Module 1: Research Methods PDF

**Module 1** **Chapter I -- Research Methods** **Introduction** Research is a systematic, controlled, empirical, and critical investigation of hypothetical propositions on relations involving natural phenomena (Kerlinger, 1973). This old definition of research mentions the different characteristi...

**Module 1** **Chapter I -- Research Methods** **Introduction** Research is a systematic, controlled, empirical, and critical investigation of hypothetical propositions on relations involving natural phenomena (Kerlinger, 1973). This old definition of research mentions the different characteristics of research such as 'systematic activity' meaning the researcher must follow rules, principles and procedures in doing it, controlled which implies that the researcher must know what variables or factors to investigate and manipulate, 'critical activity' which keep the researcher careful in observation, recording, analyzing and interpreting information that is being gathered (Alicay, 2014) When people think of research, many will think immediately of the collection of quantitative data through interviewing, questionnaires or other methods. While such primary data collection is an important part of many research projects, there is much more involved, and it is generally more appropriate to consider data collection as part of a wider process involving important stages both before and after those of data collection. This chapter introduces this concept of the 'research process', describes the elements within the process, and the relationships between the elements. It is important that you have an understanding of all elements of the research process before commencing your research projects so that you have an idea of the big picture. It will also cover formulating a research problem, research design, sampling design, measurement concepts, data collection and data sources **Learning Outcomes** At the end of the chapter, you will be able to: 1. Identify the different stages of the research process 2. Distinguish the seven characteristics of research 3. Explain the five characteristics of a good research problem 4. Differentiate descriptive and inferential problem statement 5. Formulate a research title relevant to information technology and construct the problem statement or objectives 6. Determine the functions of a research design 7. Give the different types of research design 8. Define sampling 9. Compute the appropriate size of a sample from population 10. Explain the different sampling methods 11. Identify the different method of data collection **\ ** **Lesson 1** **The Research Process** The research process described in the following section is a very generalized model of carrying out research. In reality, the process is much less 'neat', and you will generally find that you will not usually follow the process stage by stage, but will often move continually back and forth between the elements, or carry out two or more of the elements concurrently, especially if you are undertaking a more interpretative or qualitative study. Although different models of the research process exist, each containing different numbers of stages, most include the same general elements (Figure 1.1) It is important to remember that these are not isolated, discrete stages, but are actually part of one overall process. As we said earlier, it may also be the case that for certain methodological approaches the order of the stages may be somewhat different; for example, a qualitative research project may involve a continual integration of reviewing the literature and data collection. Alternatively, a grounded approach will generally involve data collection at a much earlier stage, before the theoretical and conceptual frameworks have been fully developed. Thus, you should be prepared to be flexible, depending upon the nature of the research being undertaken. Whatever approach you take, however, it is important that you maintain a sense of coherence within the overall research project, or what some refer to as a 'golden thread', or 'vertical thread'. This thread should be the research question, and everything within the research process should be related to answering that question. This chapter will briefly outline the stages of the research process. Figure 1.1. The Research Process *Stage 1. Selection of Topic* The stage will take up most, if not all of your time at the beginning is that of selecting a topic, and developing a preliminary research question and set of objectives. The selection of your research question is a crucial stage, as an inappropriate topic or question will often lead to irretrievable difficulties later in the research, so it is worth dealing with this stage carefully. It is unlikely that you will develop a final question and set of objectives at this stage of the research process, specifying what variables are involved and what data need to be collected. *Stage 2. Reviewing the Literature* A literature review essentially consists of critically reading, evaluating and organizing existing literature on the topic to assess the state of knowledge in the area. During this stage you should aim to become an 'expert' in your field of research. The literature review is generally done alongside the development of the theoretical and conceptual frameworks. Reading widely may also alert you to the other helpful factors, such as whether similar research has already been carried out, show you the types of findings that you could expect, or provide descriptions of the theoretical frameworks and previous methodologies adopted by others doing similar research. *Stage 3. Development of Theoretical and conceptual frameworks* As you read the literature, you should be continually developing and refining your theoretical and conceptual frameworks. This is a stage that can often be overlooked in the haste to collect data. It is, however, a vital part of the research process, and is important in alerting you to potential problems before they occur. Your theoretical framework refers to the underlying theoretical approach that you adopt to underpin your study, for example social learning theory, or theories of self-efficacy. The conceptual framework defines and organizes the concepts important within the study. *Stage 4. Clarification of the research question* Stage 1, 2 and 3 of the research processes will initially, in many cases, become a circular process, whereby initial research questions are chosen, investigated and often rejected for a number of reasons, for example: - The question lacks sufficient focus - The conceptual framework has identified problems in either defining and/or measuring the appropriate concepts - There are too many moderating or intervening variables - The project is unfeasible in terms of complexity, access, facilities or resources Stages 1 to 3 can take longer than initially anticipated, and you may well become discouraged by a lack of success in identifying a good research question or hypothesis. There are no easy methods to come up with an appropriate question, and it can be very much a case of perseverance. Once you have developed a good, focused research question, then the rest of the research process is based upon answering that specific question. The importance of developing a clearly focused question and set of research objectives at this stage cannot be overstated. A common fault is the lack of clarity over the overall aim of the research. Without this, it is difficult to maintain your vertical thread. *Stage 5: Research Design* Once the focused research question has been ascertained, the next stage is to consider two questions: 1. What data do I need to collect to answer this question? 