LESSON 01 NOTES.pdf

LESSON 01: QUANTITATIVE RESEARCH Introduction In almost all subject matters, our knowledge is incomplete, and problems are waiting to be solved. We can address these unknowns and find answers to unresolved problems by asking relevant questions and then seeking answ...

LESSON 01: QUANTITATIVE RESEARCH Introduction In almost all subject matters, our knowledge is incomplete, and problems are waiting to be solved. We can address these unknowns and find answers to unresolved problems by asking relevant questions and then seeking answers through systematic research. Quantitative Research may be defined as the systematic empirical investigation of social phenomena using tools of mathematics and statistics (Toreno and Clamor-Toreno, 2018). What is Quantitative Research? It is a structured way of collecting and analyzing data obtained from different sources involving the use of computational, statistical, and mathematical tools to get results (SIS International Research, 2018). Quantitative researchers seek explanations and predictions (Leedy & Ormod, 2015) that can be generalized and replicated to other persons and places (Creswell, 2009). The intent is to establish, confirm, validate relationships, and to develop generalizations that contribute to theory (Leedy & Ormod, 2015). In short, quantitative research is explaining phenomena by collecting numerical data that are analyzed using mathematically based methods in particular statistics’ (Aliaga & Gunderson, 2002). Characteristics of Quantitative Research 1. Structured research instruments are used to gather data. Accurate and systematic data collection is critical to conducting scientific research. To do this, we need reliable research instruments. In quantitative research, these instruments pertain to questionnaires from other research papers, researcher-made questionnaires, standardized tests, psychological tests, laboratory tests, imaging, and other methods that can be numerated. 2. Results are based on larger sample sizes that are representative of the population. Did you ever wonder how the Philippines’ Social Weather Stations (SWS) conduct surveys about how Filipinos feel regarding certain issues? In doing such surveys, the researchers do not ask all 106,097,027 Filipinos about how they feel; instead, they survey around 1,200 people. How can this be a survey of the entire Philippines, you ask? The answer is that the sample size is the representative of the population. Representativeness is crucial in quantitative research. 3. Research study can usually be replicated or repeated given its high reliability. The reliability of the instrument used to collect data can be thought of as consistency. Does the instrument consistently measure what it is intended to measure? For example, every December since year 2000, SWS surveys Filipinos by asking, “Is it with hope or fear that you enter the coming year?” Their results that for 2018, a new all-time high of 96% of Filipino adults are entering the year with hope rather than fear surpassing the previous record of 95% achieved in 2002, 2011, and 2016 (SWS 2017). The consistency of the findings over time proves the reliability of the instrument used by SWS. 4. Quantitative researcher has a clearly defined research question to which objective answers are sought. A quantitative researcher knows what he/she is specifically investigating. Quantitative research stands out for its objectivity. Numbers are exact to the decimal point. They don’t lie. A quantitative researcher analyzes numbers and concludes based on numerals, no matter his or her beliefs, values, and personality. 5. Quantitative research tests hypothesis and measures variables. A hypothesis is a specific statement of prediction. It is a formal tentative statement of the expected relationship between two or more variables a researcher is studying. Variables are those simplified portions of the phenomenon that you intend to study. It is derived from the root word “vary”meaning changing in amount, volume, number, form, nature, or type. These variables should be measurable i.e., they can be counted or subjected to a scale. For example, if you want to study the performance of Senior High School students in college entrance exams, then your variables may include, entrance exam scores, number of hours devoted to studying, age, general average, etc. Variables may be weight, height, anxiety levels, income, body temperature, and so on. It could be demographic, religion, income, occupation, temperature, humidity, language, food, fashion, gender, birth order, types of blood group, etc. 6. Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms. Quantitative data are presented in the form of numbers - from precise measurements and statistical tests. These data are presented in the research paper in the form of charts, graphs, or tables, the highlights of which are explained in textual form. 7. Quantitative research projects can be used to generalize concepts more widely, predict future results, or investigate casual relationships. One factor that can affect the usefulness of a quantitative study is its generalizability. It is the measure of how useful the results of a study are for a broader group of people or situations. The study is said to have a good generalizability, meaning, what is true to some may be true to all. For example, Racca and Lasaten (2016) found out that there is a significant relationship between the Philippine Science High School students’ English language proficiency and their academic performance in Science and Mathematics. Could it be the same with the students from other high schools across the country? Quantitative research is a numbers game: the larger the sample population, the more one can generalize the results and predict future results. Strengths of Quantitative Research 1. Quantitative methods offer breadth. With enough samples and data, it is easier to draw generalizable conclusions using quantitative research methods compared to single case or small population studies using qualitative research methods. 