Research Methodology Course for Terminal Examination PDF

LECTURE 1 What is a Research Question? A research question is the main question that your study sought or is seeking to answer. A clear research question guides your research paper or thesis and states exactly what you want to find out, giving your work a focus and objective. Learning how t...

LECTURE 1 What is a Research Question? A research question is the main question that your study sought or is seeking to answer. A clear research question guides your research paper or thesis and states exactly what you want to find out, giving your work a focus and objective. Learning how to write a hypothesis or research question is the start to composing any thesis, dissertation, or research paper. It is also one of the most important sections of a research proposal. A good research question not only clarifies the writing in your study; it provides your readers with a clear focus and facilitates their understanding of your research topic, as well as outlining your study’s objectives. Before drafting the paper and receiving research paper editing (and usually before performing your study), you should write a concise statement of what this study intends to accomplish or reveal. A good research question should: Be clear and provide specific information so readers can easily understand the purpose. Be focused in its scope and narrow enough to be addressed in the space allowed by your paper Be relevant and concise and express your main ideas in as few words as possible, like a hypothesis. Be precise and complex enough that it does not simply answer a closed “yes or no” question, but requires an analysis of arguments and literature prior to its being considered acceptable. Be arguable or testable so that answers to the research question are open to scrutiny and specific questions and counterarguments. The research question should be specific and focused Research questions that are too broad are not suitable to be addressed in a single study. One reason for this can be if there are many factors or variables to consider. In addition, a sample data set that is too large or an experimental timeline that is too long may suggest that the research question is not focused enough. A specific research question means that the collective data and observations come together to either confirm or deny the chosen hypothesis in a clear manner. If a research question is too vague, then the data might end up creating an alternate research problem or hypothesis that you haven’t addressed in your Introduction section. The research question should be based on the literature An effective research question should be answerable and verifiable based on prior research because an effective scientific study must be placed in the context of a wider academic consensus. This means that conspiracy or fringe theories are not good research paper topics. Instead, a good research question must extend, examine, and verify the context of your research field. It should fit naturally within the literature and be searchable by other research authors. The research question should be realistic in time, scope, and budget There are two main constraints to the research process: timeframe and budget. A proper research question will include study or experimental procedures that can be executed within a feasible time frame, typically by a graduate doctoral or master’s student or lab technician. Research that requires future technology, expensive resources, or follow-up procedures is problematic. A researcher’s budget is also a major constraint to performing timely research. Research at many large universities or institutions is publicly funded and is thus accountable to funding restrictions. The research question should be in-depth Research papers, dissertations and theses, and academic journal articles are usually dozens if not hundreds of pages in length. A good research question or thesis statement must be sufficiently complex to warrant such a length, as it must stand up to the scrutiny of peer review and be reproducible by other scientists and researchers. LECTURE 2  Hypothesis Definition  Term derived from Greek word, Latin term hupothesis which gives the meaning of ‘to put under’ or ‘to suppose’.  A hypothesis is a preliminary or tentative explanation by the researcher of what the researcher considers the outcome of an investigation will be  It is an informed/educated guess  It indicates the expectations of the researcher regarding certain variables.  It is the most specific way in which an answer to a problem can be stated  A hypothesis is never to be stated as a question, but always as a statement with an explanation following it.  Usage of the Term  In early usage, scholars often referred to a clever idea or to a convenient mathematical approach that simplified cumbersome calculations as a hypothesis- when used this way, the word did not necessarily have any specific meaning  In common usage in the 21st century, a hypothesis refers to a provisional idea whose merit requires evaluation  DIFFERENCE BETWEEN A HYPOTHESIS AND A PROBLEM  A problem is formulated in the form of a question; it serves as the basis or origin from which an hypothesis is derived  A hypothesis is a suggested solution to a problem. A problem cannot be directly tested, whereas a hypothesis can be tested and verified  Both a hypothesis and a problem contribute to the body of knowledge which supports or refutes an existing theory  When Is A Hypothesis Formulated?  A hypothesis is formulated after the problem has been stated and the literature study has been concluded  It is formulated when the researcher is totally aware of the theoretical and empirical background to the problem  The Purpose & Function of a Hypothesis  It offers explanations for the relationships between those variables that can be empirically tested  It furnishes proof that the researcher has sufficient background knowledge to enable him/her to make suggestions in order to extend existing knowledge  It gives direction to an investigation.  