PRACTICAL RESEARCH 2_Q1_Mod1 V2.pdf

What Is It Quantitative Research You have learned from Practical Research 1 that research method is classified into two main types: quantitative and qualitative. While both methods utilize a specific data gathering procedure, the former is generally concerned with understandi...

What Is It Quantitative Research You have learned from Practical Research 1 that research method is classified into two main types: quantitative and qualitative. While both methods utilize a specific data gathering procedure, the former is generally concerned with understanding phenomenon relating to or involving quality or kind. The latter, on the other hand, is based on the measurement or quantity. In this module, we will focus on quantitative methods of research and its different kinds. Quantitative research uses scientifically collected and statistically analyzed data to investigate observable phenomena. A phenomenon is any existing or observable fact or situation that we want to unearth further or understand. It is scientific for the fact that it uses a scientific method in designing and collecting numerical data. Once data is collected, it will undergo statistical analysis like Pearson’s r, t-test and Analysis of Variance (ANOVA) for analysis. Since data is analyzed statistically, it is imperative that the data obtained must be numerical and quantifiable, hence its name quantitative research. Numerical data are generally easier to collect than descriptions or phrases used in qualitative research. Information like student’s grades in different subjects, number of hours of engagement in social media platforms of teens, percentage of consumers who prefer the color blue for soap packaging, and average of daily Covid-19 patient recovery per region are just few examples of research data expressed in numbers. Some data, on the other hand, are not directly countable and thus require conversion from non-numerical information into numerical information. For instance, determining which brand of canned sardines is the best choice for consumers in terms of taste cannot be expressed in numbers unless we do a survey using a rating scale. Several forms of rating scales are available, e.g., the Likert scale that we can use to quantify data. Usually, they come in a selection of numbers with a corresponding meaning for each choice, for example: 1= tastes very good, 2 = satisfactory, or 3 = undesirable. Numerical choices convert texts into numbers so the researcher can perform mathematical operations for faster, more accurate, and more objective analysis. Characteristics of Quantitative Research Quantitative research is commonly used in natural sciences research problems because of the following characteristics: 1. Large Sample Size. To obtain more meaningful statistical result, the data must come from a large sample size. 2. Objectivity. Data gathering and analysis of results are done accurately, objectively, and are unaffected by the researcher’s intuition and personal guesses. 3. Concise Visual Presentation. Data is numerical which makes presentation through graphs, charts, and tables possible and with better conveyance and interpretation. 4. Faster Data Analysis. The use of a statistical tools gives way for a less timeconsuming data analysis.  5. Generalized Data. Data taken from a sample can be applied to the population if sampling is done accordingly, i.e., sufficient size and random samples were taken. 6. Fast and Easy Data Collection. Depending on the type of data needed, collection can be quick and easy. Quantitative research uses standardized research instruments that allow the researcher to collect data from a large sample size efficiently. For instance, a single survey form can be administered simultaneously to collect various measurable characteristics like age, gender, socio-economic status, etc. 7. Reliable Data. Data is taken and analyzed objectively from a sample as a representative of the population, making it more credible and reliable for policymaking and decision making. 8. High Replicability. The Quantitative method can be repeated to verify findings enhancing its validity, free from false or immature conclusions. Advantages of Quantitative Research The following are the advantages of quantitative research or its strengths: 1. Very objective 2. Numerical and quantifiable data can be used to predict outcomes. 3. Findings are generalizable to the population. 4. There is conclusive establishment of cause and effect 5. Fast and easy data analysis using statistical software. 6. Fast and easy data gathering 7. Quantitative research can be replicated or repeated. 8. Validity and reliability can be established Disadvantages of Quantitative Research The following are the disadvantages of quantitative research or its weaknesses: 1. It lacks the necessary data to explore a problem or concept in depth. 2. It does not provide comprehensive explanation of human experiences. 3. Some information cannot be described by numerical data such as feelings, and beliefs. 4. The research design is rigid and not very flexible. 5. The participants are limited to choose only from the given responses. 6. The respondents may tend to provide inaccurate responses. 