 1 and five. Suppose  the number you have picked is 4, that will be your starting number. So student number 4 has been selected. From there you will select every 5th name until you reach the last one, number one hundred.  You will end up with 20 selected students

A stratified sample

A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into different groups may be based on some characteristic or variable like income or education. Like anybody with ten years of education will be in  group A, between 10 and 20 group B and between 20 and 30 group C. These groups are referred to as strata. You can then randomly select from each stratum a given number of units which may be based on proportion like if group A has 100 persons while group B has 50, and C has 30 you may decide you will take 10% of each. So you end up with 10 from group A, 5 from group B and 3 from group C.

A cluster sample

A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. For example, a cluster may be something like a village or a school, a state. So you decide all the elementary schools in  Khartoum State are clusters. You want 20 schools selected. You can use simple or systematic random sampling to select the schools, then every school selected becomes a cluster. If your interest is to interview teachers on their opinion of some new textbook which has been introduced, then all the teachers in a cluster must be interviewed. Though very economical, cluster sampling is very susceptible to sampling bias. Like for the above case, you are likely to get similar responses from teachers in one school due to the fact that they interact with one another.

Purposeful sampling

Purposeful sampling selects information-rich cases for in-depth study. Size and specific cases depend on the study purpose. There are different types of purposeful sampling. Some of these are:

1.    Intensity: This method selects excellent examples of the phenomenon of interest (but not extreme examples), and allows the researcher to select a small number of cases—but it requires prior knowledge to know what to look for.
2.    Maximum Variation: This type of sampling purposely incorporates participants that exhibit wide variability on the phenomenon of interest, which allows for investigation of variables across a variety of people and situations
3.    Homogenous:  This selects a small, homogenous group of participants. This is useful for investigating a group or groups in depth.
4.    Typical Case: This is exactly what it sounds like: a sample is drawn from a group that is considered typical (average) for your variables of interest. This is useful for understanding phenomena in ordinary situations.
5.    Stratified Purposeful: For this method, samples are drawn from subgroups. For example, if you wanted to sample university students you could stratify the sample by class and then select the appropriate number of sophomores, freshmen, juniors, and seniors (Babbie, 2007). This method results in a more credible study that is representative of your population of interest.
6.    Criterion: For this method, the researcher sets some criteria (i.e., students in 3rd grade). This is useful to investigate phenomena in a specific set of people.
7.    Convenience: This is exactly what it sounds like: obtaining a sample that is easy to find. On college campuses, a convenience sample would be college undergraduates. 
8.    Combination/ Mixed Purposeful: This method combines one or more sampling techniques discussed above to allow for the best sample possible

6. Collecting the data:

In dealing with any real life problem it is often found that data at hand are inadequate, and hence, it becomes necessary to collect data that are appropriate. There are several ways of collecting the appropriate data which differ considerably in context of money costs, time and other resources at the disposal of the researcher. Primary data can be collected either through experiment or through survey. If the researcher conducts an experiment, he observes some quantitative measurements, or the data, with the help of which he examines the truth contained in his hypothesis. But in the case of a survey, data can be collected by any one or more of the following ways:

(i) By observation: This method implies the collection of information by way of investigator’s own observation, without interviewing the respondents. The information obtained relates to what is currently happening and is not complicated by either the past behaviour or future intentions or attitudes of respondents. This method is no doubt an expensive method and the information provided by this method is also very limited. As such this method is not suitable in inquiries where large samples are concerned.

(ii) Through personal interview: The investigator follows a rigid procedure and seeks answers to a set of pre-conceived questions through personal interviews. This method of collecting data is usually carried out in a structured way where output depends upon the ability of the interviewer to a large extent.

(iii) Through telephone interviews: This method of collecting information involves contacting the respondents on telephone itself. This is not a very widely used method but it plays an important role in industrial surveys in developed regions, particularly, when the survey has to be accomplished in a very limited time.

(iv) By mailing of questionnaires: The researcher and the respondents do come in contact with each other if this method of survey is adopted. Questionnaires are mailed to the respondents with a request to return after completing the same. It is the most extensively used method in various economic and business surveys. Before applying this method, usually a Pilot Study for testing the questionnaire is conduced which reveals the weaknesses, if any, of the questionnaire. Questionnaire to be used must be prepared very carefully so that it may prove to be effective in collecting the relevant information.