2. Studies using quantitative research approaches are generally easier to replicate. Furthermore, the results also tend to be somewhat more consistent when the same methods are followed. 3. Communicating data, procedures, and results are easier because the statistical terminologies, analytical techniques, and procedures are generally consistent across disciplines. 4. It is generally easier to summarize, describe, process, and analyze large volumes of information when they are in numerical form. Analysis can also be faster, especially when spreadsheets or statistical software are used. 5. Quantitative research is better for projects where objectivity is desired. Quantitative methods are designed to minimize biases, influence, and subjective interpretations of the researchers. Weaknesses of Quantitative Research 1. Some aspect of people, human behavior, and interactions are often difficult or impossible to measure. 2. Emphasis on generalizability and trends hinders the deeper examination of nuanced factors that affect specific cases, especially those that deviate from the general trend. 3. Context and other information that provide a richer understanding of observed trends and patterns can get lost in the measurement and macro examination of data. 4. Errors in the measurement or modelling of the omission of data can easily lead to the misinterpretation of results. 5. Some tools in quantitative research (e.g., self-accomplished survey questionnaires) may yield limited or even inaccurate information due to human nature. For instance, respondents may fill out the questionnaire with what they believe is socially desirable. Others may simply make errors in filling out the questionnaire. Importance of Quantitative Research People research to find solutions, even tentative ones, to problems, to improve or enhance ways of doing things, to disprove or provide a new hypothesis, or simply to find answers to questions or solutions to problems in daily life. It is important to identify and classify the kinds of quantitative research and its variables in the inquiry process. Research findings can affect people’s lives, ways of doing things, laws, rules, and regulations, as well as policies, among others. Widely, quantitative research is often used because of its emphasis on proof rather than discovery. Importance of Quantitative Research across Fields Quantitative research is an objective, methodical, well- determined scientific process of investigation. It aims to seek answers to problems through the application of scientific methodology. It is widely used in all fields to determine the unknown. Business and Management. From the time industry began, advances in business were made through extensive quantitative research done by the icons of the industry – Mary Parker Follet, Hugo Munsterberg, and Elton Mayo of the Hawthorne Studies fame, to name a few. All contributed to how businesses are run today. We now live in the era of big data, and quantitative methods used by operations analysts provide solid evidence to guide management decisions on production, distribution, accounting, marketing, and personnel management. These methods also help managers project future business conditions, enabling them to adjust their strategies as needed. Marketing, Tourism and Service Industries. Market research plays a key role in determining the factors that lead to business success. Quantitative research helps service industries by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation, and identifying drivers for brand recall and product purchase decisions. Thus, the findings help in forecasting, branding decisions, and even deciding what colors to use. Social Sciences. In social sciences, quantitative research is widely used in the fields of psychology, economics, sociology, community health, education, human development, gender, communication, and political science. Economic analysts rely on complex mathematical and statistical procedures to analyze economic phenomena, explain economic issues, as well as predict future economic conditions. Quantitative psychologists’ study and develop models, methods, and techniques used to measure human behavior and other attributes. Sociologists employ quantitative research methods to focus on ways people themselves understand and describe their social worlds. Political scientists use quantitative research to predict and explain political phenomena and political behavior. Medical Sciences and Allied Fields. Quantitative research is essential in Medical Sciences and its allied fields, such as Dentistry, Nursing, Physical and Occupational Therapy, Medical Technology, Pharmacy, etc. It plays an important role in finding out which treatments are used in the best possible