FoR midterms reviewer

Thesis

A formal academic document that presents the author's research and findings on a specific topic.

Clear Research Question or Problem Statement

Clearly states the main research question or problem the thesis aims to address and provides context for the study.

Original Contribution

Demonstrates originality and contributes new knowledge or insights to the field, building on existing research and identifying gaps in the literature.

Structure and Organization

Follows a logical and organized structure, typically including sections such as introduction, literature review, methodology, results, discussion, and conclusion, and adheres to specific formatting guidelines set by the academic institution.

Literature Review

Summarizes and critically analyzes relevant literature related to the research topic and identifies key theories, concepts, and previous studies.

Methodology

Clearly describes the research design, methods, and procedures used to collect and analyze data, addressing issues of validity, reliability, and ethical considerations.

Data Analysis and Results

Presents the findings of the research in a systematic manner and uses appropriate statistical or qualitative analysis methods.

Discussion

Interprets the results in the context of the research question and relevant literature, discussing the implications of the findings and their contributions to the field.

Conclusion

Summarizes the main findings and their significance, suggesting avenues for future research.

References and Citations

Includes a comprehensive list of references that adhere to a specific citation style (e.g., APA, MLA) and provides proper credit to existing literature and sources.

Clarity and Coherence

Written in clear and concise language, maintaining a coherent and logical flow of ideas.

Rigor and Validity

Demonstrates a rigorous approach to research, with a well-designed methodology and appropriate data analysis, addressing potential sources of bias and limitations.

Critical Thinking

Demonstrates critical thinking skills by analyzing, evaluating, and synthesizing information, formulating well-reasoned arguments and interpretations.

Audience Awareness

Considers the intended audience, which may include both specialists in the field and general readers, balancing technical details with explanations accessible to a broader audience.

Abstract

Includes a concise summary of the thesis, providing an overview of the research question, methodology, results, and conclusion.

Appendices

Includes any supplementary materials, such as raw data, questionnaires, or additional details, in appendices.

Research

A systematic process of inquiry that aims to contribute new knowledge, insights, or understanding to a particular field of study.

Systematic Process

Research involves a systematic and well-defined process with clear steps and procedures, following a structured plan to gather, analyze, and interpret data.

Logical and Rational

Research is based on logical reasoning and a rational examination of facts and evidence, seeking to avoid biases and subjective judgments in the pursuit of objective knowledge.

Empirical Basis

Research relies on empirical evidence, which is information obtained through direct observation or experience, grounded in real-world observations and data.

Critical Exploration

Research involves critical thinking and exploration of existing literature and knowledge, aiming to build on, challenge, or extend existing theories and concepts.

Problem-Oriented

Research is driven by a specific problem or question that needs investigation, aiming to find solutions, answer questions, or contribute to understanding a particular issue.

Clear Objectives

Research is guided by well-defined objectives or goals that outline what the researcher intends to achieve through the study.

Hypothesis Testing

In experimental research, hypotheses are formulated and tested to establish cause-and-effect relationships; non-experimental research may involve exploring relationships, patterns, or trends without hypothesis testing.

Replicability

A good research design should be replicable, allowing other researchers to conduct similar studies to verify or refute the findings.

Ethical Considerations

Research must adhere to ethical standards and guidelines to ensure the welfare of participants and the integrity of the study, with informed consent, privacy, and confidentiality being crucial considerations.

Objective and Unbiased

Research aims to minimize personal biases and subjectivity, maintaining objectivity through systematic data collection and analysis.

Generalization

The findings of research should have relevance beyond the specific context studied, allowing researchers to apply findings to broader populations or situations.

Innovative and Original

Research contributes to the body of knowledge by offering new perspectives, insights, or methodologies, with originality distinguishing valuable research from existing knowledge.

Communication of Results

Research findings are communicated through reports, papers, or presentations, with clear communication being essential for the dissemination of knowledge to the academic community and beyond.

Iterative Process

Research often involves an iterative process, with feedback loops that may lead to modifications in the research design or approach.

Flexibility

Researchers should be open to adjusting their methods or approach based on unexpected findings or challenges encountered during the research process.

Basic Research

Aims to expand knowledge and understanding of a particular phenomenon without any immediate application, often conducted in academic or scientific settings.

