1. Quantitative Research:

   • Hypothesis: A testable prediction or statement about the relationship between variables.

   • Survey: A method of data collection using questionnaires or interviews to gather information from a sample population.

   • Experiment: A controlled investigation designed to test cause-and-effect relationships between variables.

   • Variables: Factors that can change or be measured in a study.

   • Random Sampling: The selection of participants from a population to ensure representativeness and minimize bias.

   • Descriptive Statistics: Numerical summaries that describe the main features of data (e.g., mean, median, standard deviation).

   • Inferential Statistics: Techniques used to make predictions and inferences about a population based on sample data.

   • Correlation: A statistical measure indicating the relationship between two variables.

   • Regression Analysis: A method to model the relationship between a dependent variable and one or more independent variables.

   • Validity: The accuracy and appropriateness of research findings and conclusions.

2. Qualitative Research:

   • Phenomenology: A qualitative approach that explores the essence and meaning of experiences for participants.

   • Grounded Theory: An inductive method of generating theories based on data analysis.

   • Ethnography: The study of cultures and social phenomena through participant observation and interviews.

   • Case Study: An in-depth analysis of a single individual, group, or phenomenon.

   • Focus Group: A qualitative data collection method involving a small group of participants discussing specific topics.

   • Thematic Analysis: Identifying and analyzing recurring themes or patterns in qualitative data.

   • Saturation: The point in qualitative research where new data provides no additional insights or themes.

   • Interpretivism: An epistemological approach emphasizing the role of human interpretation in understanding social phenomena.

   • Reflexivity: The researcher's awareness of their own influence on the research process and findings.

   • Triangulation: Using multiple methods or data sources to validate research findings.

3. Mixed Methods Research:

   • Convergent Design: A mixed methods approach where qualitative and quantitative data are collected simultaneously and analyzed separately.

   • Explanatory Sequential Design: A mixed methods approach where quantitative data are collected first, followed by qualitative data to explain the results.

   • Exploratory Sequential Design: A mixed methods approach where qualitative data are collected first to inform the design of quantitative data collection.

   • Sequential Transformative Design: A mixed methods approach where the researcher incorporates a transformative framework to create social change.

   • Sequential Explanatory Design: A mixed methods approach where quantitative data are analyzed first, followed by qualitative data to explore unexpected findings.

4. Research Ethics:

   • Informed Consent: Obtaining voluntary and informed agreement from research participants before their involvement.

   • Confidentiality: Protecting the identity and data of research participants to ensure privacy.

   • Anonymity: Ensuring that the identities of research participants are not linked to their data.

   • Debriefing: Providing participants with information about the research after their involvement.

   • Research Misconduct: Violations of ethical standards in research, such as fabrication, falsification, or plagiarism.

   • Conflict of Interest: Situations where researchers have personal interests that may compromise the objectivity of their work.

   • Institutional Review Board (IRB): An independent committee that reviews and approves research protocols to protect participants.

   • Human Subjects Research: Research involving living individuals and their data or identifiable information.

   • Animal Ethics: Ensuring the ethical treatment and use of animals in research.

5. Sampling Methods:

   • Probability Sampling: A sampling technique where every member of the population has a known chance of being selected.

   • Simple Random Sampling: Selecting participants randomly from the entire population.

   • Stratified Sampling: Dividing the population into subgroups and then randomly sampling from each subgroup.

   • Cluster Sampling: Randomly selecting groups (clusters) from the population and sampling all members within the selected clusters.

   • Non-Probability Sampling: A sampling technique where not every member of the population has an equal chance of being selected.

   • Convenience Sampling: Choosing participants who are readily available and easily accessible.

   • Snowball Sampling: Participants referring other potential participants for recruitment.

   • Purposive Sampling: Handpicking participants based on specific criteria that align with the research objectives.

   • Quota Sampling: Selecting participants to achieve a predetermined proportion based on specific characteristics.

6. Data Collection Methods:

   • Observational Method: Gathering data by directly observing and recording behaviors in a natural or controlled setting.

   • Interview: A structured or semi-structured conversation with participants to obtain information.

   • Questionnaire: A written set of questions to collect data from respondents.

   • Focus Group Interview: A facilitated group discussion with participants to explore their views and experiences.

   • Document Analysis: Analyzing written, visual, or audio records to extract relevant information.

   • Content Analysis: Systematically analyzing and categorizing the content of textual or visual data.

   • Case Study Method: In-depth investigation of a single individual, group, or organization over time.

   • Surveys: Administering questionnaires or interviews to a sample of individuals to collect data.

   • Ethical Considerations: Ensuring that the research respects and protects the rights and welfare of participants.

7. Data Analysis Techniques:

   • Descriptive Analysis: Summarizing and presenting data using measures such as mean, median, and frequency distribution.

   • Inferential Analysis: Drawing conclusions or making predictions about a population based on sample data.

   • Coding: Assigning labels or categories to data for organization and analysis.

   • Content Analysis: Identifying patterns and themes in qualitative data through systematic analysis.

   • Statistical Tests: Methods used to determine the significance of relationships or differences in quantitative data.

   • Chi-square Test: A statistical test to determine the association between two categorical variables.

   • T-test: A statistical test to compare the means of two groups.

   • ANOVA (Analysis of Variance): A statistical test to compare the means of three or more groups.

   • Regression Analysis: Analyzing the relationship between a dependent variable and one or more independent variables.

   • Data Visualization: Representing data graphically to facilitate understanding and interpretation.

8. Research Reporting:

   • Research Proposal: A document outlining the research objectives, methods, and significance to seek approval and funding.

   • Abstract: A concise summary of the research paper or study.

   • Introduction: The opening section of a research paper that provides context and introduces the research topic.

   • Literature Review: A critical analysis of existing literature relevant to the research topic.

   • Methodology: Detailed description of the research design, data collection, and analysis procedures.

   • Findings: Presentation and analysis of the results obtained from data analysis.

   • Discussion: Interpretation and explanation of the results, relating them to existing knowledge.

   • Conclusion: A summary of the research findings and their implications.

   • Limitations: Recognizing and acknowledging the constraints and potential biases in the research.

   • Recommendations: Suggestions for future research or practical applications based on the study's findings.

9. Statistical Analysis Software:

   • SPSS (Statistical Package for the Social Sciences): A software package used for statistical analysis in the social sciences.

   • SAS (Statistical Analysis System): A software suite used for advanced analytics, business intelligence, and data management.

   • R: A programming language and software environment for statistical computing and graphics.

   • Python: A versatile programming language widely used in data analysis and machine learning.

   • STATA: A statistical software package used for data management and analysis.

Remember to familiarize yourself with these research methodologies terms and apply them appropriately when discussing research design, data collection, analysis, and reporting in academic contexts. Keep practicing and expanding your knowledge in the field of research methodologies to enhance your academic proficiency and achieve a higher IELTS band score. Happy studying!