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˗ˏˋ Content Curation ˎˊ˗
˗ˏˋ Content Curation ˎˊ˗
୨⎯ Introduction ⎯୧
୨⎯ Introduction ⎯୧
ꕀ Types of Quantitative Research (1)
ꕀ Types of Quantitative Research (1)
: ̗̀➛ 1. Descriptive Research
: ̗̀➛ 1. Descriptive Research
Purpose: To describe the characteristics of a population or phenomenon being studied.
Characteristics: It does not attempt to establish cause-and-effect relationships. Instead, it focuses on providing a detailed and accurate depiction of the subject.
Methods: Surveys, observational methods, and case studies.
Example: A survey to determine the average age, gender distribution, and educational background of attendees at a conference.
Descriptive research is a crucial method for comprehending the basic attributes of a certain sample or population. It focuses on describing observed events without exploring the underlying causes (Coe et al., 2017 as cited by Thomas & Zubkov, 2023). This type of studies utilises many statistical techniques, including surveys, tables of frequencies, and mean calculations, to offer a thorough overview of the gathered data.
Explanatory study aims to uncover the complex mechanisms and causal links that explain observed events (Riazi, 2016 as cited by Thomas & Zubkov, 2023). Explanatory study aims to uncover causal relationships between variables by utilising advanced statistical techniques such as ANOVA, T-tests, and regression analysis. An example of an explanatory research journey would be an investigation into possible influences on student academic performance, such as attendance, parental involvement, and study habits by a school principal who wished to understand the many factors at play. Next, the collection of data together with thorough regression analysis, seeks to determine the predictive power of each discovered variable in relation to academic success.
Descriptive research differs from explanatory designs due to the fact it does not contribute to decision-making with regard to the research focus, nor does it involve testing of hypotheses (Creswell, 2014 as cited by Thomas & Zubkov, 2023). The main purpose of descriptive research is to accurately depict the features of the sample or population being investigated, focusing particularly on answering questions about what happens within the specific context. Researchers proficiently employ statistical techniques such as mean calculations, tables of frequencies, and intervals of confidence to effectively explore descriptive inquiries. Surveys are frequently used in descriptive research approaches (de Vaus, 2013 as cited by Thomas & Zubkov, 2023). However, it is important to highlight that descriptive research is not limited to just one methodological approach. The combination of descriptive, and explanatory research designs in quantitative inquiry highlights the complexity of research, emphasising the importance of choosing the right research approach that aligns with the overall research objectives and questions (Thomas & Zubkov, 2023).
: ̗̀➛ 2. Survey Research
: ̗̀➛ 2. Survey Research
Purpose: To gather quantifiable data from a sample to make generalizations about a larger population.
Characteristics: It uses structured questionnaires or interviews with closed-ended questions to collect numerical data.
Methods: Cross-sectional surveys, longitudinal surveys, and online surveys.
Example: Conducting a nationwide survey to measure public opinion on healthcare reform.
Survey research is a quantitative approach that involves researchers sending out a set of questions to a group of people. It serves as a valuable tool for defining the attributes, dispositions, conduct, and viewpoints of a large population. Statistical data is employed in the process of analysis to either validate or disprove theories. Respondents provide responses by completing polls, surveys, or questionnaires. Survey questions that are both open-ended and closed-ended are essential for achieving effective results. Surveys can be carried out either through online or offline methods, involving considerable sample sizes. Researchers analyse a representative sample and subsequently extrapolate the results to the broader population. An advantage of surveys is their capacity to gather data from a sample that accurately represents a vast population. Examining extensive datasets enables more dependable extrapolations to the wider populace in contrast to data obtained from a single case studies (Ghanad, 2023).
The two forms of survey that can be used based on the time and type of data are:
Cross-sectional survey
Longitudinal survey
: ̗̀➛ 3. Longitudinal Research
Purpose: To study changes in variables over time.
Characteristics: It involves repeated observations of the same variables over long periods.
Methods: Panel studies, cohort studies, and time series analysis.
Example: Tracking the career progression of a group of graduates from the same university over ten years.
Longitudinal surveys, often referred to as observational surveys, entail researchers evaluating the same individuals repeatedly over a period of time in order to monitor any alterations that take place. This survey methodology is extensively employed in diverse domains including as health, applied sciences, and market analysis to assess customer satisfaction or collect feedback on products and services. Longitudinal studies are a type of correlational research in which researchers watch and gather data on several variables without trying to change them. These studies are prevalent in the fields of economics, social sciences, and medicine (Ghanad, 2023).