It indicates the next phase in the investigation and therefore furnishes continuity to the examination of the problem  CHARACTERISTICS OF A HYPOTHESIS  It should have elucidating power  It should strive to furnish an acceptable explanation of the phenomenon It must be verifiable  It must be formulated in simple, understandable terms  It should correspond with existing knowledge  TYPES OF HYPOTHESES  Hypotheses can be classified in terms of their derivation  Inductive Hypothesis Deductive Hypothesis Hypotheses can also be classified in terms of their formulation Research Hypothesis Statistical or Null Hypothesis  TYPES OF HYPOTHESES (contd.)  Inductive Hypothesis  When the researcher arrives at a conclusion based on a set of observations made by the researcher. An hypothesis based on inductive reasoning can lead to a more careful study of a situation  Deductive Hypothesis  When the researcher makes conclusions based on previously known facts. Considered more valid form of reasoning.  Research Hypothesis  It is a relationship between variables and indicates the nature of the relationship For Example:  If A is valid, B follows  If you hit a child with a cane, he/she will cry  Schools in which pupil-teacher relations are open/friendly will have less unrest than comparable schools where pupil-teacher relations are closed/tense  Statistical or Null Hypothesis (H0)  A null hypothesis is a statistical hypothesis that is tested for possible rejection under the assumption that it is true For Example:  There is no difference between pupil-teacher relations in unrest schools and pupil- teacher relations in comparable schools that experience no unrest  Formulating A Research Hypothesis  To formulate a research hypothesis:  We start with a research question and;  generate operational definitions for all variables and;  formulate a research hypothesis keeping in mind expected relationships or differences and operational definitions  Hypotheses and Prediction  A useful hypothesis is a testable statement which may include a prediction  Hypotheses are understood in terms of the particular independent and dependent variables that the researcher uses in the study  How Are Hypotheses Written?  Chocolate may cause pimples Salt in soil may affect plant growth Plant growth may be affected by the color of the light Bacterial growth may be affected by temperature Ultra violet light may cause skin cancer Temperature may cause leaves to change color  All of these are examples of hypotheses because they use the tentative word "may”  However, using the word may does not suggest how the researcher would go about proving it.  If these statements had not been written carefully, they may not have even been hypotheses at all  For example, if we say "Trees will change color when it gets cold" we are making a prediction  Or if we write, "Ultraviolet light causes skin cancer“, it could be a conclusion  Formalized Hypothesis  Notice that the example statements contain the words, if and then. They are necessary in a formalized hypothesis. But not all if-then statements are hypotheses. For example, "If I play the lottery, then I will get rich." This is a simple prediction.  In a formalized hypothesis, a tentative relationship is stated. For example, “if the frequency of winning is related to frequency of buying lottery tickets, “THEN” is followed by a prediction of what will happen if you increase or decrease the frequency of buying lottery tickets. LECTURE 3  What is research design? ❖ Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final data analysis. ❖ A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study. ❖ Understanding different types of research designs is essential as helps ensure that your approach is suitable given your research aims, objectives and questions, as well as the resources you have available to you. ❖ Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling, data collection and data analysis decisions.  What is research design? ❖ Some sources claim that the three research design types are qualitative, quantitative and mixed methods, which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). ❖ Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology. Research Design: Quantitative Studies Research Design: Quantitative Studies ❖ Quantitative research involves collecting and analyzing data in a numerical form. ❖ Broadly speaking, there are four types of quantitative research designs: descriptive, correlational, experimental, and quasi-experimental.  Descriptive Research Design ❖ As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. ❖ In other words, there is no intervention on the researcher’s part – only data collection. ❖ For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. ❖ The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.  Descriptive Research Design ❖ The key defining attribute of this type of research design is that it purely describes the situation. ❖ In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. ❖ Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics. ❖ By doing so, it can provide valuable insights and is often used as a precursor to other research design types.  Correlational Research Design ❖ Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them. ❖ In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing. ❖ For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. ❖ In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. ❖ Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).  Correlational Research Design ❖ As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. ❖ It is particularly helpful in terms of developing predictions, and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs ❖ That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality. In other words, correlation does not equal causation.  