2. What is the best way to collect this data? Breaking this down into more detail, the issues faced by the researcher are: - What overall research design should I use? Will I, for example, use a cross-sectional, experimental or longitudinal design? - Will I need to collect primary data, or will there be suitable secondary data to use? - What methods, for example interviews, questionnaire surveys and so on, will be the best ones to collect the primary data? - Who should participate in the research, and how will I gain access to them? - What are the exact procedures that I should adopt in my data collection to ensure reliability and validity? *Stage 6: Data Collection* Once the issues identified in stage 4 and 5 of the research process have been addressed, then you should have a clear idea of what data to collect, and how to collect it. You have to consider which methodology to choose, and which methods to utilize within the methodology. The background of this is dealt with in more depth in the next chapter. *Stage 7: Data Analysis and Discussion of Findings* The data you collect in stage 6 needs to be analyzed to provide answers to your research question. Methods of data analysis should be always related to the objectives of the research, that is your analysis should answer the research question on hypothesis. In your discussion of the results, reference should also be made back to the literature reviewed in stage 2; for example, how do the findings add to this literature? Do they support the literature? If not, what are the possible reasons why? A common fault is to discuss the findings with no reference back to the literature reviewed as part of stage 2 of the development of the conceptual framework. *Stage 8: Drawing Conclusions* This should relate back to the focused research question. Here, the answer to the research question(s) should be clearly stated. You can evaluate how successful you have been in achieving your research objectives, and highlight the strengths and weaknesses of the research. You may also want to make recommendations for further research **Characteristics of Research** 1. Empirical -- Research is based on direct observation. A researcher can work on a problem that can be addressed through observation. 2. Logical -- Research involves reasoning and valid procedures. Researchers have confidence on their findings because they are based on orderly procedures. A researcher makes conclusions based on the findings(inductive) and explains his findings basing on existing theories(deductive) 3. Cyclical -- A researcher who completes a study presents his findings and suggests further studies on aspects of the problem that were not addressed by his research. A research starts with a problem, works on the problem and makes generalization from which another problem may arise. 4. Analytical -- Research applies analytic procedures in gathering and analyzing data. 5. Critical -- Research requires careful and precise judgement in analysis and making generalizations. For instance, a researcher uses specific level of confidence; say.05 level, 0.01 level, in accepting or rejecting the null hypothesis. 6. Methodical- Research is conducted using methods and techniques that are appropriate to the research problems or objectives. 7. Replicable -- The design and procedures of a study can be replicated or repeated to arrive at more conclusive results. In experimental researches, replication studies are conducted to find out the adaptability of crops to different locations. In social science researches, replication studies are done to determine the results of a study in another group of subjects **Lesson 2** **Problem Definition** Research is conducted because of a problem that needs to be addressed, a question that needs to be answered, knowledge gap that should be filled. Research starts with a problem and not with a title. A common mistake of beginning researchers is to state titles then try to identify the components of the title. A research problem is the basis of a researcher in choosing the words for the title of the research. It is the guide for the researcher in stating the hypotheses and how to conduct the study which includes what research method to use, who and what are the sources of data, what instrument is used to gather the needed data, and what statistical tools are appropriate in analyzing the collected data. The title can be reworded even after the conduct of the study, but the problem cannot be changed because a change in the problem means a change in the entire study. To beginning researchers, formulating a research problem is a difficult stage of the research process. When a researcher has identified and formulated the research problem, he has a direction and basis in choosing the appropriate methodology and in accomplishing all the other parts of the research activity. A research problem is a question about the difference or relationship between two or more variables, a knowledge gap that need to be filled in. There are five characteristics to consider as bases in stating problems or objectives, with the acronym SMART. 1. *Specific* -- the question should specify what variables are to be determined. 2. *Measurable* -- the variables involved can be subjected to measurement, with any level of measurement and with the use of measuring instruments. 3. *Attainable* -- the question can be answered or the objective can be attained because the needed data can be collected and can be analyzed with appropriate tools. 4. *Realistic* -- results can be obtained because the data can be obtained following scientific procedures and techniques. 5. Time-Bound -- there is a time frame for every activity or step in the research work. A good problem statement is stated clearly in question form and it should imply possibilities of empirical testing. This means that the variables can be measured. The statements of the problem can be a combination of descriptive and inferential questions. Descriptive questions elicit responses that are generally explained through means, frequencies, ranks, standard deviations and other descriptive statistical tools. Inferential questions link together one or more descriptive questions by asking differences or relationships among the variables found in the descriptive questions. **Example of a Problem Statement** 1. *What is the computer literacy level of the respondents in terms of the following indicators?* a. *Number of hours of computer trainings* b. *Number of computer software used* c. *Score in the test on computer technology* 2. *Is the computer literacy level of the respondents related to the following characteristics?* d. *Age* e. *Gender* f. *Length of service* 1. *Is there a difference in the income derived from the livelihood projects between program beneficiaries and non-beneficiaries?* 2. *Is there a relationship between the adequacy of program inputs with the expected outputs of the program?* In statistical analysis, difference and relationship serve the same purpose because if the program beneficiaries and non-beneficiaries do not differ in income, it means that the variable income from the livelihood projects is not related to the classification of the respondents whether beneficiaries or non-beneficiaries **Criteria of Choosing a Research Topic** 1. *Importance and Urgency* - the study should be useful and important to the researcher, the school, and the society. A researcher should not waste his time, money and effort in something that is no use. There is no point in choosing a topic of no interest to anyone but the researcher only. The topic must be significant and contribute significantly in either knowledge or impact, preferably both. When a researcher conceptualizes a research project and request for funding from a funding agency, the first question a donor or funding agency will ask when reading the project topic and objective is, "So what?". This means both "what is new about what you are doing?" and "who will be better off, and in what way, as a result of what you propose to do?" The researcher must have answers to these questions on the project proposal if he is to have a topic worth submitting to a funding agency. 2. *Interesting* -- the topic of the research should be interesting not only to the researcher but also to readers. 3. *Researchability*/*Feasibility* -- can the research be done or implemented? Is it possible to collect the needed data? Does the researcher have the ability and logistics to conduct the study? 4. *Relevance and moral implication* -- the topic should be timely and practical and should not bring insult or embarrassment to any person, organization or institution. 5. *Budgetary and time requirements* -- the researcher should work on a study which he can finish in a desired time and within his budget capacity. 