This section provides more information about the HR 4 Excellence Award and the HR Strategy for Researchers in ASE. 

Overview:

This module provides a foundational understanding of the basics of research. It focuses on the scientific method and various research designs and introduces primary data collection techniques. This groundwork is essential for all aspiring researchers to design, conduct, and analyze research effectively.

The Scientific Method:

The scientific method is a systematic research approach. It begins with a question or problem, followed by background research to formulate a hypothesis. This hypothesis is then tested through experimentation. The results are analyzed, leading to conclusions and further questions or hypotheses. This iterative process is crucial in building a robust body of scientific knowledge.

• Further Reading: 
• Understanding the Scientific Method. 
• A more detailed insight: https://plato.stanford.edu/entries/scientific-method/

Research Design:

Research design is the blueprint for conducting a study. It outlines the procedures for collecting, measuring, and analyzing data. Find out more in the next module. 

Two primary types of research designs are:

1. Qualitative Research Design: Focuses on understanding human behavior and the reasons governing such behavior. It involves collecting non-numerical data like text, video, or audio. 

   • Example: Case studies, interviews, or observational research.

2. Quantitative Research Design: Involves collecting and analyzing numerical data. It is used to quantify attitudes, opinions, behaviors, and other defined variables.

   • Example: Surveys, experiments, or correlational studies.

Data Collection Techniques:

Data collection is a critical step in the research process. The technique chosen must align with the research question and design.

Learn more about data collection: https://online.hbs.edu/blog/post/data-collection-methods

Find here a comprehensive guide on data collection: https://www.surveycto.com/resources/guides/data-collection-methods-guide/

 Common methods include:

1. Surveys: Widely used in quantitative research for collecting data from a predefined group of respondents. Surveys can be in the form of questionnaires or interviews.

2. Observations: A qualitative method where data is gathered by watching participants in a natural or controlled environment.

3. Experiments: Involving a controlled environment where variables are manipulated to observe their effect on other variables.

4. Secondary Data Analysis: Utilizing existing data collected for different research. It is efficient and can provide insights into trends over time.

Conclusion:

This module serves as a stepping stone into the world of research. Understanding these foundational elements is critical to developing structured, reliable, and valuable research in contributing to the existing body of knowledge.

Overview

This module delves into the distinct yet complementary realms of quantitative, qualitative, and mixed research methods. Understanding these approaches is crucial for researchers to choose the proper method for their research questions.

Quantitative Research Methods

Quantitative research involves systematically investigating observable phenomena via statistical, mathematical, or computational techniques. Key aspects include:

1. Survey Research: A popular quantitative method involving structured questionnaires to collect quantifiable data from samples, which is then generalized to larger populations. Useful in understanding trends, attitudes, or behaviors.

   • Example: Market research surveys.

2. Experimental Research: Involves manipulation of variables to observe changes in another variable. It's highly controlled and allows for establishing cause-and-effect relationships.

   • Example: Clinical trials in medicine.

3. Statistical Analysis: The cornerstone of quantitative research, it involves analyzing data using statistical tools to infer patterns and relationships.

   • Example: Regression analysis in economic research.

Qualitative Research Methods

Qualitative research is primarily exploratory research, used to gain an understanding of underlying reasons, opinions, and motivations. It includes:

1. Case Studies: An in-depth study of a particular situation rather than a sweeping statistical survey. It provides a systematic way of looking at events, collecting data, analyzing information, and reporting the results.

   • Example: A case study of a particular organization.

2. Ethnography: Involves the study of cultures and communities through close observation and interaction. It's about immersing in the environment to understand the subjects in their natural setting.

   • Example: Studying tribal communities.

3. Grounded Theory: It's a research method in which theory is developed from data rather than vice versa. This method is helpful in creating theories when little is known about the subject.

   • Example: Research in social sciences where little prior theory exists.

Mixed Methods Research

Mixed methods research combines quantitative and qualitative research techniques, approaches, concepts, or language into a single study. This holistic approach provides a richer understanding of the research problem.

