Week 1 Introduction to Advanced Research Methodologies PDF

Introduction to Advanced Research Methodologies: Differences from Foundational Methods Sub-Topic: Exploring the Evolution from Basic to Complex Research Techniques AGENDA ❑ Overview of Foundational Research Methods: Basics of early research techniques. ❑ Introduction to Advanced...

Introduction to Advanced Research Methodologies: Differences from Foundational Methods Sub-Topic: Exploring the Evolution from Basic to Complex Research Techniques AGENDA ❑ Overview of Foundational Research Methods: Basics of early research techniques. ❑ Introduction to Advanced Research Methodologies: ❑ Overview of sophisticated methods. ❑ Key Differences Between Foundational and Advanced Methods: How methods evolve. ❑ Examples of Advanced Research Methodologies: Practical applications and case studies. ❑ Implications and Applications: How these methods impact research and practice. ❑ Interactive Activity: Engaging exercise to solidify learning. ❑ Q&A: Open discussion and questions. Overview of Foundational Research Methods Purpose: Understand and apply advanced research methods in public administration. Topics Covered: ❑ Overview of advanced research methods ❑ Quantitative and qualitative techniques ❑ Research design and proposal writing ❑ Data collection and analysis ❑ Ethical considerations and practical applications Overview of Foundational Research Methods Definition: Foundational research methods are fundamental techniques used to collect and analyze data, serving as the base for scientific inquiry and research. What are Advanced Research Methods? Advanced research methods involve complex techniques and strategies used to collect, analyze, and interpret data beyond basic methods. Basic vs. Advanced Methods Basic Methods: Simple surveys, and descriptive statistics. Advanced Methods: Complex statistical analyses, mixed methods research. Importance: Enhances the ability to address complex research questions and provide actionable insights for public administration. Key Research Methodologies: 1. Quantitative Methods/ Research Techniques A. Surveys: Use structured questionnaires to gather numerical data from a large population. Example: Customer satisfaction surveys. Key Research Methodologies: 1. Quantitative Methods/ Research Techniques B. Experiments: Test hypotheses through controlled variables and conditions. Example: Clinical trials testing the efficacy of a new drug. Key Research Methodologies: 1. Quantitative Methods/ Research Techniques C. Statistical Analysis: Apply statistical methods to analyze data patterns and relationships. Example: Regression analysis to study the effect of advertising on sales. Quantitative Research Techniques Content: Descriptive Statistics: Measures of central tendency (mean, median, mode)Measures of variability (standard deviation, range) Inferential Statistics: Hypothesis testing (t-tests, chi-square tests)Regression analysis (simple and multiple regression) Data Collection: Surveys, experiments, secondary data analysis Visuals: Example graphs and charts (e.g., bar charts, scatter plots).  See video on downloads Comparative vs inferential 2. Qualitative Methods/Research Techniques A. Interviews: Conduct in-depth, one-on-one discussions to explore individual perspectives. Example: Interviews with respondents to understand their experiences. 2. Qualitative Methods/Research Techniques B. Focus Groups: Facilitate group discussions to gather diverse opinions and insights. Example: Focus groups to explore consumer attitudes towards a new product. 2. Qualitative Methods/Research Techniques C. Case Studies: Perform a detailed examination of a single case or a small number of cases. Example: A case study on a company's response to a crisis. Qualitative Methods/Research Techniques Content: Data Collection: Interviews: Structured, semi-structured, unstructured Focus Groups: Group discussions to gather diverse perspectives Observations: Detailed notes on behaviors and interactions Data Analysis: Thematic Analysis: Identifying and analyzing patterns or themes Coding: Categorizing data into themes or concepts Visuals: Example of a coding scheme or thematic analysis map. Purpose: 1. Establish foundational principles. 2. Identify basic patterns, trends, and relationships in data. Mixed Methods Research: Integration of quantitative and qualitative approaches Provides a comprehensive understanding of research problems Mixed Methods Research: Content: Definition: Combines qualitative and quantitative methods to leverage the strengths of both. Benefits: Provides a comprehensive view of research problems Validates findings through triangulation Examples: Combining surveys (quantitative) with interviews (qualitative) Challenges: Complexity in the design and integration of data Visuals: Venn diagram or flowchart illustrating integration of methods. Research Design: Content: Components: Problem Statement: Clearly defined research problem or question Objectives: Specific goals of the research Methodology: Methods for data