6. *Novelty* -- Is the problem new? Is it breakthrough? **Sources of Research Problem** 1. *The researcher's specialization.* A researcher can perform better in an area where his knowledge and abilities are. For example, if his specialization is languages, he may wish to find out the factors that affect the reading difficulties of students, the common grammatical errors, the extent of use of gay language, non-verbal communication process, and other language related problems. Or if his specialization is Information Technology, he may wish to find out the computer literacy, the programming difficulties of students or a comparative on the academic performance of a gamer or a non-gamer student in programming and other related problems to his field. 2. *Curiosity and creative ideas of the researcher*. Working in his area of specialization, a researcher may have questions that he wants to answer. From his observations about the locality and country, a beginning researcher may come up with a researchable problem. 3. *Recommendations from theses, dissertations and published researches*. After presenting their findings, researcher usually recommended problems for future researchers because they know what aspects of the problem that they have not addressed in their studies. The recommendations are presented in the last chapter of these and dissertations. 4. *Research and Development thrusts of R&D institutions*. For researchers who intend to conduct research and development projects, agencies and organizations that give fund support have their own research agenda and priority areas for which they give fund support. **Steps in Formulating a research problem** The formulation of a research problem is the most crucial part of the research journey as the quality and relevance of your research project entirely depends upon it. Every step that constitutes the *how* part of the research journey depends upon the way you formulated your research problem. The process of formulating a research problem consists of a number of steps. Working through these steps presupposes a reasonable level of knowledge in the broad subject area within which the study is to be undertaken and the research methodology itself. A brief review of the relevant literature helps enormously in broadening this knowledge base. Without such knowledge it is difficult to 'dissect' a subject area clearly and adequately. If you do not know what specific research topic, idea, questions or issue you want to research, first go through the following steps: **Step 1:** **Identify a broad field or subject area of interest to you.** Ask yourself, 'What is it that really interests me as a professional? It is a good idea to think about the field in which you would like to work after graduation. This will help you to find interesting topic, and one which may be of use to you in the future. For example, if you are a social work student, inclined to work in the area of youth welfare, refugees or domestic violence after graduation, you might take to research in one of these areas. Or if you are studying marketing, you might be interested in researching consumer behavior. Or, as a student of public health, intending to work with patients who have HIV/AIDS, you might like to conduct research on a subject relating to this area. Or, as an IT student, you might want to study the effect of computer illiteracy or any subject relating to Information Technology. **Step 2: Dissect the broad area into subareas.** At the onset, you will realize that all the broad areas mentioned above -- youth welfare, refugees, domestic violence, consumer behavior, HIV/AIDS and computer illiteracy -- have many aspects. For example, there are many aspects and issues in the area of domestic violence. Similarly, you can select any subject area from other fields such as community health or consumer research or IT related issues and go through this dissection process. In preparing the list of subareas you should also consult others who have some knowledge of the area and the literature in your subject area. Once you have developed an exhaustive list of the subareas from various sources, you proceed to the next stage where you select what will become the basis of your enquiry. **Step 3: Select what is the most interest to you.** It is neither advisable nor feasible to study all subareas. Out of this list, select issues or subareas about which you are passionate. This is because your interest should be the most important determinant for selection, even though there are other considerations. One way to decide what interests you most is to start with the process of elimination. Go through your list and delete all those subareas in which you are not very interested. You will find that towards the end of this process, it will become very difficult for you to delete anything further. You need to continue until you are left with something that is manageable considering the time available to you, your level of expertise and other resources needed to undertake the study. Once you are confident that you have selected an issue you are passionate about and can manage, you are ready to go to the next step. **Step 4: Raise research questions.** At this step ask yourself, 'What is it that I want to find out about in this subarea?' Make whatever questions come to your mind relating to your chosen subarea and if you think there are too many to be manageable go to the process of elimination, as you did in step 3. **Step 5: Formulate objectives.** Both your main objectives and your subobjectives now need to be formulated, which grow out of your research questions. The main difference between objectives and research questions is the way in which they are written. Research questions are obviously that-questions. Objectives transform these questions into behavioral aims by using action-oriented words such as 'to find out', 'to determine', 'to ascertain' and 'to examine'. Some researchers prefer to reverse the process; that is, they start from objectives and formulate research questions from them. Some researchers are satisfied only with research questions, and do not formulate objectives at all. If you prefer to have only research questions or only objectives, this is fine, but keep in mind the requirements of your institution for research proposal. **Step 6: Assess your objectives.** Now examine your objectives to ascertain the feasibility of achieving them through your research endeavor. Consider them in the light of the time, resources and technical expertise at your disposal. **Step 7: Double-check.** Go back and give final consideration to whether or not you are sufficiently interested in the study, and have resources to undertake it. Ask yourself, "Am I really enthusiastic about this study?' and 'DO I really have enough resources to undertake it? Answer these questions thoughtfully and realistically. If you answer to one of them is 'no', reassess your objectives. **The Formulation of Research Objectives** Objectives are the goals you set out to attain in your study. Since these objectives inform a reader of what you want to achieve through the study, it is extremely important to word them clearly and specifically. Objectives should be listed under two headings: - Main objectives - Subobjectives +-----------------+-----------------+-----------------+-----------------+ | Step 1 | Step 2 | Step 3 | Step 4 | +=================+=================+=================+=================+ | Identify | Dissect | Select | Raise questions | +-----------------+-----------------+-----------------+-----------------+ | Alcoholism | 1. Profile of | Effects of | 1. What impact | | | alcoholics | alcoholism on | has | | | | the family | alcoholism | | | 2. The causes | | on marital | | | of | | relations? | | | alcoholism | | | | | | | 2. How does it | | | 3. The process | | affect the | | | of becoming | | various | | | an | | aspects of | | | alcoholic | | children's | | | | | lives? | | | 4. The effects | | | | | of | | 3. What are | | | alcoholism | | the effects | | | on the | | on the | | | family | | family's | | | | | finances? | | | 5. Community | | | | | attitudes | | | | | towards | | | | | alcoholism | | | | | | | | | | 6. The | | | | | effectivene | | | | | ss | | | | | of a | | | | | treatment | | | | | model. | | | +-----------------+-----------------+-----------------+-----------------+ | Step 5 | Step 6 | Step 7 | | +-----------------+-----------------+-----------------+-----------------+ | Formulate | Make Sure | Double-check | | | Objectives | | | | +-----------------+-----------------+-----------------+-----------------+ | Main objective | Assess these | 1. That you | | | | objectives in | are really | | | To find out the | the light of: | interested | | | effects of | | in the | | | alcoholism on | 1. The work | study | | | the family | involved | | | | | | 2. That you | | | Specific | 2. The time | agree with | | | objectives: | available | objectives | | | | to you | | | | 1. To | | 3. That you | | | ascertain | 3. The | have | | | the impact | financial | adequate | | | of | resources | resources | | | alcoholism | at your | | | | on marital | disposal | 4. That you | | | relations | | have the | | | | 4. Your | technical | | | 2. To | technical | expertise | | | determine | expertise | to | | | the ways in | in the area | undertake | | | which | | the study | | | alcohol | | | | | affects the | | | | | different | | | | | aspects of | | | | | children's | | | | | lives | | | | | | | | | | 3. To find out | | | | | the effects | | | | | of | | | | | alcoholism | | | | | on the | | | | | financial | | | | | situation | | | | | of the | | | | | family | | | | +-----------------+-----------------+-----------------+-----------------+ Subobjectives should be numerically listed. They should be worded clearly and unambiguously. Make sure that each subobjective contains only one aspect of the study. Use action-oriented words or verbs when writing your objectives. The objectives should start with words such as 'to determine', 'to find out', 'to ascertain', 'to measure' and 'to explore'. The way the main objectives and subobjectives are worded determines how your research is classified (e.g. descriptive, correlational or experimental). In other words, the wording of your objectives determines the type of research design you need to adopt to achieve them. Hence, be careful about the way you word your objectives. Irrespective of the type of research, the objectives should be expressed in such a way that the wording clearly, completely and specifically communicates to your readers your intention. There is no place for ambiguity, non-specificity or incompleteness, either in the wording of your objectives or in the ideas they communicate. **Lesson 3** **Research Design** If you are clear about your research problem, you have crossed one of the most important and difficult sections of your research journey. Having decided what you want to study, you now need to determine how you are going to conduct your study. There are a number of questions that need to be answered before you can proceed with your journey. What procedures will you adopt to obtain answers to research questions? How will you carry out the tasks needed to complete the different components of the research process? What should you do and what should you not do in the process of undertaking the study? Basically, answers to these questions constitute the core of research design. **What is a research design?** A research design is a plan, structure and strategy of investigation so conceived as to obtain answers to research questions or problems. The plan is the complete scheme or program of the research. It includes an outline of what the investigator will do from writing the hypotheses and their operational implications to the final analysis of data. (Kerlinger 1986:279) A traditional research design is a blueprint or detailed plan for how a research study is to be completed-operationalizing variables so they can be measured, selecting a sample of interest to study, collecting data to be used as a basis for testing hypotheses, and analyzing the result. (Thyer 1993:94) A research design is a procedural plan that is adopted by the researcher to answer questions validity, objectively, accurately and economically. According to Selltiz, Deutsch and Cook, 'A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure' (1962:50). Through a research design you decide for yourself and communicate to others your decisions regarding what study design you propose to use, how you are going to collect information from your respondents, how you are going to communicate your findings. In addition, you will need to detail in your research design the rationale and justification for each decision that shapes your answers to the 'how' of the research journey. In presenting your rationale and justification you need to support them critically from the literature reviewed. You also need to assure yourself and others that the path you have proposed will yield valid and reliable results. **The functions of a research design** The above definitions suggest that a research design has two main functions. The first relates to the identification and/or development of procedures and logistical arrangements required to undertake a study and the second emphasizes the importance of quality in these procedures to ensure their validity, objectivity and accuracy. Hence, through a research design you: - Conceptualize an operational plan to undertake the various procedures and tasks required to complete your study; - Ensure that these procedures are adequate to obtain valid, objective and accurate answers to the research question called the control of variance (Kerlinger, 1986:280) - Name the study design per se -- that is, 'cross-sectional', 'before-and-after', 'comparative', 'control experiment' or 'random control'. - Provide detailed information about the following aspects of the study: - Who will constitute the study population? - How will the study population be identified? - Will a sample or the whole population be selected? - If a sample is selected, how will it be contacted? - How will consent be sought? - What method of data collection will be used and why? - In the case of a questionnaire, where will the responses be returned? - How should respondents contact you if they have queries? - In the case of interviews, where will they be conducted? - How will ethical issues be taken care of? **Selecting a Study Design** Differences between quantitative and qualitative study designs In this section, we will discuss some of the most study designs in quantitative and qualitative research. Overall, there are many more study designs in quantitative research than in qualitative research. Quantitative study designs are specific, well structured, have been tested for their validity and reliability, and can be explicitly defined and recognized. Study designs in qualitative research either do not have these attributes or have them to a lesser degree. They are less specific and precise, and do not have the same structural depth. Differences in philosophical perspectives in each paradigm combined with the aims of a study, to a large extent, determine the focus, approach and mode of enquiry which, in turn, determine the structural aspects of a **study design**. The main focus in qualitative research is to understand, explain, explore, discover and clarify situations, feelings, perceptions, attitudes, values, beliefs and experiences of a group of people. The study designs are therefore often based on deductive rather than inductive logic, are flexible and emergent in nature, and are often non-linear and non-sequential in their operationalization. The study designs mainly entail the selection of people from whom the information, through an open frame of enquiry, is explored and gathered. The parameters of the scope of a study, and information gathering methods and processes, are often flexible and evolving; hence, most qualitative designs are not as structured and sequential as quantitative ones. On the other hand, in quantitative research, the measurement and classification requirements of the information that is gathered demand that study designs are more structured, rigid, fixed and predetermined in their use to ensure accuracy in measurement and classification. In qualitative studies the distinction between study designs and methods of data collection is far less clear. Quantitative study designs have more clarity and distinction between