: ̗̀➛ 4. Cross-Sectional Research
Purpose: To analyze data from a population at a single point in time.
Characteristics: It provides a snapshot of the variables of interest and is often used to infer prevalence and relationships.
Methods: Surveys, observational studies, and secondary data analysis.
Example: A survey conducted to determine the prevalence of smoking among adults in a city at a specific time.
Cross-sectional surveys, which fall under the category of observational research, entail gathering data from a representative subset of the intended population at a particular moment in time. Researchers evaluate numerous variables continuously. These surveys collect data from individuals who are alike in all aspects except for the variables being studied, enabling the analysis and comparison of many samples. In contrast, longitudinal surveys gather data from the same individuals over a period of time, with a specific emphasis on a smaller cohort that has similar attributes. This approach differs from cross-sectional research, which involve the collection of data from multiple people simultaneously (Ghanad, 2023).
ꕀ Types of Quantitative Research (2)
ꕀ Types of Quantitative Research (2)
: ̗̀➛ 5. Experimental Research
: ̗̀➛ 5. Experimental Research
Purpose: To establish cause-and-effect relationships between variables.
Characteristics: It involves the manipulation of one or more independent variables to observe their effect on dependent variables while controlling other factors.
Methods: Laboratory experiments, field experiments, and randomized controlled trials.
Example: An experiment to test the effectiveness of a new drug where one group receives the drug and another group receives a placebo.
Experimental research, an early kind of quantitative inquiry, aims to establish correlations between variables by directly observing and systematically evaluating relationships of causality (Creswell, 2014 as referenced by Rana et al., 2021). Variables are commonly categorised as independent, which are changed, and dependent, which are observable consequences. Within a laboratory setting, the independent variable could encompass a novel pharmaceutical substance or immunisation, whilst the dependent variable may pertain to measurable results such as infection rates or cellular growth. Outside of the laboratory setting, independent variables might encompass behaviours such as smoking. These exposures are examined to determine their impact on outcomes, taking into account potential confounding factors.
When conducting experimental research, a number of factors need to be carefully considered. In order to formulate testable hypotheses, one must have a solid comprehension of the body of existing research and concepts (Rana et al., 2021). Randomised controlled trials (RCTs) and quasi-experimental research are the two primary forms of experimental design. Participants in RCTs, the gold standard are randomised at random to either the treatment group or control groups. When randomization is not practical, quasi-experimental research allocate individuals according to pre-existing traits.
In order to prevent misleading results, it is necessary to consider confounders, which are variables that have an impact on the dependent as well as the independent variables. When investigating the effects of smoking on lung cancer, researchers must take into account alcohol use as a confounding factor. Control groups or placebos are crucial in experimental research as they enable comparisons between individuals who get the treatment versus those who do not. This comparison provides insight into the specific effects of the intervention (Rana et al., 2021).
Ross et al. (2004 as cited by Adedoyin, 2020) highlighted the primary categories of experimental research:
True Experiments.
Repeated Measures.
Quasi-experimental.
: ̗̀➛ 6. Quasi-Experimental Research
: ̗̀➛ 6. Quasi-Experimental Research
Purpose: To estimate the causal impact of an intervention without random assignment.
Characteristics: It resembles experimental research but lacks random assignment, making it more susceptible to internal validity threats.
Methods: Non-equivalent groups designs, pretest-posttest designs, and time series designs.
Example: Evaluating the impact of a new teaching method on student performance where classes are assigned the method based on existing schedules rather than random assignment.
Quasi-experimental research designs display resemblance to experimental designs, although they lack complete control over the variables that are independent. In contrast to actual experiments, participants in quasi-experimental research are not assigned to groups in a randomised way. However, individuals are categorised according to pre-existing attributes like as gender, age, or previous encounter with a stimulus. The intentional distribution of participants in a non-random manner complicates the establishment of conclusive cause-and-effect correlations. Quasi-experimental designs are extremely useful in situations when randomization is not possible or acceptable, for instance in educational or medical research in which it would be unethical to withhold treatment (Villegas, 2023).
