Quantitative Research Notes PDF

**Practical Research 2 Quarter 1 - Module 1 Nature of Inquiry and Research** **At the end of this module, you should be able to: 1. Describe the characteristics, strengths, weaknesses, and kinds of quantitative research (CS\_RS12-Ia-c-1); 2. Illustrate the importance of quantitative research across...

**Practical Research 2 Quarter 1 - Module 1 Nature of Inquiry and Research** **At the end of this module, you should be able to: 1. Describe the characteristics, strengths, weaknesses, and kinds of quantitative research (CS\_RS12-Ia-c-1); 2. Illustrate the importance of quantitative research across field (CS\_RS12-Ia-c-2); 3. Differentiate the kinds of variables and their uses (CS\_RS12-Ia-c-3);** **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** 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** 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." - 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?" - 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. - 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.