Research Methods PDF

RESEARCH RESEARCH - Innova'ons and breakthroughs that you come to know and enjoy are products of research. Etymologically, the word ‘research’ comes from the Middle French term ‘recherché,’ which means ‘the act of searching closely.’ Addi'on...

RESEARCH RESEARCH - Innova'ons and breakthroughs that you come to know and enjoy are products of research. Etymologically, the word ‘research’ comes from the Middle French term ‘recherché,’ which means ‘the act of searching closely.’ Addi'onally, the term ‘research’ is a combina'on of the prefix ‘re-’ (meaning ‘again’) and the word ‘search’ (meaning ‘to look for’). - In summary, research is the process of seeking informa'on once again. Its main objec've is to answer ques'ons and acquire new informa'on, whether to solve a problem or shed light on confusing facts. RESEARCH AND SCIENCE - Curiosity and Doubt: When you’re curious and doubKul about exis'ng phenomena, you seek beLer knowledge. You gather informa'on from books, the internet, and ques'ons to clarify or expand your ideas. - Informal Research: This process of informal research doesn’t strictly follow procedures but relies on various sources to find answers and solu'ons to pressing problems. - Scien=fic Approach: Science involves a systema'c approach to gaining new knowledge. It aims to describe, explain, and predict events through thorough observa'ons and controlled methods. Scien'fic knowledge relies on objec've evidence rather than personal views. - Research Validity: Scien'fic research is more accurate, reliable, and valid because it follows rigorous procedures and relies on me'culously designed studies. - Skep=cism and Casual Observa=on: Research based on casual observa'on and opinions is more suscep'ble to skep'cism, even if it provides answers. Scien'fic research ensures accuracy and benefits by embedding science in its process. SCIENTIFIC METHODS IN RESEARCH Empirical Approach Knowledge is gained through direct observa'on and experimenta'on. Only data derived from scien'fic procedures are considered factual. Disregard preconceived no'ons and personal feelings. Observa=on Your awareness of the environment generates ideas. Relying solely on awareness can lead to informa'on bias. To enhance validity, use appropriate instruments to measure observa'ons precisely. Ques=on Knowledge comes from answerable inquiries. Unanswerable ques'ons are impossible to explore realis'cally. Ques'ons must yield obtainable answers based on current scien'fic procedures. Hypotheses An educated guess explains phenomena.  Formulate testable hypotheses for analysis and predic'on. Experimenta'on validates hypotheses. Experiments Testable hypotheses ensure accurate and reliable results. The process of experimenta'on itself demonstrates scien'fic procedures. Analyses Data undergo sta's'cal analysis. Sta's'cs provide numerical evidence of validity and reliability. Minimize the chance of faulty conclusions. Conclusion Inferences should rely on concrete data, avoiding subjec've opinions. A conclusion must be objec've and supported by me'culous data analysis. Avoid adding informa'on beyond what is available in the study results. Replica=on Replica'on involves repea'ng the same study with different par'cipants. Its importance varies by discipline Purposes of replica=on Establishing reliability of findings. Discovering new knowledge or addi'onal informa'on. Assessing generalizability of results to other par'cipant groups. GOALS OF RESEARCH DESCRIBE - Refers to defining, classifying, and categorizing the phenomena being studied. Goal: Provide essen'al informa'on. PREDICT - Involves sta'ng possible consequences of present events based on exis'ng knowledge. Goal: Control ac'ons and behavior through careful planning. UNDERSTAND/EXPLAIN - Analyzing informa'on to find causes behind phenomena. Requires an established rela'onship between events. Other explana'ons of causality must be ruled out. IMPORTANCE OF RESEARCH - Knowledge establishment. - Correc'on of percep'ons. - Valida'on of phenomena. - Tes'ng effec'veness of solu'ons CONSTRUCTS - Constructs are mental abstrac'ons derived from an area of interest or a problem. They represent ideas that need inves'ga'on. VARIABLES - In research, constructs are called variables. Variables can be understood differently due to differences in values. For example, height is a variable with descrip'ons like small, average, and tall. To standardize and quan'fy variables, they become the focus of study. Direct observa=on variables are easily gauged by the senses (e.g., size, brightness, odor, taste). Indirect observa=on variables require tools or instruments (o]en abstract constructs). KINDS OF VARIABLES Independent Variable - Manipulated variables that cause a change in another variable. O]en treatments or condi'ons that produce varied responses or effects. Example: In a study on reducing test anxiety, the “peace-loving learning environment” is the independent variable. Dependent Variables - Affected by independent variables. Also called the outcome variable. Represent responses or effects resul'ng from treatments or condi'ons. Example: In the same study, “test anxiety” is the dependent variable. Confounding or Extraneous Variable - Impact the dependent variable. Need to be controlled to minimize their effect. Example: Family background of grade school students affec'ng the impact of the learning environment variable. Categorical Variable - Describe data quality. Classified into mutually exclusive categories (nominal) or ordered categories (ordinal). Examples: Civil status (single, married, widowed) and size (small, medium, large). Discrete Variable - These variables can only assume specific, dis'nct values that you cannot subdivide. Typically, you count them, and the results are integers. Con=nuous Variable - These variables can take on any numeric value within a defined range. They can be meaningfully split into smaller parts, allowing for frac'onal and decimal values. Quan=ta=ve Variable - Provide details about