ways. Research and clinical trials are an everyday part of healthcare professionals. Engineering and Technology. Research in engineering and technology has overwhelmingly been focused on quantitative designs. From inventions, product designs, modeling, simulation, and even the structural strength of beams and columns- these are all had from quantitative research findings to meet the extremely rapid product development now being demanded by the industry. Kinds of Quantitative Research Designs The research design refers to the overall strategy that you choose to integrate the different components of the study coherently and logically, thereby ensuring you will effectively address the research problem. Furthermore, a research design constitutes the blueprint for the selection, measurement, and analysis of data. The research problem determines the research you should. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. The kind of research is dependent on the researcher’s aim in conducting the study and the extent to which the findings will be used. Quantitative research designs are generally classified into experimental and non-experimental as the following matrix below. Types of Quantitative Research Description Sample Studies EXPERIMENTAL True Experimental ✓Rigid manipulation of variables The effect of a new treatment plan for breast cancer ✓ Use of control, selection, and random assignment Quasi-experimental ✓ Rigid manipulation of variables The use of conventional versus cooperative learning groups on ✓ Use of control without students’ academic randomization of variables achievement ✓ Intact groups or participants are The effect of personalized used in their entirety instruction versus conventional instruction on the computational skill NON-EXPERIMENTAL – According to Research Purpose Descriptive ✓ Describes the status of a A description of the tobacco use variable habits of teenagers ✓ Designed to provide systematic A description of the kinds of information of phenomena physical activities occurring in nursing homes i.e. Surveys Predictive ✓ Predict or forecast phenomena Factors affecting college success without establishing cause and The relationship between the effect i.e. Correlational types of activities used in Math classrooms and students’ academic achievement Explanatory ✓ Develop or test a theory to The role of peers and study explain how and why it operates groups in students’ attitude in learning and academic ✓ Identify causality behind achievement in Mathematics phenomena NON-EXPERIMENTAL – According to Time Dimension Cross-sectional ✓ Data collected at a single point Graduating students’ beliefs, in time perceptions, and experiences on their K to 12 schooling ✓ Comparisons are made across variables of interest Retrospective ✓ Comparisons are made The possible causes of lung between estimated data from the cancer and related respiratory past and data sets from the disorders of smokers present Use of Variables in Research When conducting research, it is important to identify and measure the variables being studied. Variables are properties or characteristics of some event, object, or person that can be assigned with different values or amounts. In research, a variable refers to “characteristics that have two or more mutually exclusive values or properties” (Sevilla and Other, 1988). Bernard (1994) defines a variable as something that can take more than one value, and values can be words or numbers. A variable specifically refers to characteristics or attributes of an individual or an organization that can be measured or observed, and that varies among the people or organization being studied (Creswell, 2002). Types of Variables A. Role taken by the Variable 1. INDEPENDENT VARIABLES – Those that probably cause, influence, or affect outcomes. They are invariably called treatment, manipulated, antecedent or predictor variables. This is the cause variable or the one responsible for the conditions that act on something else to bring about changes. Situation: A study is on the relationship of study habits and academic performance of UNO-R senior high school students. STUDY HABITS is the independent variable because it influenced the outcome or the performance of the students. Examples: kind of diet – with or without supplement, amount of fertilizer, exposure to sunlight, dose of a medicine 2. DEPENDENT VARIABLES – Those that depend on the independent variables; they are the outcomes or results of the influence of the independent variable. That is why it is also called outcome variable. Situation: A study is on the relationship of study habits and academic performance of UNO-R senior high school students. ACADEMIC PERFORMANCE is the dependent variable because it is depending on the study habits of the students; if the students change their study habit the academic performance also change. Examples: motor skills and memory tests of rats, growth of plants, response time to medication 3. INTERVENING OR MEDLING VARIABLES – Variables that “stand between” the independent and dependent variables, and they show the effects of the independent variable on the dependent variable. It is also called a “facilitating variable”, “moderator” or a “control variable.” Examples: ✓ In the study on “Knowledge of the Dangers of Smoking, attitudes towards Life, and Smoking Habits of Young Professionals”, the intervening variable is ‘attitude towards