7. A large sample size makes data collection more costly. Kinds of Quantitative Research Quantitative research is a broad spectrum that it can be classified into smaller and more specific kinds: descriptive, correlational, ex post facto, quasi-experimental, and experimental. Descriptive design is used to describe a particular phenomenon by observing it as it occurs in nature. There is no experimental manipulation, and the researcher does not start with a hypothesis. The goal of descriptive research is only to describe the person or object of the study. An example of descriptive research design is “the determination of the different kinds of physical activities and how often high school students do it during the quarantine period.” The correlational design identifies the relationship between variables. Data is collected by observation since it does not consider the cause and effect, for example, the relationship between the amount of physical activity done and student academic achievement. Ex post facto design is used to investigate a possible relationship between previous events and present conditions. The term “Ex post facto” which means after the fact, looks at the possible causes of an already occurring phenomenon. Just like the first two, there is no experimental manipulation in this design. An example of this is “How does the parent’s academic achievement affect the children obesity?” A quasi-experimental design is used to establish the cause-and-effect relationship of variables. Although it resembles the experimental design, the quasi-experimental has lesser validity due to the absence of random selection and assignment of subjects. Here, the independent variable is identified but not manipulated. The researcher does not modify pre-existing groups of subjects. The group exposed to treatment (experimental) is compared to the group unexposed to treatment (control): example, the effects of unemployment on attitude towards following safety protocol in ECQ declared areas. Experimental design like quasi- experimental is used to establish the cause-and-effect relationship of two or more variables. This design provides a more conclusive result because it uses random assignment of subjects and experimental manipulations. For example, a comparison of the effects of various blended learning to the reading comprehension of elementary pupils. What Is It Importance of Quantitative Research Across Fields The value of quantitative research to man’s quest to discover the unknown and improve underlying conditions is undeniable. Throughout history, quantitative research has paved the way to finding meaningful solutions to difficulties. For instance, the development of vaccines to strengthen our immunity against viruses causing highly communicable diseases like polio, influenza, chickenpox, and measles to name a few, underwent thorough experimental trials. You bet, scientists and medical experts all over the world today are working their best to fast track the development, testing and release of the vaccine for the Corona Virus Disease of 2019 (Covid-19) as the pandemic has critically affected the world economy, education, as well as physical and emotional well-being of people. The findings of the quantitative study can influence leaders’ and law-makers’ decisions for crafting and implementing laws for the safety and welfare of the more significant majority. For example, a community with high cases of Covid-19 positive patients is mandated by law to be under Enhanced Community Quarantine where only the most essential businesses can operate. On the other hand, cities with less or zero case will be under General Community Quarantine where some businesses, public and private offices are already allowed to operate. Using quantitative design helps us determine and better understand relationships between variables or phenomenon crucial to reducing the range of uncertainty because the mathematics (more of this in the last module) behind quantitative studies helps us make close estimates of the outcome (dependent variable) from a given condition/s (independent variable). Relationship between demand and supply, age and health, discipline and academic achievement, practice and winning at sports, depression and suicidal rates, algae population and Oxygen demand are just few examples of real-life applications of correlation studies in the past that we still apply today. Most inventions and innovations are products of quantitative studies. Before you can enjoy the uses and features of a smart phone, it took years of research to establish compliance to standards for interoperability, to find the most cost-effective raw materials, and to identify the sleekest and sturdiest design, the fastest data saving and processing power, and most marketable add-ons according to consumer needs. Indeed, mankind will dwell in the darkness of ignorance if not for the people who conducted their research before reading about it from books or manuals. The table below shows some of the contributions of quantitative research to other fields and their example. Field Contribution/Application Example Social Science Show effects of intervention to The effects of pandemic group behavior on social behavior and Understand cultural or racial economic stability conflicts Human satisfaction and stressors Natural and Physical Investigate the effectiveness of a Antidiabetic properties of Sciences product or treatment to common Philippine herbs illnesses Finding or enhancing alternative energy sources Advancement in material science Agriculture and Increase the yield of crops The effectiveness of Fisheries Prevent and cure crops and organic and inorganic livestock diseases fertilizer to vegetable production Sports Enhance athletic performance Diet and exercise techniques for different kinds of sports Business Offer device marketing Effectiveness of Facebook strategies ads on sales. Improve marketability Arts and Design Show relationship between The effects of music on color and architectural space learning and behavior. Maximize use of Multimedia and adaptation for recreation, business