WHAT IS RESEARCH_.pdf

PRACTICAL RESEARCH 2 LESSON #1 | WHAT IS RESEARCH? WHAT IS RESEARCH? Informal Research: Innovations and breakthroughs This process of informal research that you come to know and enjoy doesn’t strictly follow p...

PRACTICAL RESEARCH 2 LESSON #1 | WHAT IS RESEARCH? WHAT IS RESEARCH? Informal Research: Innovations and breakthroughs This process of informal research that you come to know and enjoy doesn’t strictly follow procedures are products of research. but relies on various sources to find answers and solutions to pressing Etymologically, the word ‘research’ problems. comes from the Middle French Scientific Approach: term ‘recherché,’ which means ‘the Science involves a systematic act of searching closely.’ approach to gaining new Additionally, the term ‘research’ is a knowledge. It aims to describe, combination of the prefix ‘re-’ explain, and predict events through (meaning ‘again’) and the word thorough observations and ‘search’ (meaning ‘to look for’). controlled methods. Scientific knowledge relies on objective In summary, research is the evidence rather than personal process of seeking information views. once again. Research Validity: Its main objective is to answer Scientific research is more questions and acquire new accurate, reliable, and valid information, whether to solve a because it follows rigorous problem or shed light on confusing procedures and relies on facts. meticulously designed studies. RESEARCH AND SCIENCE Skepticism and Casual Observation: Curiosity and Doubt: Research based on casual observation and opinions is more When you’re curious and doubtful susceptible to skepticism, even if it about existing phenomena, you provides answers. Scientific seek better knowledge. You gather research ensures accuracy and information from books, the benefits by embedding science in internet, and questions to clarify or its process. expand your ideas.  SCIENTIFIC METHODS IN RESEARCH Experiments: Testable hypotheses ensure Empirical Approach: accurate and reliable results. Knowledge is gained through direct The process of experimentation observation and experimentation. itself demonstrates scientific Only data derived from scientific procedures. procedures are considered factual. Disregard preconceived notions Analyses: and personal feelings. Data undergo statistical analysis. Statistics provide numerical Observation: evidence of validity and reliability. Your awareness of the environment Minimize the chance of faulty generates ideas. conclusions. Relying solely on awareness can lead to information bias. Conclusion: To enhance validity, use Inferences should rely on concrete appropriate instruments to data, avoiding subjective opinions. measure observations precisely. A conclusion must be objective and supported by meticulous data Question: analysis. Knowledge comes from answerable Avoid adding information beyond inquiries. what is available in the study Unanswerable questions are results. impossible to explore realistically. For example, if student scores Questions must yield obtainable increased after tutorial classes, answers based on current scientific focus on that specific data rather procedures. than introducing unrelated factors. Hypotheses: Replication: An educated guess explains Replication involves repeating the phenomena. same study with different Formulate testable hypotheses for participants. analysis and prediction. Its importance varies by discipline. Experimentation validates hypotheses. Purposes of replication: CONSTRUCTS Establishing reliability of findings. Constructs are mental abstractions Discovering new knowledge or derived from an area of interest or additional information. a problem. They represent ideas Assessing generalizability of results that need investigation. to other participant groups. VARIABLE WHAT ARE THE GOALS OF RESEARCH In research, constructs are called variables. Variables can be DESCRIBE understood differently due to Refers to defining, classifying, and differences in values. categorizing the phenomena being For example, height is a variable studied. with descriptions like small, Goal: Provide essential information. average, and tall. To standardize and quantify variables, they PREDICT become the focus of study. Involves stating possible consequences of present events Direct observation variables based on existing knowledge. are easily gauged by the senses Goal: Control actions and behavior (e.g., size, brightness, odor, taste). through careful planning. Indirect observation variables UNDERSTAND/EXPLAIN require tools or instruments (often Analyzing information to find abstract constructs). causes behind phenomena. Requires an established Variables must be measured to generate relationship between events. Other data for analysis. explanations of causality must be ruled out. Before measuring a variable, define it based on its purpose in the study IMPORTANCE OF RESEARCH (operational definition not lexical). Knowledge establishment. Correction of perceptions. Validation of phenomena. Testing effectiveness of solutions. Problem-solving.  