collection and analysis Importance: A well-structured design ensures the validity and reliability of research results. Visuals: Flowchart of the research design process from problem statement to conclusions. Data Collection Strategies: Content: Overview of Methods: Quantitative: Surveys, experiments, existing datasets Qualitative: Interviews, focus groups, observations Choosing Methods: Based on research questions, objectives, and resource availability Considerations: Reliability, validity, and ethical issues Visuals: Comparison table of data collection methods, highlighting pros and cons. Data Analysis Techniques: Content: Quantitative Analysis: Statistical Tests: ANOVA, regression, correlation Data Visualization: Histograms, scatter plots Qualitative Analysis: Thematic Analysis: Identifying and interpreting patterns Narrative Analysis: Analyzing stories and personal accounts Visuals: Example outputs from statistical software (e.g., regression tables) and qualitative analysis (e.g., thematic maps). Ethical Considerations: Content: Key Principles: Informed Consent: Participants must be fully aware of the study's purpose and their rights. Confidentiality: Protecting participants' personal information. Data Protection: Secure storage and handling of research data. Importance: Ensures credibility and integrity of the research. Visuals: Checklist or flowchart of ethical research practices.  ETHICAL PROTOCOL Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication. Ethical Issues ❑To whom did you ask permission for the conduct of your research? How did you do it? ❑Were the participants of your research informed that they are going to be part of a research? If Yes, how did you inform them? ❑If the respondents are children, the parents should also be informed that their children will be part of your study. ❑Did you use any literature? If yes, did you acknowledge them properly? How? Common mistakes ❑Completely missed to include this section ❑Concentrated on the process of submitting the proposal, not on the plan to uphold ethical standard during data collection ❑Does not acknowledge references Example of a well written ethical concerns section Since the study will elicit personal information from the participants, the researchers chose to adhere with the Data Privacy Act of 2012. The researchers will seek permission from the School Principal regarding the approval of the study. Participants will have their rights read to them before the experiment and the interviews will begin. A participant has the right to leave the study at any time. The researchers will also be taking photos without the participants' discernable faces. Students who will undergo the experiment will remain anonymous. Codes, not names, will be used in analyzing results to safeguard confidentiality of data. Key informants who will participate in the interviews will not be revealed in the study. Codes will also be used to maintain anonymity of the subjects. Recordings will be destroyed after transcription of the interviews. No information in the study will be directly associated to the key informants name to protect their identities.  This study will consider three ethical issues into account. These will include, informing the consent, information confidentiality as defined by the Data Privacy Act of 2012, and the researcher’s role and responsibilities. In order to build trust and confidence between the researcher and the respondents, the researchers will inform the respondents. The nature of the study and the expectations associated with their participation will be explained by the researcher. Part of this ethical responsibility included ensuring that the young people’s participation in the study will be voluntarily. Force, coercion, and intimidation will never be used as manifestations of gross disrespect for others’ fundamental rights. Validity Extent to which a research tool measures what it is ought to measure. When the research tool is made by the researcher, validity test is required. Table 1. Levels of Validity (Ragma, 2016) Points Statistical Range Descriptive Equivalent Rating 5 4.21-5.00 Very High Validity (VHV) 4 3.41-4.20 High Validity (HV) 3 2.61-3.40 Moderate Validity (MV) 2 1.81-2.60 Poor Validity (PV) 1 1.00-1.80 Very Poor Validity (VPV) Sample validation procedure using 3 validators Validator A Validator B Validator C Average Face Validity 5 5 5 5 Content Validity A. Functionality 5 4 5 4.67 B. Acceptability 5 4 5 4.67 C. Appropriateness 5 4 4 4.33 D. Timeliness 5 4 4 4.33 E. Implementability5 5 5 5 F. Sustainability 5 4 5 4.67 Average 5 4.28 4.71 4.67 Interpretation VHV VHV VHV VHV Sample validation procedure using 3 validators A B C Average ❑ The items are valid representatives of the scope 5 3 4 4 ❑ The test items cover the course content and the desired learning competencies 3 5 4 4 ❑ The items are clear and do not warrant misconceptions 5 4 5 4.67 ❑ The items are written based on the participant’s understanding 4 3 5 4 ❑ The items lead to an acceptable answer 4 3 3 3.33 ❑ The items are free