marketing and lifestyle changes. Environmental Science Determine Cause and effects of The environmental factors climate change affecting natural calamities  What Is It To get an answer to an inquiry that they are investigating, researchers will observe and measure the quality or quantity of the object of the study. It is therefore imperative for the researcher to identify the variables significant in explaining observed effects or behavior. A Variable is anything that has a quantity or quality that varies. For instance, during the quarantine period, your mother planted tomato seedlings in pots. Now common understanding from science tells you that several factors are affecting the growth of tomatoes: sunlight, water, kind of soil, and nutrients in soil. How fast the tomato seedlings will grow and bear fruits will depend on these factors. The growth of tomatoes and the number of fruits produced are examples of the Dependent Variables. The amount of sunlight, water, and nutrients in the soil are the Independent Variables. If there is an existing relationship between the independent and dependent variables, then the value of the dependent variable varies in response to the manipulation done on the independent variable. The independent variable is also identified as the presumed cause while the dependent variable is the presumed effect. In an experimental quantitative design, the independent variable is pre-defined and manipulated by the researcher while the dependent variable is observed and measured. For descriptive, correlational, and ex post facto quantitative research designs, independent and dependent variables simply do not apply. It is important to note other factors that may influence the outcome (dependent variable) not manipulated or pre-defined by the researcher. These factors are called Extraneous Variables. In our example above, the presence of pests and environmental stressors (e.g. pets, extreme weather) are the extraneous variables. Since extraneous variables may affect the result of the experiment, it is crucial for the researcher to identify them prior to conducting the experiment and control them in such a way that they do not threaten the internal validity (i.e. accurate conclusion) of the result. Controlling the extraneous variable can be done by holding it constant or distribute its effect across the treatment. When the researcher fails to control the extraneous variable that it caused considerable effect to the outcome, the extraneous variable becomes a Confounding Variable. For example, if the tomato had been infested by pests (confounding variable) then you cannot conclude that manipulations in sunlight, water, and soil nutrients (independent variable) are the only contributing factors for the stunted growth and poor yield (dependent variable) of the plant or is it the result of both the independent variables and the confounding variable. The variables can also be classified according to their nature. The diagram below shows the different classifications:  VARIABLE QUANTITATIVE QUALITATIVE (NUMERICAL) (CATEGORICAL) DISCREET CONTINUOUS NOMIN AL ORDINAL DICHOTOMOUS I. Quantitative Variables, also called numerical variables, are the type of variables used in quantitative research because they are numeric and can be measured. Under this category are discrete and continuous variables. A. Discrete variables are countable whole numbers. It does not take negative values or values between fixed points. For example: number of students in a class, group size and frequency. B. Continuous variables take fractional (non-whole number) values that can either be a positive or a negative. Example: height, temperature. Numerical data have two levels of measurement, namely: A. Intervals are quantitative variables where the interval or differences between consecutive values are equal and meaningful, but the numbers are arbitrary. For example, the difference between 36 degrees and 37 degrees is the same as between 100 degrees and 101 degrees. The zero point does not suggest the absence of a property being measured. Temperature at 0 degree Celsius is assigned as the melting point of ice. Other examples of interval data would be year and IQ score. B. Ratio type of data is similar to interval. The only difference is the presence of a true zero value. The zero point in this scale indicates the absence of the quantity being measured. Examples are age, height, weight, and distance. II. Qualitative Variables also referred to as Categorical Variables are not expressed in numbers but are descriptions or categories. It can be further divided into dichotomous, nominal or ordinal. A. Dichotomous variable consists of only two distinct categories or values, for example, a response to a question either be a yes or no. B. Nominal variable simply defines groups of subjects. In here, you may have more than 2 categories of equivalent magnitude. For example, a basketball player’s number is used to distinguish him from other players. It certainly does not follow that player 10 is better than player 8. Other examples are blood type, hair color and mode of transportation. C. Ordinal variable, from the name itself, denotes that a variable is ranked in a certain order. This variable can have a qualitative or quantitative attribute. For example, a survey questionnaire may have a numerical rating as choices like 1, 2, 3, 4, 5ranked accordingly (5=highest, 1=lowest) or categorical rating like strongly agree, agree, neutral, disagree and strongly disagree. Other examples or ordinal variable: cancer stage (Stage I, Stage II, Stage III), Spotify Top 20 hits, academic honors (with highest, with high, with honors).