1. Sequential Explanatory Strategy: Starts with quantitative data collection and analysis, followed by qualitative data collection and analysis. The purpose is to use qualitative results to assist in explaining and interpreting the findings of the quantitative study.

   • Example: A study on patient satisfaction starting with a survey (quantitative) followed by patient interviews (qualitative).

2. Sequential Exploratory Strategy: It involves starting with qualitative data collection and analysis, followed by quantitative data. It's useful when developing and testing a new instrument.

   • Example: Exploratory interviews to develop a questionnaire.

3. Convergent Parallel Strategy: Both qualitative and quantitative data are collected in parallel, analyzed separately, and then compared or related.

   • Example: Simultaneously conducting surveys and focus groups on a topic.

Conclusion

Each research method has its strengths and limitations. The choice of method often depends on the nature of the research question, the researcher's skill set, and the available resources. Understanding these methods enables researchers to apply the right approach to their investigations, ensuring robust and credible results.

Overview:

Data analysis is a pivotal stage in the research process, transforming raw data into meaningful insights. This module focuses on three key areas: descriptive statistics, inferential statistics, and data visualization, each critical for interpreting and presenting data effectively.

Descriptive Statistics:

Descriptive statistics summarize the data, offering a quick look at the sample and measures of the variables. It includes:

1. Measures of Central Tendency: Mean (average), median (middle value), and mode (most frequent value). These measures provide an insight into the data's central point.

2. Measures of Variability: Range, variance, standard deviation, and interquartile range. They describe the spread and dispersion of the data.

3. Frequency Distributions: Count, proportions, and percentages that show how often values occur in the dataset.

• Further Reading: https://corporatefinanceinstitute.com/resources/data-science/descriptive-statistics/

Inferential Statistics:

Inferential statistics enable researchers to make predictions or inferences about a population based on a sample of data. It includes techniques like:

1. Hypothesis Testing: Determining the likelihood that a given hypothesis is true based on sample data. Common tests include t-tests, chi-square tests, and ANOVA.

2. Regression Analysis: Understanding relationships between variables. It can be simple linear regression for two variables or multiple regression for more.

3. Confidence Intervals: Providing a range within which the true population parameter is expected to lie, with a certain level of confidence.

• Resource: https://corporatefinanceinstitute.com/resources/data-science/inferential-statistics/

Data Visualization:

Data visualization is the graphical representation of information and data. Effective visualizations help communicate information clearly and efficiently. Key types include:

1. Charts and Graphs: Bar charts, histograms, line graphs, and scatter plots. Each has its use depending on the type of data and the information to be conveyed.

2. Infographics: Combine graphics with data to present complex information quickly and clearly.

3. Interactive Visualizations: Allow users to explore data and discover patterns and insights dynamically.

• Tutorial: Data Visualization Techniques

ASE community members may request access to a course on BI&CI for entrepreneurs (with more information on Data Analysis and data visualization) - available as a Google Classroom - https://classroom.google.com/c/MjgzMzk2NDk0Nzla?cjc=ayys53c

(request emailto: dorel.paraschiv@ase.ro - Project Manager for project CIEN - the source of the course)

Conclusion:

A comprehensive understanding of data analysis is crucial for any researcher. This module provides foundational knowledge and resources to analyze and interpret data effectively, enabling researchers to draw meaningful conclusions and make informed decisions.

Overview:

Research ethics are fundamental to conducting any form of research. This module delves into the crucial aspects of ethical considerations, such as informed consent, confidentiality, and data protection. Understanding and applying these principles is critical to maintaining research integrity and social responsibility.

Informed Consent:

Informed consent is informing research participants about what they can expect in your study, ensuring they understand and agree to the terms willingly. Vital elements of informed consent include:

• Voluntary Participation: Ensuring that participation is voluntary and without any coercion.

• Full Disclosure: Providing all necessary information about the research purpose, procedures, risks, and benefits.

• Comprehension: Ensuring that the participant understands the information given.

• Further Reading: Informed consent examples and guidelines in EU FP7 - https://ec.europa.eu/research/participants/data/ref/fp7/89807/informed-consent_en.pdf

Confidentiality:

Confidentiality in research involves ensuring that the information provided by participants is not disclosed to unauthorized individuals. This includes:

• Data Anonymization: Removing any personal identifiers from the data.