Applied Research

Designed to solve practical problems or answer specific questions with immediate real-world applications.

Quantitative Research

Involves the collection and analysis of numerical data, utilizing statistical methods for data analysis.

Qualitative Research

Focuses on exploring and understanding non-numerical data, such as opinions, attitudes, and behaviors, with common methods including interviews, observations, and content analysis.

Descriptive Research

Involves observing and describing the characteristics of a particular phenomenon, often used to generate a snapshot of the current state of affairs.

Exploratory Research

Conducted when little is known about a topic, aiming to identify and define the problem, generate hypotheses, and establish priorities for further research.

Explanatory Research

Seeks to identify the causes and effects of a particular phenomenon, often following descriptive or exploratory research.

Longitudinal Research

Involves the collection of data over an extended period to observe changes or trends over time.

Cross-Sectional Research

Collects data from participants at a single point in time.

Action Research

Conducted by practitioners in a real-world setting to solve practical problems, involving a cyclical process of planning, acting, observing, and reflecting.

Experimental Research

Involves the manipulation of variables to test hypotheses and establish cause-and-effect relationships, often conducted in controlled laboratory settings.

Case Study Research

In-depth exploration of a particular person, group, event, or situation.

Correlational Research

Examines the relationship between two or more variables without manipulating them.

Survey Research

Collects data from a sample of individuals through the use of questionnaires or interviews.

Meta-Analysis

Involves the statistical analysis of multiple studies to synthesize and draw conclusions from existing research.

Ethnographic Research

Involves immersive observation and study of a particular culture or social group.

Grounded Theory

A qualitative research method that aims to develop theories from the data itself.

Mixed-Methods Research

Combines both qualitative and quantitative research approaches in a single study.

Population

refers to the entire group that is the subject of the study.

Sample

a subset of the population chosen for the actual study.

Random Sampling

Each member of the population has an equal chance of being included.

Stratified Sampling

Divides the population into subgroups (strata) based on relevant characteristics.

Convenience Sampling

Involves selecting individuals who are easiest to reach or obtain.

Snowball Sampling

Existing study participants recruit future participants from among their acquaintances.

Systematic Sampling

Involves selecting every kth individual from a list after a random start.

Cluster Sampling

Divides the population into clusters and randomly selects entire clusters.

Quota Sampling

Sets specific quotas for certain characteristics and then non-randomly selects individuals to meet those quotas.

Purposive Sampling

Researchers deliberately choose participants based on specific criteria or their judgment.

Volunteer Sampling

Participants volunteer to be part of the study.

Multistage Sampling

Combines two or more sampling methods in a series of stages.

Probability Sampling

Involves random selection, ensuring each element in the population has a known, non-zero chance of being included.

Non-Probability Sampling

Involves non-random selection, and the probability of any particular element being chosen is unknown.

Power Analysis

Ensures the study is designed to detect a meaningful effect if it exists.

Margin of Error

Indicates the acceptable range of error in estimating population parameters.

Significance Level

Represents the probability of rejecting a true null hypothesis (usually set at 0.05).

Effect Size

The magnitude of the difference or relationship under investigation.

Levels of Measurement

Refer to the different ways in which variables can be classified and measured.

Nominal Level of Measurement

The lowest and least precise level. Variables are categorized or labeled with no inherent order or ranking. Examples include gender, ethnicity, or categories like 'red,' 'blue,' and 'green.'

Ordinal Level of Measurement

Represents a higher level than nominal. Variables have a meaningful order or ranking, but the intervals between them are not uniform. Differences in rank are known, but the magnitude of differences is not meaningful. Examples include ranks in a competition or education levels (e.g., high school, college, graduate).

Interval Level of Measurement

Has all the characteristics of ordinal measurement, but with equal intervals between consecutive points. There is no true zero point, meaning zero does not indicate the absence of the quantity being measured. Examples include temperature measured in Celsius or Fahrenheit.

Ratio Level of Measurement

The highest and most precise level of measurement. Has all the properties of interval measurement, with the additional feature of a true zero point. A zero value indicates the complete absence of the quantity being measured. Examples include height, weight, age, and income.

