Big Data
Big data refers to extremely large and complex datasets that exceed the processing capabilities of conventional data management tools and require advanced computational methods for storage, processing, and analysis.
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Categorical data are observations that represent distinct categories or groups rather than measured quantities, with no meaningful quantitative distance between them, even when ordered.
A common data model (CDM) is a comprehensive framework that includes standardized definitions, data structures, specifications, and operational rules that allow data from different sources to be organized, presented, and analyzed in a uniform manner.
Concurrent validity is a type of criterion validity that evaluates how well a new measurement instrument agrees with an established gold standard when both are administered at the same point in time.
The Content Validity Index (CVI) is a quantitative measure of the extent to which the items on a measurement instrument are relevant to and representative of the construct they are intended to measure.
Continuous data are numerical measurements that can take any value within a defined range and can theoretically be measured to any desired level of precision.
Convergent validity is a component of construct validity that provides evidence that measures of constructs expected to be related are, in fact, correlated in the direction theory predicts.
Criterion validity is the degree to which scores from a measurement instrument correlate with an external criterion, an accepted measure of the same construct or an outcome it should predict.
Cronbach's alpha is a statistical measure of the internal consistency reliability of a set of items in a psychometric instrument, such as a questionnaire, survey, or test.
Crowdsourcing in research is the practice of distributing tasks to a large, often distributed group of people, typically through online platforms, to support activities such as data annotation, image classification, transcription, or distributed problem…
Data anonymization is the process of removing or transforming identifying information in research data so that individual participants cannot be re identified, even by the data holder or by combining the anonymized data with external sources.
Data cleaning is the process of detecting and correcting or removing inaccurate, incomplete, duplicate, or inconsistent entries from a dataset to improve its quality before analysis.
Data collection is the systematic process of gathering information from relevant sources to answer a research question, using methods selected to match the study design and the nature of the variables under investigation.
Data curation encompasses the management, processing, and enrichment of research data to produce well organized, thoroughly documented, and interoperable datasets suitable for analysis, sharing, and long term preservation.
A data dictionary is a centralized reference document that describes the structure, content, format, and meaning of each variable in a dataset.
Data harmonization is the process of combining data from different sources and reorganizing them according to a unified schema so that the resulting dataset is internally consistent, comparable across its component sources, and suitable for pooled analysis.
Data linkage is the process of combining records from two or more separately maintained data sources that relate to the same individual, event, or entity.
Data privacy in research refers to the right of participants to control how their personal information is collected, stored, used, and shared within and beyond the research context.
Data stewardship is the responsible management and governance of research data throughout its entire lifecycle, from planning and collection through documentation, storage, analysis, sharing, and long term preservation.
In research, a database is a structured collection of records or data organized for efficient search, retrieval, and management.
Discriminant validity is a form of construct validity that provides evidence that measures of theoretically distinct constructs are empirically distinguishable from one another.
Indigenous data sovereignty is the principle that Indigenous peoples have the inherent right to govern the collection, ownership, access, and application of data about their communities, lands, cultures, and resources.
Inter rater reliability is the degree of agreement between two or more independent assessors who evaluate, rate, or classify the same set of items, participants, or data.
The intraclass correlation coefficient (ICC) is a measure of the degree of agreement or consistency among measurements made by multiple raters, across repeated measures of the same quantity, or between observations nested within groups.
A large language model (LLM) is a type of artificial intelligence system built on deep neural network architectures, typically transformers, trained on vast corpora of text data to generate, summarize, translate, and analyze natural language.
Level of measurement refers to the classification of variables into one of four hierarchical scales: nominal, ordinal, interval, and ratio.
A Likert scale is a rating format commonly used in surveys and questionnaires that asks respondents to indicate their level of agreement, satisfaction, frequency, or intensity in response to a statement, typically using a five or seven point scale with…
Reliability is the degree to which a measurement instrument yields consistent, stable, and reproducible results when administered under the same conditions. It reflects the extent to which observed scores are free from random measurement error.
Response rate is the proportion of individuals who complete a survey, questionnaire, or study invitation out of the number of eligible individuals in the sample.