(v) Through schedules: Under this method the enumerators are appointed and given training. They are provided with schedules containing relevant questions. These enumerators go to respondents with these schedules. Data are collected by filling up the schedules by enumerators on the basis of replies given by respondents. Much depends upon the capability of enumerators so far as this method is concerned. Some occasional field checks on the work of the enumerators may ensure sincere work.

The researcher should select one of these methods of collecting the data taking into consideration the nature of investigation, objective and scope of the inquiry, finanical resources, available time and the desired degree of accuracy. Though he should pay attention to all these factors but much depends upon the ability and experience of the researcher. In this context Dr A.L. Bowley very aptly remarks that in collection of statistical data commonsense is the chief requisite and experience the chief teacher.

7. Execution of the project:

Execution of the project is a very important step in the research process. If the execution of the project proceeds on correct lines, the data to be collected would be adequate and dependable. The researcher should see that the project is executed in a systematic manner and in time. If the survey is to be conducted by means of structured questionnaires, data can be readily machine-processed. In such a situation, questions as well as the possible answers may be coded. If the data are to be collected through interviewers, arrangements should be made for proper selection and training of the interviewers. The training may be given with the help of instruction manuals which explain clearly the job of the interviewers at each step. Occasional field checks should be made to ensure that the interviewers are doing their assigned job sincerely and efficiently. A careful watch should be kept for unanticipated factors in order to keep the survey as much realistic as possible. This, in other words, means that steps should be taken to ensure that the survey is under statistical control so that the collected information is in accordance with the pre-defined standard of accuracy. If some of the respondents do not cooperate, some suitable methods should be designed to tackle this problem. One method of dealing with the non-response problem is to make a list of the non-respondents and take a small sub-sample of them, and then with the help of experts vigorous efforts can be made for securing response.

8. Analysis of data:

After the data have been collected, the researcher turns to the task of analyzing them. The analysis of data requires a number of closely related operations such as establishment of categories, the application of these categories to raw data through coding, tabulation and then drawing statistical inferences. The unwieldy data should necessarily be condensed into a few manageable groups and tables for further analysis. Thus, researcher should classify the raw data into some purposeful and usable categories. Coding operation is usually done at this stage through which the categories of data are transformed into symbols that may be tabulated and counted. Editing is the procedure that improves the quality of the data for coding. With coding the stage is ready for tabulation. Tabulation is a part of the technical procedure wherein the classified data are put in the form of tables. The mechanical devices can be made use of at this juncture. A great deal of data, specially in large inquiries, is tabulated by computers. Computers not only save time but also make it possible to study large number of variables affecting a problem simultaneously. Analysis work after tabulation is generally based on the computation of various percentages, coefficients, etc., by applying various well defined statistical formulae. In the process of analysis, relationships or differences supporting or conflicting with original or new hypotheses should be subjected to tests of significance to determine with what validity data can be said to indicate any conclusion(s). For instance, if there are two samples of weekly wages, each sample being drawn from factories in different parts of the same city, giving two different mean values, then our problem may be whether the two mean values are significantly different or the difference is just a matter of chance. Through the use of statistical tests we can establish whether such a difference is a real one or is the result of random fluctuations. If the difference happens to be real, the inference will be that the two samples come from different universes and if the difference is due to chance, the conclusion would be that the two samples belong to the same universe. Similarly, the technique of analysis of variance can help us in analysing whether three or more varieties of seeds grown on certain fields yield significantly different results or not. In brief, the researcher can analyse the collected data with the help of various statistical measures.

9. Hypothesis-testing:

After analysing the data as stated above, the researcher is in a position to test the hypotheses, if any, he had formulated earlier. Do the facts support the hypotheses or they happen to be contrary? This is the usual question which should be answered while testing hypotheses. Various tests, such as Chi square test, t-test, F-test, have been developed by statisticians for the purpose. The hypotheses may be tested through the use of one or more of such tests, depending upon the nature and object of research inquiry. Hypothesis-testing will result in either accepting the hypothesis
or in rejecting it. If the researcher had no hypotheses to start with, generalisations established on the basis of data may be stated as hypotheses to be tested by subsequent researches in times to come.

10. Generalisations and interpretation:

If a hypothesis is tested and upheld several times, it may be possible for the researcher to arrive at generalisation, i.e., to build a theory. As a matter of fact, the real value of research lies in its ability to arrive at certain generalisations. If the researcher had no hypothesis to start with, he might seek to explain his findings on the basis of some theory. It is known as interpretation. The process of interpretation may quite often trigger off new questions which in turn may lead to further researches.