• Secure Data Storage: Using encrypted and secure methods to store sensitive information.

• Limited Access: Restricting data access to only those who need it for research purposes.

• Resource: Best Practices in Maintaining Confidentiality in Research

Data Protection:

Data protection is about safeguarding personal data collected during research. This includes:

• Compliance with Data Protection Laws: Adhering to relevant laws and regulations like the GDPR (General Data Protection Regulation) for handling personal data. Learn more about this here. You may also contact the GDPR responsible in ASE. 

• Data Use and Sharing: Establishing clear rules about how data is used, stored, and shared.

• Participant Rights: Respecting participants' rights to access, correct, or delete their data.

• Reference: Data Protection and Research: A Guide

Ethical Approval and Oversight:

For certain types of research, especially involving human subjects, obtaining ethical approval from an Institutional Review Board (IRB) or Ethics Committee is mandatory. Usually, in economics research, this is not the case. However, if you consider that your research may fall under this category, either discuss it with the Coordination Committee (for Ph.D. theses) and/or with the Research Management Office for future guidance. 

This process involves:

• Review of Research Proposal: Evaluation of the research plan for ethical considerations.

• Ongoing Oversight: Regular review and monitoring of the research process.

Conclusion:

Ethical considerations are not just formalities; they are essential components that uphold the research's integrity and protect the participants' rights and well-being. Adhering to these principles is crucial for any researcher to conduct responsible and credible research.

Overview:

Writing a research proposal is a critical skill for researchers in economics or business. This module guides you through the essential components of a proposal, focusing on formulating research questions and hypotheses and outlining your research design.

Crafting Research Questions:

Your research question is the foundation of your proposal. It should be clear, focused, and feasible. In economics or business, a question might explore market trends, consumer behavior, or economic policies. For example, a question like, "How does consumer spending behavior change during economic recessions?" is specific and measurable.

• Tips for Formulating Research Questions:
  • Ensure clarity and specificity.
  • The question should be researchable within your resources.
  • Align it with existing literature and theories.

Developing Hypotheses:

A hypothesis is a testable prediction that stems from your research question. It should be concise and establish a relationship between two or more variables. For instance, based on the previous question, a hypothesis could be, "Consumer spending on luxury goods significantly decreases during economic recessions."

• Characteristics of a Good Hypothesis:
  • Clearly states the expected relationship.
  • Is testable with your research design.
  • Relates directly to the research question.

Research Design:

Your research design is a roadmap for how you plan to conduct your study. It includes your methodology, data collection, and analysis plan. In economics or business, this might involve quantitative methods like surveys or analysis of financial records or qualitative methods like interviews or case studies.

• Example of a Research Design:
  • Study Type: Quantitative
  • Data Collection: Survey of consumer spending habits over the last five years.
  • Analysis: Statistical analysis to identify spending patterns during economic downturns.

Structuring Your Proposal:

A well-structured research proposal typically includes the following sections:

1. Introduction: Introduce your topic, state the research question, and explain its significance.
2. Literature Review: Summarize existing research and show how your study fills a gap.
3. Methodology: Detail your research design, data collection methods, and analysis plan.
4. Expected Results: Discuss what results you anticipate and their potential implications.
5. Budget and Timeline (where applicable): Outline the resources needed and a timeline for your research. 

• Key Considerations:
  • Ensure coherence and logical flow.
  • Clearly demonstrate the feasibility and relevance of your research.
  • Adhere to any specific guidelines provided by DMCI for your project or the funding body.

A guide on Research Proposal Writing may be found: https://www.grammarly.com/blog/how-to-write-a-research-proposal/

Conclusion:

A strong research proposal is the first step in a successful research project. By carefully crafting your research question, hypothesis, and design, especially within the context of economics or business, you set a clear path for your research journey.

Overview:

Presenting research findings effectively is crucial in communicating the value and implications of your work. Research dissemination is a crucial phase in the research process, involving the communication of research findings to the appropriate audience in an effective and accessible manner. 