designs and methods of data collection. In qualitative research there is an overlap between the two. Some designs are basically methods of data collection. For example, in-depth interviewing is a design as well as a method of data collection and so are oral history and participant observation. One of the most distinguishing features of qualitative research is the adherence to the concept of respondent concordance whereby you as a researcher make every effort to seek agreement of your respondents with your interpretation, presentation of the situations, experiences, perceptions and conclusions. In quantitative research respondent concordance does not occupy an important place. Sometimes it is assumed to be achieved by circulating or sharing the findings with those who participated in the study. The 'power-gap' between the researcher and the study population in qualitative research is far smaller than in quantitative research because of the informality in structure and situation in which data is collected. In quantitative research enough detail about a study design is provided for it to be replicated for verification and reassurance. In qualitative research little attention is paid to study designs or the other structural aspects of a study, hence the replication of a study design becomes almost impossible. This leads to the inability of the designs to produce findings that can be replicated. Findings through quantitative study designs can be replicated and retested whereas this cannot be easily done by using qualitative study designs. Another difference in the designs in qualitative and quantitative studies is the possibility of introducing researcher bias. Because of flexibility and lack of control it is more difficult to check researcher bias in qualitative studies. Study designs in each paradigm are appropriate for finding different things. Study designs in qualitative research are more appropriate for exploring the variation and diversity in any aspect of social life, whereas in quantitative research they are more suited to finding out the extent of this variation and diversity. If your interest is in studying values, beliefs, understandings, perceptions, meanings, etc., qualitative study designs are more appropriate as they provide immense flexibility. On the other hand, if your focus is to measure the magnitude of that variation, 'how many people have a particular value, belief, etc.?', the quantitative designs are more appropriate. For good quantitative research it is important that you combine quantitative skills with qualitative ones when ascertaining the nature and extent of diversity and variation in a phenomenon. In most cases where you want to explore both, you need to use methods that fall in the domain of both paradigms. **Study designs in quantitative research** Some of the commonly used designs in quantitative studies can be classified by examining them from three different perspectives: 1. the number of contacts with the study population; 2. the reference period of the study; 3. the nature of the investigation. Every study design can be classified from each one of these perspectives. These perspectives are arbitrary bases of classification; hence, the terminology used to describe them is not universal. However, the names of the designs within each classification base are universally used. Note that the designs within each category are mutually exclusive; that is, if a particular study is cross-sectional in nature it cannot be at the same time a before-and-after or a **longitudinal study**, but it can be a nonexperimental or experimental study, as well as a **retrospective study** or a **prospective study**(See Figure 3.1). **Study designs based on the number of contacts** Based on the number of contacts with the study population, designs can be classified into three groups: 1. cross-sectional studies; 2. before-and-after studies; 3. longitudinal studies. ***The cross-sectional study design*** **Cross-sectional studies**, also known as one-shot or status studies, are the most commonly used design in the social sciences. This design is best suited to studies aimed at finding out the prevalence of a phenomenon, situation, problem, attitude or issue, by taking a cross-section of the population. They are useful in obtaining an overall 'picture' as it stands at the time of the study. They are 'designed to study some phenomenon by taking a cross-section of it at one time' (Babbie 1989: 89). Such studies are cross-sectional with regard to both the study population and the time of investigation. A cross-sectional study is extremely simple in design. You decide what you want to find out about, identify the study population, select a sample (if you need to) and contact your respondents to find out the required information. For example, a cross-sectional design would be the most appropriate for a study of the following topics: - The reasons for homelessness among young people. - The quality assurance of a service provided by an organization. - The impact of unemployment on street crime (this could also be a before-and-after study). - The relationship between the home environment and the academic performance of a child at school. - The attitude of the community towards equity issues. - The extent of unemployment in a city. - Consumer satisfaction with a product. - The effectiveness of random breath testing in preventing road accidents (this could also be a before-and-after study). - The health needs of a community. - The attitudes of students towards the facilities available in their library. As these studies involve only one contact with the study population, they are comparatively cheap to undertake and easy to analyze. However, their biggest disadvantage is that they cannot measure change. To measure change it is necessary to have at least two data collection points -- that is, at least two cross-sectional studies, at two points in time, on the same population. ***The before-and-after study design*** The main advantage of the before-and-after design (also known as the pre-test/post-test design) is that it can measure change in a situation, phenomenon, issue, problem or attitude. It is the most appropriate design for measuring the impact or effectiveness of a program. A before-and-after design can be described as two sets of cross-sectional data collection points on the same population to find out the change in the phenomenon or variable(s) between two points in time. The change is measured by comparing the difference in the phenomenon or variable(s) before and after the intervention (see figure 3.2) A before-and-after study is carried out by adopting the same process as a cross-sectional study except that it comprises two cross-sectional data sets, the second being undertaken after a certain period. Depending upon how it is set up, a before-and-after study may be either an experiment or a non-experiment. It is one of the most commonly used designs in evaluation studies. The difference between the two sets of data collection points with respect to the dependent variable is considered to be the impact of the program. The following are examples of topics that can be studied using this design: - The impact of administrative restructuring on the quality of services provided by an organization. - The effectiveness of a marriage counselling service. - The impact of sex education on sexual behavior among schoolchildren. - The effect of a drug awareness program on the knowledge about, and use of, drugs among young people. - The impact of incentives on the productivity of employees in an organization. - The impact of increased funding on the quality of teaching in universities. - The impact of maternal and child health services on the infant mortality rate. - The effect of random breath testing on road accidents. - The effect of an advertisement on the sale of a product. ***The longitudinal study design*** The before-and-after study design is appropriate for measuring the extent of change in a phenomenon, situation, problem, attitude, and so on, but is less helpful for studying the pattern of change. To determine the pattern of change in relation to time, a longitudinal design is used; for example, when you wish to study the proportion of people adopting a program