Research Instruments

What is a questionnaire?
Questionnaires are one of the most common and popular tools to gather data from a large number of people.

A good questionnaire can be a powerful tool to inform your evaluation, and a poorly designed questionnaire can make life difficult for both those that have to complete it, and those that have to analyse the data.

Questionnaire layout and design

Questionnaire layout   

      Making sure the layout does not look cluttered. Use adequate spacing between questions.
      Ensuring the questions are numbered and presented in a logical sequence. Group questions by topics or themes.
      Starting with easier or less controversial questions and finishing with more personal questions, including demographic details such as age and income.
      Using larger or bold font to attract
      The shorter the better.
      Most people are time-poor, and a long questionnaire risks limiting your response rate.
      A well-designed questionnaire should not take more than 10 minutes to complete.
      Remember to only ask the questions that you NEED to have answers for.

Invitation and instructions

      The questionnaire should clearly outline why you want people to take part, and the importance of their participation.
      A strong invitation that provides respondents with a sense that their answers and opinions are valued and respected should increase the response rate.
      It is important that you have clear instructions at the start of the questionnaire that explains:
      the purpose of the questions
      who the information is for and how it will be used
      the confidentiality of the answers
      deadlines for completion
      It is also important to have clear instructions as to how to answer questions.
      Instructions need to be provided at the start of each new section that uses a different answering format or response scale.

Things to consider in instructions include whether you want respondents to:

      one answer only
      select all that apply
      rate the answers
      provide a written answer.

Wording of questions

      The wording of questions is critical in ensuring you obtain the information required to answer your evaluation questions.
      This includes:

1.     using language that is appropriate to the audience
2.     using clear, simple questions that avoid ambiguity, double meanings, and jargon,
3.     avoiding leading questions that can lead to bias.

Questions can fall into the following categories:

      In relation to the content of the question:
      Factual questions
      Factual questions are those that can be verified in some way.
      Non-factual questions
      Non-factual questions refer to gauging the knowledge, beliefs, attitudes, and opinions.
      In relation to the form of the question
      Open-ended question
      A question where the respondent writes their own answer. This allows respondents to think about the question, provide suggestions, or test their knowledge, but it is harder to analyse.
      Close-ended question
      A question where the respondent has to select their answer from the range of responses provided.
      The range of responses can vary from two-option answers, to ratings, ranking, or statements.

Tips on Wording Questions

      Avoid double-barrelled questions for example. “Do you take action to correct errors and mistakes?”

       This should be broken down into two questions.
      Be specific about the subject of questions, for example “Do you take action to correct errors?”
      This can be broken down to specific actions, such as supplying the correct from, rewording the error etc.
      The same applies for attitudes and opinions - be as specific as possible in order to obtain the information you want.
      It is also important to be specific about time frames.
      Be specific about timeframes in questions, for example inn a questions such as “Have you taken action to correct errors in recent months?”, ‘recent months’ may not be specific enough for your evaluation.
       It may be best to use more specific timeframes, such as “Since the training workshop held in July, have you taken action to correct errors by supplying the correct from\/
      Avoid leading or loaded questions, for example “Have you stopped correcting errors by supplying the correct from?”
      This is likely to lead to most respondents answering “yes”.
      Further, a “yes” answer also does not differentiate between those who used to correct errors by supplying the correct from were taking long showers, and have  stopped doing so since the training workshop , and those who have never used this technique in their life.

Likert  (lɪkərt/) scale is a psychometric scale commonly involved in research that employs questionnaires. It is the most widely used approach to scaling responses in survey research. This scale us named after its inventor, psychologist Rensis Likert. When responding to  a Likert questionnaire item, respondents specify their level of agreement or disagreement on a symmetric agree-disagree scale for a series of statements. Thus, the range captures the intensity of their feelings for a given item.     

