Summary
Highlights
A recap of previous topics, including research titles, introductions, purpose statements, questions, and hypotheses. The current session focuses on the method section, covering research design, sample, instrument, data collection, data analysis, and other relevant aspects of the quantitative research process. The methods section is crucial for establishing the credibility and believability of research findings.
The video clarifies the distinction between 'methods' and 'methodology'. Methods are specific techniques for data collection and analysis (e.g., surveys, interviews). Methodology is a broader term encompassing a set of methods, philosophical, and theoretical frameworks guiding the study's design. The purpose of the methodology section is to demonstrate transparency, reproducibility, validity, reliability, and trustworthiness of the research.
There isn't a one-size-fits-all template, but a research design should align with the study's objectives. Subheadings like research design, environment, participants, instruments, data gathering, data analysis, and ethical considerations are recommended for clarity. The sections describe who, what, where, when, and how the research will be conducted, ensuring thoroughness and detail for potential replication or verification.
The research design section outlines the general approach (quantitative, qualitative, mixed-methods) and the specific design (e.g., experimental, correlational, survey, observational, causal-comparative, quasi-experimental). Justification for the chosen design is essential, explaining why it is appropriate for the research question and citing relevant scholars or studies. Alignment of the design with the study's purpose is crucial for validity.
This section describes the study's setting. It's not limited to geographical location but can include profiles, historical accounts, vision/mission of organizations, or features of participants. Relevance is key; only information pertinent to the research purpose should be included. For non-experimental studies, both environment and local are presented, while experimental studies typically focus only on the local of study, for ethical considerations, specific names of schools/organizations are generally anonymized.
This section details who or what was studied. If the research involves only data analysis (e.g., data mining), this section can be excluded. Distinctions are made between 'respondents' (for surveys/questionnaires) and 'subjects' (for intervention or experimental groups). Key details include sample size, sampling technique (probability sampling recommended for quantitative studies), selection criteria, recruitment process, and relevant demographic information. The description should be aligned with the study's focus.
Guidelines for determining sample size are provided, including rules of thumb based on the type of statistical analysis (e.g., 30 for group differences, 50 for correlation/regression). The use of sample size calculators (e.g., G*Power, raosoft.com) is demonstrated. Factors like margin of error, confidence level, and population size influence calculations. Higher confidence and lower margin of error require larger sample sizes. Consulting a statistician is advised if unsure.
This part describes the tools used for data collection, such as surveys, software, or laboratory equipment. Details include the instrument's name, developer, what data it collects, and how it is scored or interpreted. For adapted instruments, any modifications and revalidation efforts must be mentioned, along with explicit permission from original authors. Information on validity and reliability (or calibration for engineering instruments) is crucial to ensure data credibility.
This section outlines, in detail, how data was collected. It goes beyond mere permission-seeking to describe the actual administration process (e.g., interview duration, observation methods, online survey platforms, experimental steps). A step-by-step approach is recommended, along with justifications for chosen procedures and citations. Conditions necessary for successful data collection (e.g., environmental factors in experiments) should also be included. Graphics can be used to illustrate complex procedures.
This section explains how the collected data will be treated and analyzed, typically using statistical tests for quantitative research. It's advisable to align analysis methods with specific research questions or sub-problems. Details include identifying data needed for each sub-problem, describing the treatment and analysis per sub-problem, and providing justifications and citations for chosen methods. Mentioning software used (e.g., IBM SPSS Statistics) and statistical significance thresholds (e.g., p < 0.05) is also important.
This crucial section outlines how ethical standards are maintained throughout the research. For studies involving humans or animals, ethical approvals (e.g., IRB, ethics committee) are often required. Researchers must discuss how they ensure the welfare of participants, including informed consent, confidentiality, voluntary participation, and protection of vulnerable groups (minors, indigenous people, persons with disabilities). Data management, storage, and disposal protocols to protect sensitive information are also covered. Even for literature reviews, proper attribution to original authors is an ethical consideration.