from any errors 3 5 4 4 ❑ Generally, the tool measures what it intends to measure 4 4 5 4.33 Average 4 3.86 4.29 4.05 Interpretation HV HV VHV HV Reliability Extent to which a research tool ascertains dependability and consistency. When the tool elicits quantitative data, reliability testing is a must. When a tool is qualitative, reliability testing is not required. Table 2. Interpreting Reliability Coefficient(Ragma, 2016) Statistical Range Descriptive Equivalent Rating 1.00 Perfect Reliability 0.71-0.99 Very High Reliability 0.51-0.70 High Reliability 0.21-0.50 Low Reliability 0.01-0.20 Negligible Reliability 0.00 No reliability  Action Research in the New Normal: Rediscovering the Basics Reliability Reliability Tools When to use KR-20 & Split-half Used when the tool to be measured is a test (Multiple choice or dichotomous questions) KR-21 Used when questions identify no correct answer (Rating scales) Cronbach’s Alpha Used in research tools employing Likert Scales IN-SERVICE TRAINING FOR TEACHERS Practical Tips for Conducting Research Planning: Develop a clear research plan and timeline. Execution: Maintain consistency and rigor in data collection. Analysis: Use appropriate tools and techniques for accurate results. Reporting: Communicate findings clearly and effectively. Introduction to Advanced Research Methodologies Advanced research methodologies involve sophisticated and complex techniques that build on foundational methods to address more intricate research questions and provide deeper insights. Purpose: ✓ Address complex research questions that go beyond basic analysis. ✓ Provide nuanced understanding and more robust findings. Characteristics: ❑ Complex Data Analysis: Utilizes advanced statistical techniques and algorithms for in-depth analysis. Example: Machine learning algorithms for predictive analytics. Characteristics: ❑ Multi-Method Approaches: Integrates multiple research methods for a more comprehensive analysis. Example: Combining quantitative surveys with qualitative interviews. Characteristics: ❑ Emphasis on Theory Development: Focuses on creating or refining theories based on comprehensive data analysis. Example: Developing new theoretical models from grounded theory research. Key Differences Between Foundational and Advanced Methods Complexity: Foundational: Generally simpler, with straightforward techniques and analyses. Advanced: Involves complex processes, including multi-layered analyses and sophisticated tools. Data Analysis: Foundational: Basic statistical methods such as mean, median, and standard deviation. Advanced: Advanced statistical techniques and data science methods like regression analysis, factor analysis, and machine learning algorithms. Research Questions: Foundational: Often addresses descriptive and exploratory questions (e.g., What is the average satisfaction level?). Advanced: Focuses on explanatory and predictive questions (e.g., How does marketing influence consumer behavior over time?). Matching research questions to data analysis techniques What is the big picture? In the first instance, your data can be used to paint an overall picture of what is going on and what your audience is doing. It may help to answer questions such as: Is there a problem? What does the problem look like? and what the scale of the problem is. 1.Advanced Research Case Study Methodologies (Part 1) Methodology: Overview: In-depth analysis of a single case or a small number of cases within their real-life context. Application: Useful for exploring complex issues that cannot be easily examined using other methods. Example: Analyzing a company's strategic decisions during a crisis. Advanced Research Methodologies (Part 1) 2. Grounded Theory: Overview: A qualitative research method that involves developing theories based on data collected from the field. The goal is to build theory from the ground up. Application: Helps researchers understand and develop theories from qualitative data. Example: Developing a theory of organizational change from interviews with employees. See video from download Advanced Research Methodologies 1. Mixed Methods Research: Overview: Combines quantitative and qualitative research methods in a single study to provide a fuller picture. Application: Useful for capturing both numerical data and contextual understanding. Example: Combining survey data with focus group feedback to evaluate a new policy. 