over a period. Longitudinal studies are also useful when you need to collect factual information on a continuing basis. You may want to ascertain the trends in the demand for labor, immigration, changes in the incidence of a disease or in the mortality, morbidity and fertility patterns of a population. In longitudinal studies the study population is visited a number of times at regular intervals, usually over a long period, to collect the required information (see Figure 3.3). These intervals are not fixed so their length may vary from study to study. Intervals might be as short as a week or longer than a year. Irrespective of the size of the interval, the type of information gathered each time is identical. Although the data collected is from the same study population, it may or may not be from the same respondents. A longitudinal study can be seen as a series of repetitive cross-sectional studies. Longitudinal studies have many of the same disadvantages as before-and-after studies, in some instances to an even greater degree. In addition, longitudinal studies can suffer from the **conditioning** **effect**. This describes a situation where, if the same respondents are contacted frequently, they begin to know what is expected of them and may respond to questions without thought, or they may lose interest in the enquiry, with the same result. The main advantage of a longitudinal study is that it allows the researcher to measure the pattern of change and obtain factual information, requiring collection on a regular or continuing basis, thus enhancing its accuracy. **Study designs based on the reference period** The *reference period* refers to the time-frame in which a study is exploring a phenomenon, situation, event or problem. Studies are categorized from this perspective as: - retrospective; - prospective; - retrospective--prospective. ***The retrospective study design*** Retrospective studies investigate a phenomenon, situation, problem or issue that has happened in the past. They are usually conducted either on the basis of the data available for that period or on the basis of respondents' recall of the situation. For example, studies conducted on the topic below is classified as retrospective studies: - The relationship between levels of unemployment and street crime. ***The prospective study design*** Prospective studies refer to the likely prevalence of a phenomenon, situation, problem, attitude or outcome in the future. Such studies attempt to establish the outcome of an event or what is likely to happen. Experiments are usually classified as prospective studies as the researcher must wait for an intervention to register its effect on the study population. The following are classified as prospective studies: - To determine, under field conditions, the impact of maternal and child health services on the level of infant mortality. - To establish the effects of a counselling service on the extent of marital problems. - To determine the impact of random breath testing on the prevention of road accidents. - To find out the effect of parental involvement on the level of academic achievement of their children. ***The retrospective--prospective study design*** **Retrospective--prospective studies** focus on past trends in a phenomenon and study it into the future. Part of the data is collected retrospectively from the existing records before the intervention is introduced and then the study population is followed to ascertain the impact of the intervention. A study is classified under this category when you measure the impact of an intervention without having a control group. In fact, most before-and-after studies, if carried out without having a control -- where the baseline is constructed from the same population before introducing the intervention -- will be classified as retrospective--prospective studies. Trend studies, which become the basis of projections, fall into this category too. Some examples of retrospective--prospective studies are: - The effect of random breath testing on road accidents. - The impact of incentives on the productivity of the employees of an organization. - The impact of maternal and child health services on the infant mortality rate. - The effect of an advertisement on the sale of a product. **Study designs based on the nature of the investigation** On the basis of the nature of the investigation, study designs in quantitative research can be classified as: - experimental; - non-experimental; - quasi- or semi-experimental. **How to create a research design** The research design is a framework for planning your research and answering your research questions. Creating a research design means making decisions about: - The type of data you need - The location and timescale of the research - The participants and sources - The variables and hypotheses (if relevant) - The methods of collecting and analyzing data The research design sets the parameters of your project: it determines exactly what will and will not be included. It also defines the criteria by which you will evaluate your results and draw your conclusions. The reliability and validity of your study depends on how you collect, measure, analyze, and interpret your data. A strong research design is crucial to a successful research proposal, scientific paper, or dissertation. *Step 1: Consider your priorities and practicalities* For most research problems, there is not just one possible research design, but a range of possibilities to choose from. The choices you make depend on your priorities in the research, and often involve some tradeoffs- a research design that is strong in one area might be weaker in another. Examples A qualitative case study is good for gaining in-depth understanding of a specific context, but it does not allow you to generalize to a wider population A laboratory experiment allows you to investigate causes and effects with high internal validity, but it might not accurately represent how things work in the real world (external validity). As well as scientific considerations, you also need to think practically when designing your research. - How much time do you have to collect data and write up the research? - Will you be able to gain access to the data you need (e.g. by travelling to a specific location or contacting specific people)? - Do you have the necessary research skills (e.g. statistical analysis or interview techniques)? If you realize it is not practically feasible to do the kind of research needed to answer your research questions, you will have to refine your questions further. *Step 2: Determine the type of data you need* You probably already have an idea of the type of research you need to do based on your problem statement and research questions. There are two main choices that you need to start with. Primary vs secondary data You will directly collect original data (e.g. through surveys, interviews, or experiments) and then analyze it. This makes your research more original, but it requires more time and effort, and relies on participants being available and accessible. You will analyze data that someone else already collected (e.g. in national statistics, official records, archives, publications, and previous studies). This saves time and can expand the scope of your research, but it means you don't have control over the content or reliability of the data. Qualitative and quantitative data If your objectives involve describing subjective experiences, interpreting meanings, and understanding concepts, you will need to do qualitative research. Qualitative research designs tend to be more flexible, allowing you to adjust your approach based on what you find throughout the research process. If your objectives involve measuring variables, finding frequencies or correlations, and testing hypotheses, you will need to do quantitative research. Quantitative research designs tend to be more fixed, with variables and methods determined in advance of data collection. Note that these pairs are not mutually exclusive choices: you can create a research design that combines primary and secondary data and uses mixed methods (both qualitative and quantitative). *Step 3: Decide how you will collect the data* Once you know what kind of data you need, you