This module focuses on the critical aspects of creating impactful visual aids and delivering engaging presentations and, further on, on research dissemination aspects, such as the various channels and strategies for disseminating research to maximize impact and reach.

Creating Effective Visual Aids:

Visual aids play a pivotal role in enhancing the comprehension and retention of your research findings. The most common form is the PowerPoint presentation, but there are many other creative formats like posters, infographics, or interactive digital platforms. See in this module also a list of tools you may use to present. 

1. Clarity and Simplicity: Your visuals should complement your presentation, not overshadow it. Use clear, simple designs and avoid clutter.

2. Data Visualization: Graphs, charts, and diagrams should be easy to understand. Tools like Microsoft Excel, Tableau, or Adobe Illustrator can be used to create compelling visualizations.

3. Consistency in Design: Maintain a consistent color scheme, font style, and layout throughout your visual aids to create a cohesive look.

4. Accessibility: Ensure your visuals are accessible to all audience members, including those with visual impairments. Use large fonts and high-contrast color schemes.

Delivering Effective Presentations:

The way you deliver your research findings is as important as the content itself. An effective presentation engages the audience, communicates your message clearly, and leaves a lasting impact.

1. Know Your Audience: Tailor your presentation to the knowledge level and interests of your audience. This helps in making your content relatable and engaging.

2. Structure Your Presentation: Start with an introduction that outlines your research question and its significance. Present your methodology, findings, and conclude with implications or recommendations. A clear structure helps in guiding the audience through your research journey.

3. Practice and Timing: Rehearse your presentation multiple times to get comfortable with the flow and timing. Aim to deliver your points concisely and leave time for questions.

4. Engaging Delivery: Use a clear and confident voice. Pay attention to your body language and make eye contact with the audience. Engage with them by posing questions or including interactive elements.

5. Handling Questions: Be prepared to answer questions and engage in discussions. Listen to the questions carefully, and answer them thoughtfully and respectfully.

Choosing the Right Dissemination Channels:

The dissemination of research should be tailored to the target audience. Common channels include:

1. Academic Journals: Publishing in peer-reviewed journals is a traditional and respected method of sharing research findings with the academic community.

   • Tip: Choose a journal that aligns with your research field for maximum impact. See a list of journals on UEFISCDI - Scientometrie. 

2. Conferences and Seminars: Presenting at academic conferences or seminars is a great way to engage with peers and receive feedback.

   • Tip: Use visual aids and concise language to communicate your findings effectively.

3. Public Media and Blogs: Using public platforms like media outlets, blogs, or social media can help reach a broader, non-academic audience. However, consider that this type of publishing is not a method that may be accepted by funding bodies or evaluation committees. 

   • Tip: Translate your research into layman’s terms to ensure it is accessible to the general public. 

Effective Communication Strategies:

Effectively communicating research involves more than just presenting data; it requires a narrative that resonates with the audience.

1. Clear and Concise Messaging: Avoid jargon and use simple language to ensure your research is understandable to non-specialists.

2. Visual Aids: Graphs, charts, and infographics can help to illustrate complex data in a digestible format.

3. Storytelling: Framing research within a story can help engage the audience and make the information more memorable.

The Role of Digital Platforms:

In today’s digital age, online platforms play a significant role in research dissemination.

1. Research Databases: Uploading your work to research databases like Google Scholar increases accessibility.

   • Tip: Ensure your research is SEO-optimized to enhance discoverability.

2. Social Media: Platforms like LinkedIn, Twitter, or ResearchGate effectively share research with a diverse audience and network with peers.

Ethical and Legal Considerations:

Be aware of the ethical and legal aspects of research dissemination, especially in terms of copyright, plagiarism, and respecting participant confidentiality.

Conclusion:

Effective research presentation is a skill that enhances the impact of your work. By combining well-designed visual aids with an engaging delivery, you can effectively communicate your research findings and leave a lasting impression on your audience. Effective research dissemination is vital to ensuring that your findings contribute to knowledge, influence policy, and drive innovation. This module provides the tools and knowledge necessary to effectively communicate your research to both academic and non-academic audiences.