KIND OF VARIABLES CATEGORICAL VARIABLES Describe data quality. INDEPENDENT VARIABLES Classified into mutually exclusive Manipulated variables that cause a categories (nominal) or ordered change in another variable. Often categories (ordinal). treatments or conditions that Examples: Civil status (single, produce varied responses or married, widowed) and size (small, effects. medium, large). Example: In a study on reducing test anxiety, the “peace-loving DISCRETE VARIABLES learning environment” is the Discrete data cannot have independent variable. fractional or decimal values. These variables can only assume DEPENDENT VARIABLES specific, distinct values that you Affected by independent variables. cannot subdivide. Also called the outcome variable. Typically, you count them, and the Represent responses or effects results are integers. resulting from treatments or conditions. Examples: The number of cats in an animal Example: In the same study, “test shelter. anxiety” is the dependent variable. The number of books you check out from the library. CONFOUNDING/EXTRANEOUS VARIABLE The number of heads in a sequence Impact the dependent variable. of coin tosses. Need to be controlled to minimize The result of rolling a die. their effect. The population of a country (since Confounding or Extraneous it’s counted in whole numbers). Variables: Example: Family background of grade school students affecting the impact of the learning environment variable. CONTINUOUS VARIABLE Why is it important to study the levels of Continuous data have an infinite measurement? number of potential values between any two points. PROPER INTERPRETATION OF DATA These variables can take on any Understanding levels of numeric value within a defined measurement helps interpret data range. related to variables. They can be meaningfully split into For example, when gender is smaller parts, allowing for fractional categorized as male and female, and decimal values. knowing the quantity of Examples: participants in each category is Height of individuals. essential Temperature (measured with decimal places). Decisions on Statistical Analysis: Time taken to complete a task. The choice of statistical analysis Weight. depends on how a variable is measured. QUANTITATIVE VARIABLES If comparing the quantity of males vs. females, simple frequency and Provide details about the number or averages suffice. level of something. Count frequency of responses or FOUR LEVELS OF MEASUREMENT effects. Example: Popularity contest votes. Nominal Scales Represent kinds or types of objects. Concerned with names and Often synonymous with categorical categories. variables. Examples: Nationality, hair color. May use numeric codes for measurement. Ordinal Scales: Used for ranked data. Allows comparison of degree. Examples: First, second, third; good, better, best. Ordinal Scales: MAJOR APPROACHES Used for ranked data. Allows comparison of degree. QUALITATIVE RESEARCH Examples: First, second, third; good, better, best. Qualitative research aims to provide a detailed understanding Interval Scales: of characteristics, kinds, and quality related to a subject or event. Equal units of measurement. Researchers use methods like No true zero point. in-depth interviews and narrative Examples: Temperature, attitude, IQ. descriptions. Data Type: Qualitative variables Think of interval data like measuring (non-numerical data) are temperature. We use Celsius or Fahrenheit. obtained. The intervals between values are equal Examples: Studies on lived experiences of (e.g., the difference between 10°C and 20°C male convicts, emotions of people who is the same as between 20°C and 30°C). suffered loss, or a politician’s perspective But there’s no true zero point (0°C doesn’t on morality. mean “no temperature”). Example: If it’s 20°C today and 10°C Advantages: tomorrow, it’s not twice as hot—it’s just a Provides rich descriptions of real difference. experiences. Allows for in-depth exploration and Ratio Scales: elaboration by participants. Highest level. Helps understand abstract factors Uses zero as base point. (customs, traditions, family roles, Allows comparison of differences etc.). and relative magnitudes. Examples: Height, weight, age. Disadvantages: Lack of statistical analysis due to Ratio data have equal intervals and a true non-numeric data. zero point. True zero means “nothing” (e.g., Limited sample size affects data weight of 0 kg means no weight). credibility. Ratios make sense (e.g., 20 kg is twice as Subjective bias from the heavy as 10 kg). researcher’s perspective. Examples: Weight, height, age—all QUANTITmeasured in real units. QUANTITATIVE MIXED METHODS Quantitative research Researchers combine both systematically describes large qualitative and quantitative collections of things. It involves approaches. hypothesis testing and mechanistic Useful for comprehensive analysis understanding. and addressing diverse research The quantitative approach involves questions. making predictions and describing events using numerical figures. It Advantages: relies on statistical analysis to Rich Explanation: Qualitative data interpret data. provides a deeper understanding. Data Type: Quantitative variables Validity and Reliability: Combining (numeric data) are collected. both methods enhances the study’s Examples: Surveys, experiments, statistical validity and reliability. analyses. Disadvantages: Advantages: Time-Consuming: Integrating both Generates reproducible knowledge. approaches takes longer. Allows statistical analysis. Guideline Challenges: Few Confirmatory Method: It follows a guidelines for applying both scientific method by testing methods can lead to discrepancies hypotheses. in findings. Bias Reduction: By examining numerical data, bias is minimized. Generalizability: Findings can be applied to larger populations due to operational definitions of variables. Disadvantages: Requires larger samples. Statistical training needed for analysis. Limited Focus: Only focuses on the object under investigation. Narrow Explanations: Interpretations are based solely on statistical data.  Main Characteristics of Quantitative Broad Study Scope: Approach Quantitative research involves a greater number of subjects, Data Collection: enhancing result generalization. Quantitative research gathers data using structured research Objectivity and Accuracy: instruments (e.g., questionnaires or Study results are more objective surveys). and accurate. Sample Sizes: Replicability: Results are based on larger sample When the right procedures are sizes that represent the population. followed, quantitative research can be replicated and compared with Replicability: similar works. The study can be replicated or repeated due to its high reliability. Summarizing Information: Researchers can summarize Clearly Defined Research Question: extensive data sources and make Researchers seek objective cross-category comparisons. answers to well-defined research questions. Avoiding Bias: By maintaining distance from Careful Design: subjects and using neutral All study aspects are carefully facilitators, personal bias can be planned before data collection. minimized. Numerical Data: Limitations of Quantitative Approach Data are expressed in numbers and statistics. Contextual Limitation: While quantitative data can test Generalization and Prediction: hypotheses, they may be limited in The approach allows generalizing explaining context. The focus on concepts widely, predicting future numerical results sometimes outcomes, and investigating causal overlooks the broader context. relationships. Artificial Setting: CAUSAL-COMPARATIVE RESEARCH Research is often conducted in Purpose: controlled environments, which Establish cause-effect may not fully reflect real-life relationships. situations. Additionally, research Independent Variable: tools may introduce bias from the Often a demographic factor researcher’s perspective. (e.g., gender, race). Example: Low joblessness reduces a TYPES OF QUALITATIVE RESEARCH country’s poverty rate. DESCRIPTIVE RESEARCH EXPERIMENTAL RESEARCH Purpose: Purpose: Collect data to test Measure the effect of an hypotheses or describe independent variable study variables. (cause) on a dependent Data Collection: variable (effect). Typically numeric data Control: gathered through surveys, Researchers can interviews, or observations. manipulate independent Application: variables, and participants Common in science, are randomly assigned. technology, engineering, and social sciences. QUASI-EXPERIMENTAL RESEARCH Purpose: CORRELATIONAL RESEARCH Determine causes and Purpose: effects when full Determine the level of experimental control is not relation between possible. quantifiable variables. Application: Correlation: Used for naturally occurring Does not imply causation phenomena and their but helps predict variable impact on people. values.