life’. A person’s attitude may increase or decrease the influence of ‘knowledge on the dangers of smoking (independent variable) on ‘smoking habits’ (dependent variable). Thus, knowing the dangers of smoking, one may shun smoking. One may argue, however, that the knowledge about the dangers of smoking may not necessarily prevent a person from smoking if he does not mind dying early as long as he/she enjoys life. ✓ Even if farm production is good, if the attitude towards payment is negative, loan repayment would be low, whereas, if the attitude towards repayment is positive or favorable, loan repayment would be high. 4. CONTROL VARIABLES – Special types of independent variables that are measured in the study because they potentially influence the dependent variable. Researchers use statistical procedures (e.g. analysis of covariance) to control these variables. They may be demographic or personal variables that need to be “controlled” so that the true influence of the independent variable on the dependent variable can be determined. Examples: income level, gender, educational level, location, ethnicity, race, family size. 5. ANTECEDENT VARIABLES – Variables that are found before (ante) the independent variable. These are expected to influence the independent variable/s. Example: in the study entitled “Extent of Exposure to Print Media and Reading ability of Senior High school students,” the main concern is the relationship between students ‘extent of exposure to print media’ (independent variable) and their ‘reading ability’ (dependent variable. The students’ exposure to print media, however, may depend on their sex, residence, and their parents’ education (antecedent variables). B. Value on a Scale and Corresponding Levels of Measurement 1. CONTINUOUS VARIABLES – A variable that can take an infinite number on the value that can occur within the population. Its values can be divided into fractions. Examples of this type of variable include age, height, and temperature. Continuous variables can be further categorized as: a. INTERVAL VARIABLES – It has values that lie along an evenly dispersed range of numbers. It is a measurement where the difference between the two values does have meaning. Examples: temperature, a person’s net worth (how much money you have when you subtract your debt from your assets), time as read on a 12-hour clock, IQ scores, age, etc. (In temperature, this may illustrate as the difference between a temperature of 60 degrees and 50 degrees is the same as the difference between 30 degrees and 20 degrees. The interval between values makes sense and can be interpreted.) b. RATIO VARIABLES – It has values that lie along an evenly dispersed range of numbers when there is absolute zero. It possesses the properties of interval variable, nominal, ordinal, and has a clear definition of zero, indicating that there is none of that variable. Examples: height, weight, and distance, work experience, etc. (Most scores stemming from response to survey items are ratio-level values because they typically cannot go below zero. Temperature measured in degrees Celsius and degrees Fahrenheit is not a ratio variable because 0 under these temperatures scales does not mean any temperature at all.) 2. DISCRETE VARIABLES – This is also known as a categorical or classificatory variable. This is any variable that has a limited number of distinct values, and which cannot be divided into fractions like sex, blood group, and the number of children in a family. Discrete variables may also be categorized into: a. NOMINAL VARIABLE – It represents categories that cannot be ordered in any particular way. It is a variable with no quantitative value. It has two or more categories but does not imply an ordering of cases. Examples: eye color, business type, religion, biological sex, political affiliation, basketball fan affiliation, etc. (A sub-type of nominal scale with only two categories just like sex is known as dichotomous.) b. ORDINAL VARIABLE – It represents categories that can be ordered from greatest to smallest. This variable has two or more categories that can be ranked. Examples: education level, income brackets, social class, typhoon signal etc. (An illustration of this is, if you asked people if they liked listening to music while studying and they could answer either “NOT VERY MUCH”, “MUCH”, “VERY MUCH” then you have an ordinal variable. While you can rank them, we cannot place a value to them. In this type, distances between attributes do not have any meaning. For example, you used educational attainment as a variable on the survey, you might code elementary school graduates = 1, high graduates = 2, college undergraduate = 3, and college graduate = 4. In this measure, a higher number means greater education. Even though we can rank these from lowest to highest, the spacing between the values may not be the same across the levels of the variables. The distance between 3 and 4 is not the same as the distance between 1 and 2. References David, F. (2002). Understanding and Doing Research: A Handbook for Beginners. Panorama Printing, Inc. Orleans, A., Laurel-Sotto, R. (2018). Science in Today’s World Research in Daily Life 2. Quezon City, Sibs Publishing House Inc. Pulmones, R. (2016). Quantitative Research. Phoenix Publishing House. San Miguel, J., Ph.D. (2018). STEP BY STEP Practical Research 2 for Senior High School. Cavite City, NCR: REAP.