Practical Research 2 PDF

PRACTICAL RESEARCH 2 NATURE OF QUANLTITATIVE RESEARCH Characteristics of Quantitative Research Disadvantages of Quantitative Research 1. It is reliable and objective...

PRACTICAL RESEARCH 2 NATURE OF QUANLTITATIVE RESEARCH Characteristics of Quantitative Research Disadvantages of Quantitative Research 1. It is reliable and objective. 1. The context of the study or the experiment is ignored in such a way that is 2. It uses statistics to generalize a finding. does not consider the natural setting where the study is conducted. 3. It reduces and restructures a complex problem to a limited number of variables. 2. Having a large study sample requires researchers to spend more 4. It looks at the connections between variables and establishes cause and effect resources. relationships in highly controlled circumstances. 3. The results are limited since they are usually based on the analysis of 5. It tests theories or hypotheses. numbers and are not obtained from detailed narratives. 6. It assumes that the sample is representative of the population. 4. It provides less elaborate accounts of human perceptions. 7. The subjectivity of its methodology is a secondary concern. 5. In experimental research, the level of control might not be normally 8. It deals with the details of the subject. applicable in the real world because it is usually done in laboratory. 6. Preset or fixed alternative answers may not necessarily reflect the true Advantages of Quantitative Research answers of the participants. 7. Findings can be influenced by the researcher’s perspective since most of 1. It allows the researcher to measure and analyze the data to arrive at an objective the time; the participants are unknown to him or her. answer to the problem. 2. The result is generally reliable since the study uses a big sample of the The Importance of Quantitative Research in Different Fields population. 3. Established standards are used in choosing the instruments, in sampling 1. Education. It can be used in measuring the level of performance of procedures, and in choosing the most appropriate statistical treatment, thus students and teachers, and in assessing the effectiveness of the methods making the research replicable. and the difference programs conducted. 4. Personal biases can be avoided since personal interaction is not part of the 2. Business. It can improve the overall marketing strategy, and help the research process. company make informed decisions on how to best move forward with a 5. Processes involved are simplified since the steps in doing quantitative research particular product or services. are made easy and systematic. 3. Medical and Health Care. It yields statistics that can help improve the rate 6. Results can be reduced through statistical treatments and can be interpreted in a of recovery of patients with illnesses and sicknesses, and the efficacy of few statements. medicines and vaccines, among others. 4. Sciences. It can lead to a more responsible and accountable operation of the different components of technology. CLASSIFICATIONS OF VARIABLES during the conduct of an experiment and could influence the result of the study. They are known as covariate variable. 1. Numeric Variables. These are the variables with values that describe a 4. Non-experimental Variables measurable numerical quantity and answers the questions “how many” or “ how a. Predictor variables. These variables change the other variables/s in a much”. These values are considered as quantitative data. non-experimental study. a. Continuous variables. These variables can assume any value between b. Outcome/ Criterion Variables. These variables are usually influenced by a certain set of real numbers. The values depend on the scale used. the predictor variables. Continuous variables are also called interval variables. Examples: time, 5. Variables according to the number being studied. age, temperature, height, and weight. a. Univariate study. Only one variable is being studied. b. Discrete variables. These variables can only assume any whole value b. Bivariate study. Two variables are being studied. within the limits of given variables. Examples: the number of registered c. Polyvariate study. More than two variables are being studied. cars, number of business locations, number of children in the family, population of students, and total number of faculty members. 2. Categorical Variables. These variables with values that describe a quality or characteristic of a data unit like “what type” or “which category” THE RESEARCH TITLE a. Ordinal variables. These variables can take a value which can be Generally, the title should: logically ordered or ranked. Examples: Academic grades such as A, B, C; clothing size such as XS, S, M, L, XL; and measures of attitudes like 1. Summarize the main idea of the paper; strongly agree, agree, disagree, or strongly disagree. 2. Be concise statement of the main topic; b. Nominal Variables. These are variables whose values cannot be 3. Include the major variable/s; organized in a logical sequence. Examples: business types, eye colors, 4. Show the relationship of the main variables of the study; kinds of religion, various languages, and types of learners. 5. Include the main task of the research about the major variables under c. Dichotomous variables. These variables represent only two categories. study; and Examples: gender (male and female), answer (yes or no) and veracity 6. Mention the participants (true or false) d. Polychotomous variables. These are variables that have many Elements: Main Task / Major variables / Topic / Participants / Setting / Outcomes categories. Examples: educational attainment (elementary, high school, Reminders: college, graduate, and post-graduate), level of performance (excellent, very good, good, satisfactory, or poor) 1. The words: METHOD, RESULT, INVESTIGATIONS should not appear 3. Experimental variables 2. The general problem can be the thesis title, when changed into a a. Independent variables. These variables are usually manipulated in an statement. experiment. Thus, it is also called manipulated or explanatory variable. 