the number or level of something. Count frequency of responses or effects. Example: Popularity contest votes. Qualita=ve Variable - Represent kinds or types of objects. O]en synonymous with categorical variables. May use numeric codes for measurement. Why is it important to study the levels of measurement? - Proper Interpreta=on of Data Understanding levels of measurement helps interpret data related to variables. For example, when gender is categorized as male and female, knowing the quan'ty of par'cipants in each category is essen'al. - Decisions on Sta=s=cal Analysis The choice of sta's'cal analysis depends on how a variable is measured. If comparing the quan'ty of males vs. females, simple frequency and averages suffice. FOUR LEVELS OF MEASUREMENTS - Nominal Scales - Concerned with names and categories. - Ordinal Scales- Used for ranked data. Allows comparison of degree. - Interval Scales- Equal units of measurement. No true zero point. - Ra=o Scales- Highest level. Uses zero as base point. Allows comparison of differences and rela've magnitudes. MAJOR APPROACHES Qualita=ve Research - Qualita've research aims to provide a detailed understanding of characteris'cs, kinds, and quality related to a subject or event. Researchers use methods like in-depth interviews and narra've descrip'ons. - Data Type: Qualita've variables (non-numerical data) are obtained. - Examples: Studies on lived experiences of male convicts, emo'ons of people who suffered loss, or a poli'cian’s perspec've on morality. - Advantages: Provides rich descrip'ons of real experiences. Allows for in-depth explora'on and elabora'on by par'cipants. Helps understand abstract factors (customs, tradi'ons, family roles, etc.). - Disadvantages: Lack of sta's'cal analysis due to non-numeric data. Limited sample size affects data credibility. Subjec've bias from the researcher’s perspec've. Quan=ta=ve Research - Quan'ta've research systema'cally describes large collec'ons of things. It involves hypothesis tes'ng and mechanis'c understanding. - The quan'ta've approach involves making predic'ons and describing events using numerical figures. It relies on sta's'cal analysis to interpret data. - Data Type: Quan'ta've variables (numeric data) are collected.  - Examples: Surveys, experiments, sta's'cal analyses. - Advantages: Generates reproducible knowledge. Allows sta's'cal analysis. Confirmatory Method: It follows a scien'fic method by tes'ng hypotheses. Bias Reduc'on: By examining numerical data, bias is minimized. Generalizability: Findings can be applied to larger popula'ons due to opera'onal defini'ons of variables. - Disadvantages: Requires larger samples. Sta's'cal training needed for analysis. Limited Focus: Only focuses on the object under inves'ga'on. Narrow Explana'ons: Interpreta'ons are based solely on sta's'cal data. Mixed Methods - Researchers combine both qualita've and quan'ta've approaches. - Useful for comprehensive analysis and addressing diverse research ques'ons. - Advantages: Rich Explana'on: Qualita've data provides a deeper understanding. Validity and Reliability: Combining both methods enhances the study’s validity and reliability. - Disadvantages: Time-Consuming: Integra'ng both approaches takes longer. Guideline Challenges: Few guidelines for applying both methods can lead to discrepancies in findings. MAIN CHARACTERISTICS OF QUANTITATIVE APPROACH Data Collec=on: Quan'ta've research gathers data using structured research instruments (e.g., ques'onnaires or surveys). Sample Sizes: Results are based on larger sample sizes that represent the popula'on. Replicability: The study can be replicated or repeated due to its high reliability. Clearly Defined Research Ques=on: Researchers seek objec've answers to well-defined research ques'ons. Careful Design: All study aspects are carefully planned before data collec'on. Numerical Data: Data are expressed in numbers and sta's'cs. Generaliza=on and Predic=on: The approach allows generalizing concepts widely, predic'ng future outcomes, and inves'ga'ng causal rela'onships. Broad Study Scope: Quan'ta've research involves a greater number of subjects, enhancing result generaliza'on. Objec=vity and Accuracy: Study results are more objec've and accurate. Replicability: When the right procedures are followed, quan'ta've research can be replicated and compared with similar works.  Summarizing Informa=on: Researchers can summarize extensive data sources and make cross-category comparisons. Avoiding Bias: By maintaining distance from subjects and using neutral facilitators, personal bias can be minimized. LIMITATIONS OF QUANTITATIVE APPROACH - Contextual Limita=on: While quan'ta've data can test hypotheses, they may be limited in explaining context. The focus on numerical results some'mes overlooks the broader context. - Ar=ficial SeXng: Research is o]en conducted in controlled environments, which may not fully reflect real-life situa'ons. Addi'onally, research tools may introduce bias from the researcher’s perspec've. TYPES OF QUANTITATIVE RESEARCH - Descrip=ve Research Purpose: Collect data to test hypotheses or describe study variables. Data Collec=on: Typically, numeric data gathered through surveys, interviews, or observa'ons. Applica=on: Common in science, technology, engineering, and social sciences. - Correla=onal Research Purpose: Determine the level of rela'on between quan'fiable variables. Correla=on: Does not imply causa'on but helps predict variable values. - Casual – Coopera=ve Research Purpose: Establish cause-effect rela'onships. Independent Variable: O]en a demographic factor (e.g., gender, race). - Experimental Research Purpose: Measure the effect of an independent variable (cause) on a dependent variable (effect). Control: Researchers can manipulate independent variables, and par'cipants are randomly assigned. - Quasi- Experimental Research Purpose: Determine causes and effects when full experimental control is not possible. Applica=on: Used for naturally occurring phenomena and their impact on people.