Page 8
Semester 2: Research Methodology and Biostatistics
Research methodology: Meaning, objectives, types, research process, problem definition, research design, sampling design
Research Methodology: Meaning, Objectives, Types, Research Process, Problem Definition, Research Design, Sampling Design
Research methodology refers to the systematic plan for conducting research. It outlines the research methods and techniques to be used while conducting a study.
It provides a framework for the research process and ensures that the research is valid and reliable.
To enhance the understanding of the research topic.
To develop methods for data collection and analysis.
Explores phenomena through non-numeric data, emphasizes understanding the reasons behind behaviors.
Involves numerical data and statistical analysis to test hypotheses and measure variables.
Identifying the research problem
Conducting a literature review
Formulating hypotheses
Designing the research
Collecting data
Analyzing data
Interpreting results
Clarifying what the research aims to solve or understand.
Clear articulation of the problem
Scope of the problem
Significance of the problem
Descriptive: Describes characteristics of the population.
Experimental: Tests causal relationships among variables.
Cross-sectional: Observes a sample at one point in time.
Longitudinal: Studies a sample over an extended period.
Determines how respondents are selected to participate in a study.
Random sampling: Every member of the population has an equal chance of being selected.
Systematic sampling: Every nth member is chosen from a list.
Stratified sampling: Population is divided into subgroups and samples are drawn from each.
Data collection methods: Questionnaire, interview, case study, experimentation, primary and secondary data
Data collection methods
Questionnaire
A questionnaire is a structured tool used to collect data from respondents. It consists of a series of questions, designed to gather specific information relevant to research objectives. Questionnaires can be administered in various ways, including online, face-to-face, or via phone. They are cost-effective, allow for anonymity, and can facilitate the collection of data from a large sample size.
Interview
An interview is a qualitative data collection method involving direct, face-to-face interaction between the researcher and the participant. Interviews can be structured, semi-structured, or unstructured, allowing for flexibility in data gathering. This method is useful for exploring in-depth information, attitudes, and perceptions, providing rich qualitative data, but may be time-consuming and resource-intensive.
Case Study
A case study is an in-depth investigation of a particular individual, group, or event within its real-life context. This method allows for comprehensive analysis and understanding of complex issues, providing insights that may not be achieved through other methods. Case studies are often used in qualitative research but can also be quantitative. They are valuable in understanding specific phenomena but may lack generalizability.
Experimentation
Experimentation is a quantitative research method that involves manipulating one or more independent variables to observe their effect on a dependent variable. This method is often used in controlled settings to establish cause-and-effect relationships. Experiments can be randomized or non-randomized and allow for the collection of statistical data, though they may be limited by ethical concerns and practical constraints.
Primary Data
Primary data refers to data collected firsthand for a specific research purpose. This method includes data gathered through surveys, interviews, observations, and experiments. The advantage of primary data is its relevance and specificity to the research problem. However, it can be time-consuming and expensive to collect.
Secondary Data
Secondary data involves the use of data that has already been collected by others for different purposes. Sources include published research, reports, and databases. Utilizing secondary data can be cost-effective and time-saving, but researchers must consider the relevance, reliability, and potential biases in existing data.
Data organization: Editing, coding, classification, tabulation
Data organization in research methodology and biostatistics
Editing
Editing involves checking and correcting data for accuracy, consistency, and completeness. It is the first step to ensure that the data is ready for analysis. This process may include removing errors, addressing incomplete entries, and ensuring uniform formatting.
Coding
Coding is the process of transforming qualitative data into quantitative data. It involves assigning numerical or categorical codes to responses, making it easier to analyze data statistically. Proper coding is crucial for accurate analysis and results.
Classification
Classification refers to the systematic grouping of data based on shared characteristics. This subtopic involves determining categories for data items to facilitate analyses and comparisons. It enhances the data's interpretability.