True experimental designs may be unfeasible or too costly, especially among researchers with scarce funds. Quasi-experimental designs enable researchers to examine issues by utilising pre-existing data, hence minimising expenses and logistical difficulties. Quasi-experimental research can provide better management of confounding variables in comparison to solely observational studies, hence boosting their external validity. While its internal validity may not be as robust as actual experiments, it nevertheless exceeds most of various non-experimental methods, making them a reliable alternative when randomization is not feasible (Villegas, 2023).
: ̗̀➛ 7. Correlational Research
: ̗̀➛ 7. Correlational Research
Correlational study aims to identify variables that display a relationship, such that a modification in one variable leads to a corresponding modification in the other. In this type of design, researchers analyse two variables to determine if there is a statistically significant connection between them, without changing the variables. Correlation measures the magnitude or orientation of the association involving more than one variable. For instance, they may evaluate the relationship between food and anxiety. In contrast to experimental research, which relies entirely on scientific methodology and hypothesis testing, this type of study is purely descriptive. According to Ghanad (2023), there are three types of correlational studies:
Positive correlational research
Negative correlational research
No correlational research
A correlation might have either a negative or positive direction (Bhandari, 2021).
🎐 Characteristics of Quantitative
🎐 Characteristics of Quantitative
Quantitative research methods involve the collection of numerical data and afterwards the use of statistical, mathematical, or computational approaches for analysis. Below are a few vital characteristics of quantitative research methods.
๋࣭⭑ Data Collection and Data Analysis ๋࣭⭑
๋࣭⭑ Data Collection and Data Analysis ๋࣭⭑
Data Collection ˏˋ°•*⁀➷
In the context of quantitative research, a "research instrument" pertains to the techniques utilised to gather data, including questionnaires, interviews, and observations (Ghanad, 2023). The procedure of quantitative data collection comprises several essential steps:
Determining the standards for selecting participants or samples in the research, with the aim of correctly reflecting the target community.
Securing requisite authorizations from relevant organisations or persons participating in the study, while guaranteeing adherence to ethical and legal protocols.
Deciding on the nature of the data to gather, such as categorical responses, quantitative measures, or alternative data formats.
Carefully choose research tools that are suitable for the study objectives and the characteristics of the data being gathered.
Cautiously overseeing the process of collecting data to guarantee the uniformity, precision, and dependability of the acquired information.
Data Analysis ˏˋ°•*⁀➷
When examining quantitative data, there are multiple interrelated processes that are involved. The initial phase involves arranging the data for analysis, which encompasses the tasks of assigning numerical scores, assessing score categories, employing statistical software, and entering data into a computer. In the second stage, the data is analysed, starting with a descriptive analysis that provides information on central tendency and variance measurements. Afterwards, more intricate inferential studies are performed to examine hypotheses, evaluate confidence intervals, and ascertain effect sizes. Presentation of data in the form of tables, figures, and discussion, summarization of results, and comparison with prior theories and literature comprise the third stage of research reporting. The text finishes by acknowledging the limits of the research and providing recommendations for further studies. In short, this approach guarantees a comprehensive analysis of data in order to meet the study's inquiries and hypotheses (Creswell, 2002 as cited by Ghanad, 2023).
The analysis of data is based upon the specific design and aims of the investigation. Data analysis can be approached in two main ways: descriptive statistics and inferential statistics (Rana et al., 2021).
➺ 1.Descriptive Statistics
➺ 1.Descriptive Statistics
Descriptive statistics simplify and explain sophisticated numeric data, providing understanding. They assist researchers in effectively and comprehensively presenting data (Ghanad, 2023). Essential elements consist of:
Distribution: Frequency of each value.
Central Tendency: Measures like mean, median, and mode.
Variability: Spread of values, measured by standard deviation, variance, range, kurtosis, and skewness.
➺ 2.Inferential Statistics
➺ 2.Inferential Statistics
Inferential statistics entails utilizing a subset of data to draw conclusions and make predictions about the population as a whole. Descriptive statistics offer a concise overview of the sample, whereas inferential statistics allow for more generalizable conclusions. The accuracy of conclusions relies on the utilization of random and unbiased sampling techniques (Ghanad, 2023).
˗ˏˋ꒰Ethical Considerations ꒱;
˗ˏˋ꒰Ethical Considerations ꒱;
Ethical considerations are paramount in quantitative research, ensuring the responsible and ethical conduct of studies (Bhandari, 2024). Key ethical principles:
Informed Consent: Before participating in a study, individuals must fully understand its purpose, procedures, risks, and benefits. Researchers obtain informed consent from participants, allowing them to voluntarily decide whether to take part.