Experimental Research Design ❖ Experimental research design is used to determine if there is a causal relationship between two or more variables. With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). ❖ Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality. ❖ For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. ❖ You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.  Experimental Research Design ❖ Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. ❖ However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes, which can reduce the statistical power and generalisability of the results. ❖ Moreover, experimental research design requires random assignment. This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling). ❖ Doing so helps reduce the potential for bias and confounding variables. This need for random assignment can lead to ethics-related issues. ❖ For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.  Quasi-Experimental Research Design ❖ A quasi-experimental research design is used when the research aims to identify causal relations, but participants cannot (or don’t want to) be randomly assigned to different groups (for practical or ethical reasons). ❖ Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison. ❖ For example, suppose you were studying the effects of a new teaching method on student achievement in a particular school district. In that case, you may be unable to assign students to either group randomly and instead have to choose classes or schools that already use different teaching methods. ❖ This way, you still achieve separate groups, without having to assign participants to specific groups yourself.  Quasi-Experimental Research Design ❖ Naturally, quasi-experimental research designs have limitations when compared to experimental designs. ❖ Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings. ❖ All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. ❖ What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables. LECTURE 4  What is Data Collection? ❖ Data collection is the process of collecting and evaluating information or data from multiple sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities. ❖ It is an essential phase in all types of research, analysis, and decision-making, including that done in the social sciences, business, and healthcare. ❖ During data collection, researchers must identify the data types, the sources of data, and the methods being used. ❖ We will soon see that there are many different data collection methods. Data collection is heavily reliance on in research, commercial, and government fields. ❖ Before an analyst begins collecting data, they must answer three questions first: What’s the goal or purpose of this research? What kinds of data are they planning on gathering? What methods and procedures will be used to collect, store, and process the information? ❖ Additionally, we can divide data into qualitative and quantitative types. ❖ Qualitative data covers descriptions such as color, size, quality, and appearance. ❖ Unsurprisingly, quantitative data deals with numbers, such as statistics, poll numbers, percentages, etc.  Why Do We Need Data Collection? ❖ Before a judge makes a ruling in a court case or a general creates a plan of attack, they must have as many relevant facts as possible. ❖ The best courses of action come from informed decisions, and information and data are synonymous. ❖ The concept of data collection isn’t new, as we’ll see later, but the world has changed. ❖ Today, there is far more data available, and it exists in forms that were unheard of a century ago. ❖ The data collection process has had to change and grow, keeping pace with technology. ❖ Whether you’re in academia, trying to conduct research, or part of the commercial sector, thinking of how to promote a new product, you need data collection to help you make better choices. ❖ Now that you know what data collection is and why we need it, let's look at the different methods of data collection. ❖ Data collection could mean a telephone survey, a mail-in comment card, or even some guy with a clipboard asking passersby some questions. But let’s see if we can sort the different data collection methods into a semblance of organized categories.  What Are the Different Data Collection Methods? ❖ Primary and secondary methods of data collection are two approaches used to gather information for research or analysis purposes. Let's explore each data collection method in detail: 1. Primary Data Collection ❖ The first techniques of data collection is Primary data collection which involves the collection of original data directly from the source or through direct interaction with the respondents. ❖ This method allows researchers to obtain firsthand information tailored to their research objectives. There are various techniques for primary data collection, including: ❖ a. Surveys and Questionnaires: Researchers design structured questionnaires or surveys to collect data from individuals or groups. These can be conducted through face-to-face interviews, telephone calls, mail, or online platforms. b. Interviews: Interviews involve direct interaction between the researcher and the respondent. They can be conducted in person, over the phone, or through video conferencing. Interviews can be structured (with predefined questions), semi-structured (allowing flexibility), or unstructured (more conversational). c. Observations: Researchers observe and record behaviors, actions, or events in their natural setting. This method is useful for gathering data on human behavior, interactions, or phenomena without direct intervention. d. Experiments: Experimental studies involve manipulating variables to observe their impact on the outcome. Researchers control the conditions and collect data to conclude cause-and-effect relationships. e. Focus Groups: Focus groups bring together a small group of individuals who discuss specific topics in a moderated setting. This method helps in understanding the opinions, perceptions, and experiences shared by the participants.  