3. The title must have 10-15 words b. Dependent variables. These variables are usually affected by the manipulation of the independent variables. They are also called response or predicted variable. c. Extraneous variables. These variables are already are also called mediating or intervening variables. These variables are already existing STATEMENT OF THE PROBLEM The opening paragraph of this part of the research paper contains the general problem of the study. It has to be restated with specific details on the participants, setting, and period of the study. Important Elements in the Statement of the General problem are: 1. Main Tasks. These satisfy the questions “what to do?” with the major variables, such as to associate, relate, assess, measure, and determine, among others. 2. Major Variables 3. Participants. The subject or respondents of the study. 4. Specific Setting 5. Coverage Date. The period when to conduct the study. 6. For developmental research, the intended outputs such as an intervention program, module, or policies, among others. Welcome to... PRACTICAL RESEARCH 2 Prepared by: Ms. Cates Tadlas What is Quantitative Research? Clear and concise definition of quantitative research Emphasize the focus on numerical data and statistical analysis A systematic investigation of phenomena by gathering quantifiable data and performing statistical analysis to describe, predict, or explain phenomena. CHARACTERISTICS OF QUANTITATIVE RESEARCH Objective and unbiased Large sample sizes Numerical data Statistical analysis Generalizability of findings STRENGTHS OF QUANTITATIVE RESEARCH Objectivity: Reduces researcher bias through numerical data. Generalizability: Findings can often be applied to larger populations. Reliability: Results can be replicated in similar studies. Efficiency: Data analysis can be automated, saving time. Precision: Numerical data allows for precise measurement and comparison. WEAKNESSES OF QUANTITATIVE RESEARCH Lack of Depth: Focus on numbers may overlook complex issues and nuances. Overreliance on Numbers: Quantitative data alone might not fully explain phenomena. Potential for Bias: Data collection and analysis methods can introduce bias. Limited Flexibility: Research design is often less adaptable. Difficulty Establishing Causality: Correlation does not always imply causation. IMPORTANCE OF QUANTITATIVE RESEARCH ACROSS FIELDS Business and Healthcare Economics and Medicine Education Natural Sciences BUSINESS AND ECONOMICS Example: "Market research using surveys and data analysis helps businesses understand customer preferences and tailor products accordingly." EDUCATION Example: "Quantitative research on student achievement can inform policy decisions to improve educational outcomes." HEALTHCARE AND MEDICINE Example: "Clinical trials use quantitative methods to evaluate the safety and effectiveness of new treatments." NATURAL SCIENCES Example: "Quantitative research is essential for understanding climate change patterns and developing sustainable solutions."  TYPES OF... EXPERIMENTAL NON-EXPERIMENTAL True experiments: Random Descriptive research: Describes characteristics of a assignment, control group, population. Correlational research: Examines relationships manipulation of independent variable. between variables. Quasi-experiments: Lack random Comparative research: Compares groups on assignment but still manipulate specific variables. independent variables. Survey research: Collects data through Pre-experiments: No control group or questionnaires or interviews. random assignment. Observational research: Observes behavior in natural settings.  KINDS OF QUANTITATIVE RESEARCH EXPERIMENTAL NON-EXPERIMENTAL Experimental Research Descriptive Research Definition: Investigates cause-and-effect Definition: Describes the characteristics of a population relationships or phenomenon Examples: controlled experiments, randomized Examples: surveys, observational studies, correlational controlled trials studies Key points: Key points: Manipulates independent variable to observe Collects data to describe, summarize, and interpret effects on dependent variable Does not test hypotheses Uses control groups for comparison Examples of research questions (e.g., What is the Examples of research questions (e.g., Does a new average age of students in a school?) drug effectively treat a disease?)  KINDS OF QUANTITATIVE RESEARCH EXPERIMENTAL NON-EXPERIMENTAL Quasi-Experimental Research Correlational Research Definition: Similar to experimental research but lacks Definition: Examines relationships between variables random assignment Examples: pre-test/post-test designs, time series Examples: correlation coefficient, scatterplots designs Key points: Key points: Determines the strength and direction of Used when random assignment is not feasible relationships Lower level of control compared to true Does not imply causation experiments Examples of research questions (e.g., Does a new Examples of research questions (e.g., Is there a teaching method improve student relationship between hours of study and exam performance?) scores?) KINDS OF QUANTITATIVE RESEARCH NON-EXPERIMENTAL Survey Research Definition: Collects data from a sample of individuals using questionnaires or interviews Examples: cross-sectional surveys, longitudinal surveys Key points: Used to measure attitudes, opinions, behaviors Can be conducted through various methods (online, mail, telephone) Examples of research questions (e.g., What is the public opinion on climate change?) KINDS OF QUANTITATIVE RESEARCH NON-EXPERIMENTAL Observational Research Definition: Involves observing and recording behavior in natural settings Examples: participant observation, non-participant observation Key points: Used to study behavior in real-world contexts Requires careful observation and data recording Examples of research questions (e.g., How do children interact with technology in a classroom?) CLASSIFICATION OF VARIABLES DATA ANALYSIS Descriptive statistics (mean, median, mode, standard deviation) Inferential statistics (hypothesis testing, correlation, regression) THANK YOU AND GOOD LUCK