Confidentiality: Personal data collected from participants must be kept confidential and secure. Researchers assure participants that their information will only be used for research purposes and will not be disclosed to unauthorized individuals.
Privacy: Participants' privacy is respected, minimizing intrusion into their personal lives and ensuring their rights are protected. Access to sensitive information requires permission, and data is used solely for research objectives.
Avoiding Harm: Researchers prioritize minimizing potential harm to participants, both physical and psychological. Safeguards are implemented to protect participants, support services are available if needed, and well-being is monitored throughout the study.
Fairness and Equity: Research is conducted without discrimination based on factors like race, gender, religion, or socioeconomic status. Equity is maintained, ensuring equal opportunities for participation and fair treatment of all individuals involved.
Transparency and Integrity: Researchers uphold honesty, transparency, and integrity in their work. Findings are accurately reported, and any conflicts of interest or biases are acknowledged to maintain the credibility and trustworthiness of the research.
☁️ Research Questions ☁️
☁️ Research Questions ☁️
In quantitative research, research questions are developed to analyse the correlations between variables and are often established at the beginning of the research. These factors are exact and usually associated with the target population, characteristics that are influenced by other factors, and the overall plan for doing the research. Research questions may also aim at explaining the behaviour of a population in respect to one or more variables, or describe the qualities of variables that are to be measured (descriptive research questions). These questions may also seek to identify disparities between groups in regard to an outcome variable (comparative research questions), or clarify patterns and interconnections between variables (relationship research questions) (Barroga & Matanguihan, 2022).
● Descriptive Questions ●
This type of research topic is fundamental in quantitative research and seeks to explain the circumstances, locations, causes, or mechanisms behind a certain occurrence. In addition, they utilise data and statistics to provide a description of an event or occurrence (Sirisilla, 2023). Research Question Example: What percentage of Malaysian men and women exceed their daily calorific allowance?
● Comparative Questions ●
Occasionally, it is advantageous to compare one instance with another. Hence, comparative questions become beneficial in the examination of cohorts exhibiting dependent factors (Sirisilla, 2023). For example: What is the difference in time spent on video games between people aged 15-17 and 18-21?
● Relationship-Based Questions ●
This research topic aims to determine the impact of one variable on another. Experimental studies primarily utilise these form of research topics (Sirisilla, 2023). For example: What is the relationship between age and lifestyle amongst women in Australia?
Pros and Cons of quantitative research
Pros and Cons of quantitative research
My Learning Path
My Learning Path
✩ Reflection ✩
✩ Reflection ✩
Through the carefully selected materials for learning I have made accessible, I have greatly expanded my understanding of quantitative research methodologies. As a result of my study of various kinds of quantitative research designs, including descriptive, survey, experimental, and quasi-experimental approaches, I have developed a more profound comprehension of each method. Due to this knowledge, I can now assess if I have selected the best research approach for my mini-project, which has played a significant role in developing its approach to research.
A solid understanding of the research design was one of the most important things I got from the readings. One type of study that collects data through surveys and observational studies is descriptive research, which helps accurately depict the features of a population or phenomenon. My previous experience with descriptive research had been rocky; I had read an article that classified it as quantitative, but another study claimed it belonged to the qualitative category. Consequently, I need clarification on the authors' contradictory perspectives. I wanted to know the answer, so I did my own research to ensure I was on the correct track. At last, I learned that descriptive research can be either quantitative or qualitative, depending on its methodology. This ultimately impacted my understanding and knowledge.
Quantitative research relies heavily on standardized collection tools and statistical techniques, which the content above has also addressed. Because of this, I have been considering the methods and tools I will utilize to do my mini-research project. The statistics and data collection tools I use can greatly affect how true and reliable the results are in the future. Regardless of the nature of the study, ethics must be considered since they determine whether the research may be deemed appropriate. I have integrated the majority of the ideas and principles into my mini-research.
In conclusion, my curated learning materials have helped me acquire more about quantitative research methodologies. Although I learned a lot and had a great experience with this project, I still need to improve my knowledge in some areas. Since I will conduct my final year project next semester, this knowledge will be a navigator for my future self.
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