2. Secondary Data Collection ❖ The next techniques of data collection is Secondary data collection which involves using existing data collected by someone else for a purpose different from the original intent. Researchers analyze and interpret this data to extract relevant information. Secondary data can be obtained from various sources, including: ❖ a. Published Sources: Researchers refer to books, academic journals, magazines, newspapers, government reports, and other published materials that contain relevant data. ❖ b. Online Databases: Numerous online databases provide access to a wide range of secondary data, such as research articles, statistical information, economic data, and social surveys. ❖ c. Government and Institutional Records: Government agencies, research institutions, and organizations often maintain databases or records that can be used for research purposes. ❖ d. Publicly Available Data: Data shared by individuals, organizations, or communities on public platforms, websites, or social media can be accessed and utilized for research. ❖ e. Past Research Studies: Previous research studies and their findings can serve as valuable secondary data sources. Researchers can review and analyze the data to gain insights or build upon existing knowledge.  Data Collection Tools ❖ Now that we’ve explained the various techniques let’s narrow our focus even further by looking at some specific tools. For example, we mentioned interviews as a technique, but we can further break that down into different interview types (or “tools”). Word Association ❖ The researcher gives the respondent a set of words and asks them what comes to mind when they hear each word. Sentence Completion ❖ Researchers use sentence completion to understand the respondent's ideas. This tool involves giving an incomplete sentence and seeing how the interviewee finishes it. Role-Playing ❖ Respondents are presented with an imaginary situation and asked how they would act or react if it were real. In-Person Surveys The researcher asks questions in person. Online/Web Surveys These surveys are easy to accomplish, but some users may be unwilling to answer truthfully, if at all. Mobile Surveys These surveys take advantage of the increasing proliferation of mobile technology. Mobile collection surveys rely on mobile devices like tablets or smartphones to conduct surveys via SMS or mobile apps. Phone Surveys No researcher can call thousands of people at once, so they need a third party to handle the chore. However, many people have call screening and won’t answer. Observation Sometimes, the simplest method is the best. Researchers who make direct observations collect data quickly and easily, with little intrusion or third-party bias. Naturally, this method is only effective in small-scale situations. LECTURE 5 Reliability vs. Validity in Research | Difference, Types and Examples ❖ Reliability and validity are concepts used to evaluate the quality of research. ❖ They indicate how well a method or technique is. Or test measures something. ❖ Reliability is about the consistency of a measure, and validity is about the accuracy of a measure. ❖ It’s important to consider reliability and validity when you are creating your research design, planning your methods, and writing up your results, especially in quantitative research. ❖ Failing to do so can lead to several types of research bias and seriously affect your work.  Understanding reliability vs validity ❖ Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable. What is reliability? ❖ Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable. EXAMPLES ❖ You measure the temperature of a liquid sample several times under identical conditions. The thermometer displays the same temperature every time, so the results are reliable. ❖ A doctor uses a symptom questionnaire to diagnose a patient with a long-term medical condition. Several different doctors use the same questionnaire with the same patient but give different diagnoses. This indicates that the questionnaire is low-reliability as a measure of the condition.  What is validity? ❖ Validity refers to how accurately a method measures what it is intended to measure. ❖ If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world. ❖ High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid. EXAMPLES ❖ Suppose the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same. In that case, the thermometer is probably malfunctioning, and therefore its measurements are not valid. ❖ If a symptom questionnaire results in a reliable diagnosis when answered at different times and with different doctors, this indicates that it has high validity as a measurement of the medical condition. ❖ However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation. ❖ The thermometer that you used to test the sample gives reliable results. However, the thermometer has not been calibrated properly, so the result is 2 degrees lower than the true value. Therefore, the measurement is not valid. ❖ A group of participants take a test designed to measure working memory. The results are reliable, but participants’ scores correlate strongly with their level of reading comprehension. This indicates that the method might have low validity: the test may be measuring participants’ reading comprehension instead of their working memory. ❖ Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid. How are reliability and validity assessed? ❖ Reliability can be estimated by comparing different versions of the same measurement. ❖ Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theories. ❖ Methods of estimating reliability and validity are usually split up into different types. Types of validity The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.  Where to write about reliability and validity in a thesis It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper. Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.