- "Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability."
Methods and techniques of analyzing data including descriptive and inferential statistics, as well as qualitative analysis methods.
Introduction to Qualitative Data Analysis: Understanding the meaning and scope of qualitative data analysis, fundamental concepts, and differences between quantitative and qualitative data analysis approaches.
Data Collection Methods: Understanding different data collection methods such as interviews, focus groups, observation, and document analysis.
Data Coding and Categorizing: Understanding how to code and categorize qualitative data, identifying themes and patterns, and developing frameworks and models for analysis.
Data Transcription: Understanding the process of converting audio and video recordings into a written format.
Data Validation and Reliability: Understanding the steps to ensure the validity and reliability of qualitative data through triangulation, member checking, and other qualitative research techniques.
Qualitative Data Analysis Tools: Understanding the different types of software available for qualitative data analysis including NVivo, ATLAS.ti, and MAXQDA.
Introduction to Quantitative Data Analysis: Understanding the meaning and scope of quantitative data analysis, fundamental concepts such as measurement, variable, and data types, and differences between qualitative and quantitative data analysis approaches.
Descriptive Statistics: Understanding the basic descriptive statistics such as mean, mode, median, range, standard deviation, and variance, and their significance in quantitative data analysis.
Inferential Statistics: Understanding the basic inferential statistics such as hypothesis testing, correlation analysis, regression analysis, and analysis of variance (ANOVA), and their significance in quantitative data analysis.
Sampling Techniques: Understanding different sampling techniques such as simple random sampling, stratified random sampling, and cluster sampling, and their significance in quantitative data analysis.
Data Visualization: Understanding different data visualization techniques such as charts, graphs, and tables, and their significance in quantitative data analysis.
Quantitative Data Analysis Tools: Understanding the different types of software available for quantitative data analysis including SPSS, Stata, and SAS.
Introduction to Assessment and Evaluation: Understanding the meaning and scope of assessment and evaluation in education, fundamental concepts, and differences between assessment and evaluation.
Types of Assessment and Evaluation: Understanding different types of assessment and evaluation such as formative assessment, summative assessment, diagnostic assessment, and evaluative assessment.
Assessment and Evaluation Methods: Understanding different assessment and evaluation methods such as tests, quizzes, observations, case studies, and portfolios, and their significance in education.
Reliability and Validity in Assessment and Evaluation: Understanding the steps to ensure the reliability and validity of assessment and evaluation instruments through psychometric analysis, inter-rater reliability, and triangulation.
Data Collection and Analysis Techniques in Assessment and Evaluation: Understanding the different data collection and analysis techniques for assessment and evaluation such as surveys, interviews, focus groups, and statistical analysis.
Use of Technology in Assessment and Evaluation: Understanding the role of technology in assessment and evaluation such as online assessments, e-portfolios, and learning analytics.
Content Analysis: A method of examining written, spoken or visual communication in detail, to identify patterns and themes in the data.
Narrative Analysis: Examines the structure and meaning conveyed by stories or narratives, used to explore how people make sense of experiences.
Discourse Analysis: Analyses the language used in communication, examining the underlying assumptions, power relationships and social context.
Grounded Theory: Systematic inductive method of discovering and developing a theory from qualitative data, used to build explanations for social phenomena.
Ethnography: Immersion in a social setting to understand the dynamics and social patterns at play, used to describe cultural phenomena and social norms.
Descriptive Analysis: Used to summarise and describe the key features and characteristics of a dataset, which could include measures such as mean, mode, median and standard deviation.
Inferential Analysis: Involves using statistical techniques to make inferences about a population based on a sample, used to determine the level of confidence in the reliability of results.
Correlational Analysis: Examines the relationship between two or more variables, and whether they tend to vary together.
Regression Analysis: Examines how the changes in one variable affect changes in another, and can be used for predicting outcomes based on the relationships between the variables.
Factor Analysis: A statistical technique used to identify underlying factors or dimensions within a more complex set of data, to organise and summarise the data based on common characteristics.
- "Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
- "It is assumed that the observed data set is sampled from a larger population."
- "Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "In this context, inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."
- "Inferential statistical analysis infers properties of a population, while descriptive statistical analysis is solely concerned with properties of the observed data."
- "Inferential statistical analysis infers properties of a population, for example by... deriving estimates."
- "It is assumed that the observed data set is sampled from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "Inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."
- "Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
- "Descriptive statistics is solely concerned with properties of the observed data."
- "Descriptive statistics... does not rest on the assumption that the data come from a larger population."
- "It is assumed that the observed data set is sampled from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "Inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."