Tabulation
Tabulation is the process of summarizing data in a table format. It organizes data in rows and columns, making it easier to analyze and interpret. Effective tabulation helps in comparing different data sets and visualizing trends.
Data representation: Diagrammatic and graphical
Data representation: Diagrammatic and graphical
Introduction to Data Representation
Data representation is essential in understanding and communicating research findings. Diagrammatic and graphical methods help visualize complex data.
Types of Diagrammatic Representation
1. Flowcharts: Used to represent processes or workflows. 2. Venn Diagrams: Useful for showing relationships between different sets. 3. Charts and Graphs: Includes bar charts, pie charts, and line graphs to summarize quantitative data.
Graphical Representation of Data
Graphical representation involves visual tools that help in portraying data trends, patterns, and comparisons, enhancing comprehension.
Importance of Visual Data Representation
Visual representations of data aid in quicker comprehension, effective communication of results, and better decision making.
Best Practices in Data Visualization
1. Keep it simple and clear. 2. Use appropriate scales and labels. 3. Choose colors wisely to avoid confusion.
Statistical measures: Measures of central tendency, dispersion, association, correlation, regression
Measures of Central Tendency
Measures of central tendency describe the center point or typical value of a dataset. Key measures include mean, median, and mode. The mean is the average of all values, the median is the middle value when data is ordered, and the mode is the most frequently occurring value in the dataset.
Measures of Dispersion
Measures of dispersion indicate the variability or spread of a dataset. Common measures include range, variance, and standard deviation. The range is the difference between the maximum and minimum values. Variance measures the average of the squared differences from the mean, while standard deviation is the square root of variance, representing the dispersion in the same units as the data.
Measures of Association
Measures of association evaluate the strength and direction of the relationship between two variables. This includes covariance and correlation. Covariance assesses how two variables change together, while correlation standardizes this measure to a scale from -1 to 1, indicating the strength and direction of the linear relationship.
Correlation
Correlation quantifies the degree to which two variables are related. A positive correlation indicates that as one variable increases, so does the other, while a negative correlation indicates an inverse relationship. The Pearson correlation coefficient is commonly used to assess linear relationships.
Regression
Regression analysis predicts the value of a dependent variable based on the value(s) of one or more independent variables. Simple linear regression uses one independent variable, while multiple regression incorporates several. The results help in understanding the nature of relationships and in making predictions.
Probability and distribution: Rules, normal, binomial, tests of significance, analysis of variance
Probability and distribution
Introduction to Probability
Probability is the measure of the likelihood that an event will occur. It ranges from 0 to 1, with 0 indicating impossibility and 1 indicating certainty. Events can be classified as independent, dependent, mutually exclusive, and exhaustive.
Basic Probability Rules
1. Addition Rule: The probability of the occurrence of at least one of two mutually exclusive events is the sum of their probabilities. 2. Multiplication Rule: The probability of the occurrence of two independent events is the product of their probabilities.
Probability Distributions
A probability distribution shows all the possible outcomes of a random variable and the probabilities associated with each outcome. Common distributions include normal distribution and binomial distribution.
Normal Distribution
The normal distribution is a continuous probability distribution that is symmetrical around the mean. It is characterized by its bell-shaped curve. Key properties include mean, median, and mode being equal, and it is defined by two parameters: mean and standard deviation.
Binomial Distribution
The binomial distribution represents the number of successes in a fixed number of independent Bernoulli trials. It is characterized by two parameters: n (number of trials) and p (probability of success). The probability mass function can be used to calculate the probability of obtaining k successes.
Tests of Significance
Tests of significance are statistical tests used to determine if the observed data significantly differ from a null hypothesis. Common tests include t-tests, chi-square tests, and ANOVA.
Analysis of Variance (ANOVA)
ANOVA is a statistical method used to compare the means of three or more groups to determine if at least one group mean is different from the others. It assesses the influence of one or more factors by comparing the means of different samples.