2. Systematic Reviews and Meta-Analysis: Overview: Systematic reviews aggregate and critically assess existing research findings, while meta-analysis statistically combines results from multiple studies. Application: Provides comprehensive insights and identifies trends or gaps in research. Example: A meta-analysis of clinical trials to determine the effectiveness of a new treatment. Examples of Advanced Methodologies in Practice Example 1: Machine Learning Algorithms Study Description: Machine learning techniques analyze large datasets to make predictions or identify patterns. Example: Using machine learning to predict customer churn based on historical data. Examples of Advanced Methodologies in Practice Example 2: Longitudinal Mixed-Methods Study Description: Combines quantitative data and qualitative insights over time to assess the impact of an intervention. Example: Evaluating the long-term effects of a new teaching method on student performance using test scores and interviews. Practical Impact: Implications and Applications Advanced methodologies offer deeper insights and improved accuracy. They enable researchers to address complex problems and derive more robust conclusions. Industry Applications: Business: Advanced analytics for market research and customer insights. Healthcare: Personalized medicine and longitudinal studies for patient care. Social Sciences: Comprehensive evaluations of policies and interventions. Future Directions: Emerging trends include the use of AI, big data analytics, and new computational methods that continue to evolve research methodologies. Game: "Research Methodology Jeopardy“ Description: A quiz game where participants answer questions related to different research methods and their applications. Teams earn points for correct answers. SUMMARY Definition: Basic techniques used for data collection and analysis, forming the foundation of research. Common Methods: Quantitative Methods: Surveys, experiments, and statistical analysis for numerical data. Qualitative Methods: Interviews, focus groups, and case studies for detailed, descriptive data. SUMMARY Purpose: Establish basic principles and identify patterns or relationships in data. Sophisticated techniques that build upon foundational methods to address complex research questions. Characteristics: Complex Data Analysis: Advanced statistical techniques and algorithms. Multi-Method Approaches: Combining multiple research methods for a comprehensive analysis. Emphasis on Theory Development: Creating or refining theories based on extensive data. Purpose: Provide deeper insights and robust findings for complex research problems. Key Differences Between Foundational and Advanced Methods Complexity: Foundational Methods: Simple. Advanced Methods: Complex and multi-layered. Data Analysis: Foundational Methods: Basic statistical techniques. Advanced Methods: Advanced techniques, including machine learning and complex algorithms. Research Questions: Foundational Methods: Descriptive and exploratory. Advanced Methods: Explanatory and predictive. Case Study Methodology: Overview: In-depth analysis of a single or small number of cases within their real-life context. Application: Ideal for exploring complex, context-specific issues. Grounded Theory: Overview: Develop theories based on data collected from the field. Application: Generates new theories from qualitative data without preconceived hypotheses. Mixed Methods Research: Overview: Combines quantitative and qualitative methods in one study for a comprehensive view. Application: Captures both numerical data and contextual insights. Systematic Reviews and Meta-Analysis: Overview: Systematic reviews aggregate research findings, while meta-analysis statistically combines results. Examples of Advanced Methodologies in Practice Machine Learning Algorithms: Example: Used for predictive analytics, such as forecasting customer behavior based on historical data. Longitudinal Mixed-Methods Study: Example: Evaluate an intervention's long-term effects using quantitative metrics and qualitative feedback. Implications and Applications Practical Impact: Advanced methodologies provide deeper insights, improve research accuracy, and address complex issues effectively. Industry Applications: Business: Enhances market research and customer insights. Healthcare: Supports personalized medicine and long-term patient studies. Social Sciences: Offers comprehensive evaluations of policies and interventions. Future Directions: Emerging trends include AI, big data, and evolving computational methods. References Books: Creswell, J.W., & Creswell, J.D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications. Yin, R.K. (2018). Case Study Research and Applications: Design and Methods. Sage Publications. Articles: Tashakkori, A., & Teddlie, C. (2010). SAGE Handbook of Mixed Methods in Social & Behavioral Research. Sage Publications. Higgins, J.P.T., & Green, S. (Eds.). (2011). Cochrane Handbook for Systematic Reviews of Interventions. Wiley-Blackwell. Websites: The Research Methods Knowledge BaseThe SAGE Encyclopedia of Research Design References Berardo, L. S., & Hall, P. L. W. (2020). Research Methods in Public Administration and Public Management. Perry, J. E. (2019). Quantitative Research Methods for Public Administration. Newcomer, K. E. (2018). Qualitative Research Methods for Public Administration. McMillan, D. W. (2017). The Research Proposal: A Practical Guide. Squires, B. L. (2021). Ethics and Integrity in Research.Marquardt, J. E. (2019). Data Analysis for Public Administration.Hall, P. A. (2022). Communicating Research Results. O’Connell, M. J. (2020). Applying Research in Public Administration.