Research methodology basics

 Here’s a detailed comparison between ABDC (Australian Business Deans Council) and Scopus , presented in tabular form: Aspect ABDC (Australian Business Deans Council) Scopus Purpose A ranking system specifically for business and management journals. A comprehensive citation database covering a wide range of disciplines, including sciences, social sciences, arts, and humanities. Scope Focuses on journals relevant to business, management, and economics disciplines. Covers journals from multiple disciplines, including business, engineering, medicine, and more. Evaluation Criteria Journals are classified into four tiers: A*, A, B, and C, based on quality and impact in business fields. Includes all journals indexed in Scopus, regardless of discipline, evaluated based on citation metrics and peer-review standards. Database Management Ma...

  CiteScore and Impact Factor (IF) are metrics used to evaluate the influence and quality of academic journals, but they differ in their calculation methods, databases used, and focus. Here's a comparison: 1. Source of Data CiteScore : Based on the Scopus database (managed by Elsevier). Covers a broader range of sources, including peer-reviewed journals, conference proceedings, and book chapters. Impact Factor : Based on the Web of Science database (managed by Clarivate Analytics). Focuses primarily on journals and their citations. 2. Calculation Method CiteScore : CiteScore = Citations in a given year to documents from the past 4 years/Number of documents published in the same 4 years Includes all document types (articles, reviews, editorials, etc.). Impact Factor : Impact Factor = Citations in a given year to articles from the past 2 years/Number of citable articles published in the same 2 years Considers only citable items (articles and reviews). 3. Time Frame CiteScore : Co...

 Here is a list of recommended books on research methodology in finance that offer comprehensive guidance on various techniques, methods, and approaches used in financial research: "Research Methods in Finance" by Chris Brooks This book provides an introduction to research methods and techniques in finance. It covers the essential aspects of quantitative methods, data analysis, and financial modeling, with practical examples. "Quantitative Financial Economics: Stocks, Bonds, and Foreign Exchange" by Keith Cuthbertson and Dirk Nitzsche This book provides an in-depth understanding of quantitative techniques and methods in financial research, including econometrics, time series analysis, and modeling financial data. "Financial Modelling" by Simon Benninga This book focuses on financial modeling techniques with practical examples in Excel. It's a useful resource for researchers needing to apply quantitative methods to real-world finance problems. "App...

  Sampling error and Type I and II errors are crucial concepts in statistics, particularly in hypothesis testing and inferential analysis. Here's a detailed explanation: 1. Sampling Error Definition : Sampling error occurs when the results obtained from a sample differ from the true values of the population due to the fact that only a subset of the population is studied. Causes : Sample size is too small. Sampling method is biased or non-representative. Random variations in sample selection. Impact : Leads to inaccurate estimations of population parameters (e.g., mean, proportion). Mitigation : Use random sampling methods. Increase sample size to reduce variability. Stratify the population to ensure representation of all subgroups. 2. Type I and Type II Errors In hypothesis testing, these errors occur when conclusions about the null hypothesis ( H 0 H_0 H 0 ​ ) are incorrect: Type I Error (False Positive) : Definition : Rejecting the null hypothesis ( H 0 H_0 H 0 ​ ) when it is a...

  Model formulation is the process of developing a mathematical, conceptual, or graphical representation of a real-world phenomenon or problem for the purposes of analysis and decision-making. It involves defining relationships among variables, identifying key parameters, and structuring them into a model that explains or predicts outcomes. Importance of Model Formulation Simplifies Complexity : Reduces a complex problem to manageable components. Enhances Understanding : Provides insights into the underlying mechanisms or relationships. Facilitates Prediction : Helps predict future trends or outcomes based on current data. Guides Decision-Making : Offers a structured approach to evaluate alternatives or test hypotheses. Supports Theoretical Development : Links empirical observations to theoretical constructs. Steps in Model Formulation Define the Research Problem : Clearly identify the problem or phenomenon to be studied. Example: How does advertising expenditure impact product sa...

 A conceptual framework is a visual or narrative structure that outlines the key concepts, variables, and their relationships within a research study. It serves as a blueprint, guiding the researcher on how the study’s components are interconnected. Purpose of a Conceptual Framework Clarify Concepts : Defines key variables and constructs in the study. Establish Relationships : Illustrates how variables are expected to interact. Guide Research : Provides a clear focus for data collection, analysis, and interpretation. Justify Study : Aligns the research with theoretical foundations or prior studies. Identify Gaps : Highlights areas where knowledge is lacking, shaping research objectives. Key Components of a Conceptual Framework Variables : Independent Variables : Factors presumed to influence or cause changes in the dependent variable. Dependent Variables : Outcomes or effects being studied. Moderating/Intervening Variables : Variables that might affect the relationship between ind...

 Here’s a detailed comparison of descriptive statistics and inferential statistics : 1. Definition Descriptive Statistics : Summarizes and describes the main features of a dataset. Inferential Statistics : Draws conclusions, makes predictions, or tests hypotheses about a population based on sample data. 2. Purpose Descriptive Statistics : Provides a snapshot of the data. Focuses on what the data shows . Inferential Statistics : Makes generalizations beyond the dataset. Focuses on what the data means . 3. Scope Descriptive Statistics : Concerned only with the data at hand (sample or population). Inferential Statistics : Goes beyond the data to infer about the population. 4. Techniques Descriptive Statistics : Measures of Central Tendency : Mean, median, mode. Measures of Dispersion : Range, variance, standard deviation. Data Visualization : Charts, graphs, frequency tables. Inferential Statistics : Estimation : Confidence intervals, point estimates. Hypothesis Testing : t-tests, AN...