need to decide how, where and when you will collect it. This means you need to determine your research methods -- the specific tools, procedures, materials and techniques you will use. You also need to specify what criteria you'll use to select participants or sources, and how you will recruit or access them. Research Methods Surveys - How many respondents do you need and what sampling method will you use (e.g. simple random or stratified sampling)? - How will you distribute the survey (e.g. in person, by post, online)? - How will you design the questionnaire (e.g. open or closed questions)? Interviews - How will you select participants? - Where and when will the interviews take place? - Will the interviews be structured, semi-structured or unstructured? Experiments - Will you conduct the experiment in laboratory setting or in the field? - How will you measure and control the variables? - How will you design the experiment (e.g. between-subjects, within-subjects, double blinding)? Secondary data - Where will you get your sources from (e.g. online or physical archive)? - What criteria will you use to select sources (e.g. date, range, content)? *Step 4: Decide how will you analyze the data* - To answer you research questions, you will have to analyze the data you collected. The final step in designing the research is to consider your data analysis methods. **Lesson 4** **Sampling Design** **Sampling, its definition** Research aims to discover principles that are applicable universally, but involving the universe or whole population is practically impossible. It is necessary to get only a part of the population. This part of the population is called sample and the process of getting a sample from the population is called sampling. Sampling is defined as the selection of a part of a population in such a way that the sample is representative of the population and judgment about the population can be made on the basis of the sample. **Reasons for Sampling** 1. Economy. Sampling saves time, resources and effort. A researcher spends much less time, money and effort in duplicating questionnaires and interview guides, interviewing or floating and retrieving questionnaires, and data analysis. 2. Practicality. In most situations, it is impossible to involve the whole population if it is too large for every element to be measured. Some populations can never be studied directly because of lack accessibility, limited time, or prohibitive cost. For example, in a study on achievement motivation of college students in Region 2, it is impossible to interview or give questionnaire or observe every college student in the area covered. 3. Necessity. Sampling is imperative when the testing or measuring procedure is destructive, that is, if the sample is destroyed by testing, the researcher cannot test the entire population. For instance, a study where the life length of a bulb is measured, a researcher does not need to use all electric light bulbs to test their life-span. A researcher needs only a small quantity of blood from the human body to test for the presence of a rare disease. 4. Ethicality. For those in business concerned with products whose testing involves ethical considerations (such as drugs), it may be unlawful or unethical to consider testing a whole population. For example, in testing the effects of a certain medication, it is unethical to test the effect of the drug on the whole population. 5. Comprehensiveness. Sampling allows the increase in the scope of the study. With only a portion of the population under consideration, the scope of the study may be increased. For example, if a researcher wants to conduct a study on college students, instead of covering only the college students in one institution or municipality, he may cover the whole province or region because he can get a sample of college students from the different schools in the region. Slovin's Formula: used when you have limited information on the characteristics of the population Example Suppose a researcher has a population of 6,000 farmers and the margin of error is 5%, the sample size is computed as follows: Sol'n: = 375 farmers **Sampling Methods** The ultimate aim of sampling is to get individuals that are true representatives of the population. Before gathering the needed data, it is important to choose what sampling method to use. Here are the methods in getting a sample: 1. *Simple Random Sampling* -- a technique which gives each member of the population equal non-zero chance to be included in the sample. In sample random sampling, the samples are identified through one of the following ways: a. Lottery technique -- also called fishbowl or raffle technique. It is done by listing the names or numbers of the members of the population on-small pieces of paper, all the pieces put into a box or container, the box is shaken vigorously to give each paper a chance to be picked up, the process is repeated until the desired sample size is drawn. b. Table of Random Numbers -- a table of Random Numbers is a list of numbers with no definite pattern. Tables of random numbers may take several pages and come in different patterns, forms and number of digits, but their use is based on the same principle, that is to get the first random number then get the succeeding numbers in the list, horizontally in the rows or vertically in the columns. c. Random Number Generator in a Scientific Calculator -- A researcher can get random numbers from a scientific calculator. The random number key is usually marked with Ran\#. Every time the key is pressed, a random number comes out in the calculator screen. If the researcher has a list of the number and names of the population, he can identify which of those in the list should be included in the sample by pressing the random number key. For example, he presses the Ran\# and 47 is shown, then number 47 in the list is one of the respondents. He presses again and 09 comes out, then number 9 in the list is also included in the sample. He repeats the procedure until the desired sample size is obtained. 2. *Systematic Sampling* -- If a researcher has a list of N units arranged in some order, he can have a sample of n units by taking a unit at random from the first k unit and every kth subsequent unit. For example, in a population of N=6000 office workers listed alphabetically from 1 to 6000 and the researcher wants to get a sample of n = 100, the sample can be obtained in the following steps: a. Determine k = systematic sample number of classes, using the following formula: k=N/n where n = sample size, N = population size k=6000/100 = 60 b. Get a random number from the first k which is 1 to 60. c. Suppose the number randomly selected is 27, then the first n unit (meaning the first member of the sample) is number 27 in the list of office workers. d. The other sample units are taken by adding k to 27. That is, 27 + k = 87 (Number 87 office worker is the second sample unit). The succeeding sample units are number 147, 207, 267, 327, etc. in the list until 100 samples are taken. 1. Drawing of the sample is easy. 2. The sample is spread evenly over the population. 3. It is easy to administer in the field. 3. *Stratified Sampling* In stratified sampling, the population of N units is first subdivided into subpopulations of n~1~,n~2~...n~k~. There are several reasons for stratifying the population: e. Information is required for certain subdivisions of a population. Example: A company wants to study the buying habits of a certain locality. He may want to know the buying habits of people in certain income brackets. So, income may be used as a stratifying factor. f. For administrative convenience Example: A large company may draw samples from its regional branches situated in different parts of the country. Then the area covered by each regional office may constitute a stratum. g. The Population is extremely heterogeneous. Variation of results can be reduced by stratifying. This can be done by making each stratum relatively homogenous with respect to a criterion. Example: Suppose a researcher is interested in average weight of school children, stratification may be a) children from public schools and b) children from private schools. h. The problem of sampling (their nature, size, etc.) may differ in different parts of the population. Example: If a researcher wants to sample annual sales of a region's retail outlets, then it might be convenient to categorize the outlets according to size -- large, medium and small. When drawing samples from each stratum, the problem that may come up is the size of the sample drawn from each stratum. If equal samples are drawn from each stratum, it is stratified equal allocation. A researcher may also take the sample size proportional to the stratum size, in which case he is using stratified proportional allocation. 