Nominal Characteristics

Categories with no inherent order.

Ordinal Characteristics

Ordered categories with unequal intervals.

Interval Characteristics

Ordered categories with equal intervals but no true zero point.

Ratio Characteristics

Ordered categories with equal intervals and a true zero point.

Implications for Statistical Analysis

Nominal and ordinal data often use non-parametric statistical tests. Interval and ratio data allow for more sophisticated statistical analysis, including parametric tests.

Surveys and Questionnaires

Researchers design structured sets of questions to collect data from participants. Pros: Efficient for gathering large amounts of data; responses can be easily quantified. Cons: Relies on participants' ability and willingness to provide accurate information; may be limited by response bias.

Interviews

Researchers directly interact with participants to gather information. Pros: Allows for in-depth exploration; can clarify ambiguous responses. Cons: Time-consuming; may be influenced by interviewer bias; may be influenced by social desirability bias.

Observation

Researchers observe and record behaviors, events, or phenomena. Pros: Provides direct and real-time data; useful for studying natural behavior. Cons: Observer bias may affect results; participants may alter behavior when being observed.

Experiments

Researchers manipulate variables to observe the effects on outcomes. Pros: Enables establishing cause-and-effect relationships. Cons: May lack external validity; ethical concerns with certain manipulations.

Field Trials

Similar to experiments but conducted in real-world settings. Pros: Greater ecological validity; results are more applicable to real-world scenarios. Cons: Less control over extraneous variables; may be logistically challenging.

Case Studies

In-depth examination of a single individual, group, or event. Pros: Provides detailed and context-rich information; useful for rare phenomena. Cons: Limited generalizability; subject to researcher bias.

Content Analysis

Systematic analysis of text, audio, or visual content to identify patterns or themes. Pros: Objective analysis of large amounts of data; useful for studying media or communication. Cons: May lack context; subjectivity in coding.

Ethnography

In-depth study of a particular culture or community through participant observation. Pros: Provides a holistic understanding; captures cultural nuances. Cons: Time-consuming; requires immersion; subject to observer bias.

Biometric Data Collection

Measures physiological responses (e.g., heart rate, EEG) to gather data. Pros: Objective and direct measurement; less susceptible to self-report biases. Cons: Requires specialized equipment; ethical considerations.

Web Analytics

Analyzes online user behavior, often used in digital marketing or website optimization. Pros: Real-time data; large sample sizes. Cons: Limited to online activities; may lack context.

Data Entry and Recording

Consistency: Ensure consistent data entry methods and conventions. Accuracy: Double-check data for errors during entry. Validation: Implement validation checks to reduce errors.

Variable Identification

Define Variables: Clearly define and label each variable in the dataset. Coding: Assign unique codes or labels to different categories.

Categorization Methods

Numerical Categorization: Assign numerical codes to represent categories. Textual Categorization: Use descriptive labels or text to represent categories. Alphanumeric Codes: Combine letters and numbers for more detailed categorization.

Hierarchical Categorization

Nested Categories: Organize data hierarchically with subcategories and main categories. Tree Structures: Use tree-like structures to represent relationships.

Use of Software

Database Management Systems (DBMS): Use database software for structured storage and retrieval. Spreadsheets: Excel or similar tools are suitable for smaller datasets.

Coding Schemes

Develop Coding Systems: Create consistent coding systems for categorical variables. Documentation: Document coding schemes for future reference.

Data Dictionary

Create a Data Dictionary: Document details about each variable, including its name, description, type, and permissible values. Metadata: Include information about the source, date, and any transformations applied.

Data Cleaning

Identify Outliers: Detect and address outliers or anomalies in the data. Handle Missing Data: Decide on methods for handling missing or incomplete data.

Standardization

Units of Measurement: Standardize units for numerical variables. Date and Time Formats: Ensure consistency in date and time formats.

Data Transformation

Variable Recoding: Combine or reclassify categories for analysis. Aggregation: Aggregate data at different levels if needed.

Data Security and Privacy

Anonymization: Protect sensitive information by anonymizing or aggregating data. Access Controls: Restrict access to sensitive data based on roles.

Documentation

Record Changes: Document any changes made to the dataset. Version Control: Implement version control for datasets to track revisions.