Research Methods PDF

RESEARCH METHODS  CHAPTER 3: RESEARCH METHODS INTRODUCTION RESEARCH DESIGN PARTICIPANTS OF THE STUDY SAMPLING PROCEDURE RESEARCH INSTRUMENT DATA GATHERING PROCEDURES DATA ANALYSIS ETHICAL CONSIDERATIONS 2 ...

RESEARCH METHODS  CHAPTER 3: RESEARCH METHODS INTRODUCTION RESEARCH DESIGN PARTICIPANTS OF THE STUDY SAMPLING PROCEDURE RESEARCH INSTRUMENT DATA GATHERING PROCEDURES DATA ANALYSIS ETHICAL CONSIDERATIONS 2  Research design is the framework of research methods and RESEARCH DESIGN techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success. The Process of Research Design 1. The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results. 2. Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study. 3. Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives. 4. Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random, stratified random sampling, or convenience sampling. 5. Choose your data collection methods: Decide on the data collection methods, such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data. 6. Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations. 7. Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis, content analysis, or discourse analysis, and plan how to interpret the results. 4 5 PARTICIPANTS OF THE STUDY Study participants are people who voluntarily take part in a research study and provide data to researchers. They are also known as subjects, research participants, or respondents. Participants contribute to research by: answering questions, completing tasks, and undergoing observations. 6 SAMPLING PROCEDURE When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research. To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method. There are two primary types of sampling methods that you can use in your research: Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, SAMPLING allowing you to easily collect data. PROCEDURE First, you need to understand the difference between a population and a sample, and identify the target population of your research. The population is the entire group that you want to draw conclusions about. The sample is the specific group of individuals that you will collect data from. The population can be defined in terms of geographical location, age, income, or many other characteristics. POPULATION VS. SAMPLE 9 The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis. SAMPLE SIZE 10 Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice. PROBABILITY SAMPLING METHODS 11 In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included. This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias. That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible. Non-probability sampling techniques are often used in exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population. NON-PROBABILITY SAMPLING METHOD 12 RESEARCH INSTRUMENT A Research Instrument is a tool used to collect, measure, and analyze data related to your research interests. A research instrument can include interviews, tests, surveys, or checklists. The Research Instrument is usually determined by researcher and is tied to the study methodology. This document offers some examples of research instruments and study methods. 13 CHARACTERISTICS OF A GOOD RESEARCH INSTRUMENT Valid and reliable Based on a conceptual framework, or the researcher's understanding of how the particular variables in the study connect with each other Must gather data suitable for and relevant to the research topic Able to test hypothesis and/or answer proposed research questions under investigation Free of bias and appropriate for the context, culture, and diversity of the study site Contains clear and definite instructions to use the instrument 14 TYPES OF RESEARCH INSTRUMENTS: INTERVIEWS Interviews or the interaction where verbal questions are posed by an interviewer to elicit verbal responses from an interviewee. Structured Interview: A formal set of questions posed to each interviewee and recorded using a standardized procedure. Unstructured Interview: A less formal set of questions; the interviewer modifies the sequence and wording of questions. Non-Directive Interview: An unguided interview, including open-ended questions and use of spontaneous engagement. Focus Interview: An emphasis on the interviewees subjective and personal responses where the interviewer engages to elicit more information. Focus Group Interview: A group of selected participants are asked about their opinion or perceptions concerning a particular topic. 15 TYPES OF RESEARCH INSTRUMENTS: OBSERVATIONS Observation (watching what people do) is a type of correlational (non - experimental) method where researchers observe ongoing behavior. Structured Observations: Research conducted at a specific place, time, where participants are observed in a standardised procedure. Rather than writing a detailed description of all behaviors observed, researchers code observed behaviors according to a previously agreed upon scale. Naturalistic Observation: The study the spontaneous behavior of participants in natural surroundings. The researcher simply records what they see in whatever way they see it. Participant Observation: A variation on natural observations where the researcher joins in and becomes part of the group they are studying to get a deeper insight into their lives. 16 TYPES OF RESEARCH INSTRUMENTS: SURVEYS Survey research encompasses any measurement procedures that involve asking questions of respondents. The types of surveys can vary on the span of time used to conduct the study. They can be comprised of cross-sectional surveys and/or longitudinal surveys. Types of questions asked in surveys include: Free-Answer: Also referred to as open-ended questions, these include unrestricted, essay, or unguided questions. Guided Response Type: Recall-type questions asking the participant to recall a set of categories. Multiple-choice or multiple response questions. 17 DATA GATHERING PROCEDURES Data gathering is the first and most important step in the research process, regardless of the type of research being conducted. It entails collecting, measuring, and analyzing information about a specific subject and is used by businesses to make informed decisions. 