       It is considered symmetric or "balanced" because there are equal amounts of positive and negative positions.
      Often five ordered response levels are used, hence is the ‘5-point Likert Scale’

      Strongly disagree
      Neutral (undecided)
      Strongly agree


You will find below some examples of verbs which appear to be commonly used in writing up research:
Describing The Aims Of The Research
 In English, there is a tendency to PERSONIFY the study:
The present study focuses on the impact of new technologies used in e-learning  on the learning outcomes of EFL students.  
This study investigates the significance  of  using visual/audio aids in the teaching of vocabulary for young learners. 
This research attempts to establish a correlation between learning styles and learning strategies within the framework of Bloom’s taxonomy.
 We do, however, also find examples of the pattern “the aim is to (verb)…..”:
The objective of this study is to investigate the potentiality of supplementary readers on developing the writing skills of the students.
The purpose of this study is to explore the influence of classroom language on the fluency of the students.

Reporting The Work Of Other Researchers
Publications in the recent literature indicate that both traditional and non-traditional techniques are widely used in the teaching of pronunciation.
John and Jack  (1997) have shown that accurate values can be obtained by carrying out cross-sectional studies..
John et al [2000] presented the most thorough and complete review of recent innovations in the field.
Jack [2010] observed that the third model performed best in large crowded classes.
John [1998] investigated the phenomenon using a series of experiments involving different types of teacher training.

Discussing Methods
Samples were taken simultaneously from early starters and  late starters  .
Data was obtained by taking hool leaving examinations.
For each case, a flow chart of activity was developed.
A large amount of data was collected over a range of conditions.
Pre- and post-tests were carried out in multi-lingual classes in order to determine the  effect of different languages on learning English. 

Referring To Diagrams
Fig. (1.5)  shows the pattern within mixed ability groups.
Figure (2.5) shows the relationship between the teaching techniques and the learning outcomes.
Table(5. 4) lists the scores recorded during successive tests.
The experimental findings are summarized in the table below.
Figure (5.10) suggests that recycling of vocabulary in this book does not flow a systematic manner.

Presenting Results
The results show that the trend that emerged from the experiment accurately describes the IQ-achievment elationship.
These results show that the effect magnitude can be attributed to a large extent to the environmental factors.
Using the modified approach resulted in improved performance compared to the traditional approach.
It was found that the highest values were obtained from the weaker samples.
The results suggest that the higher achievement can  largely be due to clarity of objectives in the teacher’s mind.
The data demonstrate that the pre-task activities prior to writing enhance performance quality.

Discussing Findings
The reduced quality of performance   may be attributed to the poorly constructed test items.
However, it can be deduced that the improvement in comprehension is a result of focusing more on high order thinking skills.
The results obtained from the field studies were generally consistent with previous studies.
The technique presented is limited in its effectiveness by the fact that it improves on earlier techniques along one dimension only.
From the experimental results, it can be concluded that corporal punishment  is the main reason behind absenteeism.

Harvard Style

Proposal Template

Sudan University of Science and Technology
Faculty of Graduate Studies
MA Programme in ELT
A Proposal for Research
Title of the research:
Student’s Name:


1.0 Introduction

2.0 Statement of the problem
The problem which the present study attempts to investigate is ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ --------------------------------------------------------------------------------------------------------------------------
3.0 Significance of the study
This study is considered significant for the following reasons:

4.0 Objectives of the study
The study tries to realize the following objectives:

5.0 Research questions
The study will provide answers for the following questions:

6.0 Research hypotheses
The study has the following as its hypotheses:

7.0 Methodology

The study will be ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The following instruments will be used ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The data will be analyized --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

8.0 Limitations of the study
Limitations are influences that the researcher cannot control.   They are the shortcomings, conditions or influences that cannot be controlled by the researcher that place restrictions on your methodology and conclusions. Any limitations that might influence the results should be mentioned.
  • the instruments you utilized.
  • the sample. 
  • time constraints. 

9.0 Delimitations of the study
Delimitations are choices made by the researcher which should be mentioned. They describe the boundaries that you have set for the study. This is the place to explain:
  • the things that you are not doing (and why you have chosen not to do them).
  • the literature you will not review (and why not).
  • the population you are not studying (and why not).
  • the methodological procedures you will not use (and why you will not use them).


10.0         Research Outline
Chapter One: Introduction
1.0 Background
2.0 Statement of the problem
3.0 Significance of the study
4.0 Objectives of the study
5.0 Research questions
6.0 Hypotheses of the study
7.0 Methodology of the study
8.0 Limitations of the stuck
9.0  Delimitations of the study
10.0                     Summary
Chapter Two: Theoretical Framework and Previous Studies
Chapter Three: Methodology
Chapter Four: Data Analysis and Discussion
Chapter Five: Conclusion and Recommendation

11.0         References

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