4. *Cluster Sampling* In a study, sampling units are either single observational units, such as individuals, or groups of observational units, such as households. Usually, it is difficult to identify the individual units that make up the population. For example, if a researcher wishes to get samples from residents of a large city to obtain opinion data, he can be sure that no one can identify every individual in the city in order to construct a sampling frame. However, the individual units may all be contained in city blocks or districts. In this case the blocks or districts can be used as sampling frame. In cluster sampling, a sample of groups is selected from the sampling frame and data are taken from individual units in the groups randomly selected. Such groups are called clusters. If all the individual units in a cluster are included in the sample, the procedure is called simple cluster sampling. If, from each cluster, individual units are randomly selected for inclusion in the sample, the procedure is called two-stage cluster sampling. Another example is when a researcher administers questionnaires in a certain school to 50 students in N =20 sections of 10 students per section. He gets a list of the 20 sections from which a random sample of n=5 sections can be chosen. Then he approaches all the students within each of the 5 samples sections. The main advantage of cluster sampling is that it is usually cheaper when sampling requires extensive traveling. The main disadvantage is that the information will be less precise than through simple random sampling. 5. *Multi-stage Sampling* -- this is applied when there is a need to do sampling not only once but in two or more steps before a researcher can have a desired sample to represent the population. For example, a researcher conducts a study on college students in Cagayan. From a list of all colleges and universities in the province, he gets a cluster sample of colleges as the first sampling stage. From each identified college, he gets a cluster sample of departments as the second sampling stage, and then he identifies students from each identified department by simple random sampling as the third stage. **In any sampling method, the important considerations are randomness and representativeness. Every member of the population has an equal chance of being selected.** **Lesson 5** **Measurement Concepts** **Measurement** Measurement is the foundation of scientific inquiry. In order to test our hypotheses, we must observe our theoretical concepts at the operational level. Measurement in research consists of assigning numbers to empirical events in compliance with a set of rules. 1. Selecting observable empirical events 2. Using numbers or symbols to represent aspects of the events 3. Applying a mapping rule to connect the observation to the symbol **What is Measured?** Concepts used in research may be classified as: - Characteristics of the objects - A person's physical properties may be stated in terms of weight, height, posture. - Psychological properties include attitudes and intelligence. - Social properties include leadership, ability class affiliation or status. **Rules of Measurement** - Assign the numbers 1 through 7 to individuals according to how productive they are. If the individual is an unproductive worker with little output assign the numeral 1. - If a study on office computer systems is not concerned with person's depth of experience but defines people as users or nonusers, a '1' for experience with the system and a '0' for non experience with the system can be used. **Levels of Measurement** There are different levels of measurement, which provide differing amounts of information about the theoretical construct. Levels can be further differentiated in terms of the 'level' or nature of measurement that are 'continuous' or 'discrete' in their form. *Continuous variables* They have an infinite number of values that flow along a continuum. On a continuum, values can be divided and sub-divided indefinitely in mathematical theory. Even a five-point likert scale could be divided into larger number or smaller units by sub-dividing between each pair of points on the scale. *Discrete Variables* They have relatively fixed set of separate values or variable attributes. Instead of a smooth continuum of values, discrete variables contain distinct categories (e.g. sex: Male and Female). Continuous and discrete variables yield four levels of measurement (degree of precision of measurement). The four levels are: nominal, ordinal, interval and ratio. **Nominal Scale** used with variables that are qualitative in nature. The data collected are simply labels, categories or nameless without any implicit or explicit ordering of the categories or explicit ordering of the labels. The observations or subjects belong to the same category. It is the lowest level of measurement. Nominal scale does not possess any of the attributes of magnitude, equal interval or absolute zero point Example: 1. ### Sex/ Gender 2. ### The complexion of students 3. Hair Color of students **Ordinal scale** has a relative low level of property of magnitude, but it does not have the property of equal intervals between the adjacent units. This is concerned with the **ranking** or **order** of the objects measured. The level of measurement is higher than nominal. Example 1. Winners of a contest 2. Faculty Rank 3. Military Rank. **Interval scale** has its property of magnitude and equal interval between two adjacent units, but it does have an absolute zero point. The data collected can be ordered or rank. The unit measurement is constant. The level of measurement is higher than the ordinal. Example 1. Temperature in Celsius scale 2. IQ of zero does not mean total absence of knowledge. ***Ratio scale*** is the highest level of measurement scale***.*** It has all the properties of an interval scale, that is, it has magnitude and equal intervals plus the absolute zero point. There is a constant size interval between each successive unit on the measurement scale. Furthermore, there is a physical significance to this zero. Examples 1. The reaction time to a particular drug 2. The number of visits to a Doctor, 3. The weight loss of on diet individual, 4. The average score of CAT score of IT students **Criteria for good measurement** 1. *Reliability* The degree to which measures are free from error and therefore yield consistent results. The reliability of a measure indicates the stability and consistency with which the instrument measures the concept. Example: Imperfections in the measuring process that affect the assignment of scores or numbers in different ways each time a measure is taken, such as a respondent who misunderstands a question are the cause of low reliability. 2. *Validity* Is a test of how well an instrument that is concerned with whether we measure the right concept. There are two type of validity: Internal and external validity. Internal validity is concerned about issue of the authenticity of the cause-and-effect relationships. External validity is concerned about issue of the generalizability to the external environment. **Goodness of Measures** *Item Analysis* -- test whether items in the instruments should belong there. Steps include the following: 1. Calculate total score 2. Divide respondents into high and low score 3. Compute t-test for each item 4. Use only items that are significant *Reliability Analysis* -- is the measure without bias (error free) and therefore consistent across time and across items in the instrument (e.g. is it stable and consistent?). *Validity Analysis* -- is the instrument measuring the concept sets out to measure and not something else?