Posted on June 14th, 2017 by Meghan Rainsberry
Confounding occurs where the relationship between independent and dependent variables is distorted by one or more additional, and sometimes unmeasured, variables. A confounding variable must be associated with both the independent and dependent variables but must not be an intermediate step in the relationship between the two (i.e. not on the causal pathway).
For example, we know that physical exercise (an independent variable) can reduce a person’s risk of cardiovascular disease (a dependent variable). We can say that age is a confounder of that relationship as it is associated with, but not caused by, physical activity and is also associated with coronary health. See also ‘unobserved heterogeneity’, below.
Posted in |
Comments Off on Confounding
Posted on September 24th, 2020 by Jennie Blows
A continuous variable is a variable that has an infinite number of uncountable values e.g. time, temperature. They are also known as quantitative variables or scale variables.
Posted in |
Comments Off on Continuous variable
Posted on September 24th, 2020 by Jennie Blows
Cohort effects relates to changes in an outcome associated with being a member of a specific cohort of people (e.g. born in the same year; or starting school at the same time).
Posted in |
Comments Off on Cohort effects
Posted on September 24th, 2020 by Jennie Blows
In metadata management, coverage refers to the temporal, spatial and topical aspects of the data collection to describe the comprehensiveness of a dataset. For longitudinal studies, this can relate to the topics that are covered across waves, the population to which one can generalise or the geographic extent of the dataset.
Posted in |
Comments Off on Coverage
Posted on July 24th, 2016 by ninjasforhire
Cross-sectional surveys involve interviewing a fresh sample of people each time they are carried out. Some cross-sectional studies are repeated regularly and can include a large number of repeat questions (questions asked on each survey round).
Posted in |
Comments Off on Cross-sectional
Posted on September 24th, 2020 by Jennie Blows
Within the context of data protection, a data access agreement specifies the terms under which users are provided access to specified datasets. This usually forms part of the application process to the data controller to ensure that researchers adhere to a set of terms regarding data confidentiality, sensitivity and dissemination before accessing the data. See also: research ethics
Posted in |
Comments Off on Data access agreement
Posted on September 24th, 2020 by Jennie Blows
Data cleaning is an important preliminary step in the data analysis process and involves preparing a dataset so that it can be correctly analysed. ‘Cleaning’ the data usually involves identifying data input errors, assessing the completeness of the dataset and verifying any anomalies (e.g. outliers).
Posted in |
Comments Off on Data cleaning
Posted on September 24th, 2020 by Jennie Blows
Within the context of data protection, data confidentiality is the process of protecting participants’ data from being accessed or disclosed by those unauthorised to do so. Key methods employed in data confidentiality include anonymisation of responses (removal of personal identifying information) and data encryption (protecting the data using codes and/or passwords).
Posted in |
Comments Off on Data confidentiality
Posted on July 24th, 2016 by ninjasforhire
Data harmonisation involves retrospectively adjusting data collected by different surveys to make it possible to compare the data that was collected. This enables researchers to make comparisons both within and across studies. Repeating the same longitudinal analysis across a number of studies allows researchers to test whether results are consistent across studies, or differ in response to changing social conditions.
Posted in |
Comments Off on Data harmonisation
Posted on December 4th, 2018 by Ryan Bradshaw
Data imputation is a technique for replacing missing data with an alternative estimate. There are a number of different approaches, including mean substitution and model-based multivariate approaches.
Posted in |
Comments Off on Data imputation