18 TYPES OF DATA: QUALITATIVE DATA This type of data can’t be measured or expressed as a number. It s less structured than quantitative data. Qualitative data is information acquired to understand more about a research subject’s underlying motivations—answering “how” and “why” questions. It is information that is descriptive in nature and can consist of words, pictures, or symbols, which is why it isn’t easily measurable. Qualitative data is obtained through the answers to open-ended questions that allow study participants to answer in their own words. When asked on a survey, an open text box is used for answers. Examples of questions that will yield qualitative data are: How do you feel about using products from XYZ brand? You indicated that you prefer product A. Why is that your favorite laundry detergent? 19 TYPES OF DATA: QUANTITATIVE DATA Quantitative data is structured and can be Examples of quantitative analyzed statistically. Expressed in numbers, research questions are: the data can be used to measure variables. The How often do you How many containers of results are objective and conclusive. Questions purchase laundry laundry detergent do you used to collect quantitative data are usually detergent? purchase at one time? “how many,” “how much,” or “how often?” Once weekly 1 Quantitative data can be measured by numerical variables, analyzed through Every two weeks 2 statistical methods, and represented in charts Once a month 3 and graphs. Other Another amount 20 METHODS OF DATA GATHERING Surveys Forms In-person interviews Focus groups Customer observation Online tracking 21  DATA ANALYSIS Data analysis is important across various domains and industries. It helps with: Decision Making: Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes. Problem Solving: Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency. Performance Evaluation: Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics. Gathering Insights: Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand. Risk Management: Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts. By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line. 22  TYPES OF DATA ANALYSIS Descriptive Analysis Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions. Diagnostic Analysis Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process. 23  TYPES OF DATA ANALYSIS Predictive Analysis Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e- commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers. Prescriptive Analysis Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences. 24  DATA ANALYSIS METHODS Statistical Analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables. Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 25  DATA ANALYSIS METHODS Text mining involves analyzing unstructured data, such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction. Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data. For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution. Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data. 26  PARAMETRIC V NON-PARAMETRIC Parametric Test Definition In Statistics, a parametric test is a kind of hypothesis test which gives generalizations for generating records regarding the mean of the primary/original population. The t-test is carried out based on the students’ t-statistic, which is often used in that value. The t-statistic test holds on the underlying hypothesis, which includes the normal distribution of a variable. In this case, the mean is known, or it is considered to be known. For finding the sample from the population, population variance is identified. It is hypothesized that the variables of concern in the population are estimated on an interval scale. Non-Parametric Test Definition The non-parametric test does not require any population distribution, which is meant by distinct parameters. It is also a kind of hypothesis test, which is not based on the underlying hypothesis. In the case of the non-parametric test, the test is based on the differences in the median. So this kind of test is also called a distribution-free test. The test variables are determined on the nominal or ordinal level. If the independent variables are non-metric, the non-parametric test is usually performed. 27  PARAMETRIC V NON-PARAMETRIC The key difference between parametric and nonparametric test is that the parametric test relies on statistical distributions in data whereas nonparametric do not depend on any distribution. Non-parametric does not make any assumptions and measures the central tendency with the median value. Some examples of non-parametric tests include Mann-Whitney, Kruskal-Wallis, etc. Parametric is a statistical test which assumes parameters and the distributions about the population are known. It uses a mean value to measure the central tendency. These tests are common, and therefore the process of performing research is simple. 28 PARAMETRIC V NON-PARAMETRIC 29  PARAMETRIC V NON-PARAMETRIC Additionally, Spearman’s correlation is a nonparametric alternative to Pearson’s correlation. Use Spearman’s correlation for nonlinear, monotonic relationships and for ordinal data. 30 STATISTICAL TOOLS 31 STATISTICAL TOOLS 32 STATISTICAL TOOLS 33 STATISTICAL TOOLS 34 STATISTICAL TOOLS 35 STATISTICAL TOOLS 36 STATISTICAL TOOLS 37 STATISTICAL TOOLS 38 STATISTICAL TOOLS 39 STATISTICAL TOOLS 40 STATISTICAL TOOLS 41 STATISTICAL TOOLS 42 STATISTICAL TOOLS 43 STATISTICAL TOOLS 44 STATISTICAL TOOLS 45 STATISTICAL TOOLS 46 STATISTICAL TOOLS 47 STATISTICAL TOOLS 48 STATISTICAL TOOLS 49 STATISTICAL TOOLS 50 STATISTICAL TOOLS 51 ETHICAL CONSIDERATIONS 52 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY INTRODUCTION 53 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY INTRODUCTION 54 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY RESEARCH DESIGN 55 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY RESEARCH DESIGN 56 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY PARTICIPANTS OF THE STUDY 57 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY PARTICIPANTS OF THE STUDY 58 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY SAMPLING PROCEDURE 59 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY RESEARCH INSTRUMENT 60 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY DATA GATHERING PROCEDURE 61 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY DATA GATHERING PROCEDURE 62 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY DATA ANALYSIS PROCEDURE 63 SAMPLE OF CHAPTER 3: RESEARCH METHODS/ METHODOLOGY ETHICAL CONSIDERATION 64 END OF PRESENTATION 65