Parallel Design: In randomized trials, a parallel design is one in which subjects are randomly assigned to treatments, which then proceed in parallel with each group. Conducted properly, they provide assurance that any difference between treatments is in fact due to treatment effects (or random chance), rather than some systematic...
View Full DescriptionRepeated Measures Data: Repeated measures (or repeated measurements) data are usually obtained from multiple measurements of a response variable. Such multiple measurements are carried out for each experimental unit over time (as in a longitudinal study ) or under multiple conditions. An essential statistical peculiarity of such data is dependence...
View Full DescriptionResponse: In design of experiments, response is a dependent variable. Its values are measured for all subjects, and the question of primary interest is how factors affect the response. See Variables (in design of experiments) for an explanatory example. Browse Other Glossary Entries
View Full DescriptionSample Size Calculations: Sample size calculations typically arise in significance testing, in the following context: how big a sample size do I need to identify a significant difference of a certain size? The analyst must specify three things: 1) How big a difference is being looked for (also called the...
View Full DescriptionStatistical Glossary Self-Controlled Design: In randomized trials, a self-controlled design is one in which results are measured in each subject before and after treatment. Both parallel designs and crossover designs can also include a self-controlled feature. Browse Other Glossary Entries
View Full DescriptionSequential Analysis: In sequential analysis, decisions about sample size and the type of data to be collected are made and modified as the study proceeds, incorporating information learned at earlier stages. One major application of sequential analysis is in clinical trials in medicine, where successful therapies can often be accepted...
View Full DescriptionStratified Sampling: Stratified sampling is a method of random sampling. In stratified sampling, the population is first divided into homogeneous groups, also called strata. Then, elements from each stratum are selected at random according to one of the two ways: (i) the number of elements drawn from each stratum depends...
View Full DescriptionSystematic Sampling: Systematic sampling is a method of random sampling. The elements to be sampled are selected at a uniform interval that is measured in time, order, or space. Browse Other Glossary Entries
View Full DescriptionTime-series data: See longitudinal data Browse Other Glossary Entries
View Full DescriptionCox Proportional Hazard: See Proportional hazard model Browse Other Glossary Entries
View Full DescriptionFinite Mixture Models: Outside the social research, the term "finite mixture models" is often used as a synonym for "latent class models" in latent class analysis . Browse Other Glossary Entries
View Full DescriptionFixed Effects: The term "fixed effects" (as contrasted with "random effects") is related to how particular coefficients in a model are treated - as fixed or random values. Which approach to choose depends on both the nature of the data and the objective of the study. A fixed effect approach...
View Full DescriptionGeneral Linear Model: General (or generalized) linear models (GLM), in contrast to linear models, allow you to describe both additive and non-additive relationship between a dependent variable and N independent variables. The independent variables in GLM may be continuous as well as discrete. (The dependent variable is often named "response",...
View Full DescriptionGeneral Linear Model for a Latin Square: In design of experiment, a Latin square is a three-factor experiment in which for each pair of factors in any combination of factor values occurs only once. Consider the following Latin Square, B C D A C D A B D A...
View Full DescriptionGLM: See General Linear Model. Browse Other Glossary Entries
View Full DescriptionHierarchical Linear Modeling: Hierarchical linear modeling is an approach to analysis of hierarchical (nested) data - i.e. data represented by categories, sub-categories, ..., individual units (e.g. school -> classroom -> student). At the first stage, we choose a linear model (level 1 model) and fit it to individual units in...
View Full DescriptionHierarchical Loglinear Models: Hierarchical linear modeling is an approach to analysis of hierarchical (nested) data - i.e. data represented by categories, sub-categories, ..., individual units (e.g. school -> classroom -> student). At the first stage, we choose a linear model (level 1 model) and fit it to individual units in...
View Full DescriptionInteraction effect: An interaction effect refers to the role of a variable in an estimated model, and its effect on the dependent variable. A variable that has an interaction effect will have a different effect on the dependent variable, depending on the level of some third variable. For example, increasing...
View Full DescriptionLatent Structure Models: Latent structure models is a generic term for a broad set of categories of statistical models. This set includes factor analysis models, covariance structure models, latent profile analysis models, latent trait analysis models, latent class analysis models, and some others. Each category gives rise to a particular...
View Full DescriptionLatent Variable Models: Latent variable models are a broad subclass of latent structure models . They postulate some relationship between the statistical properties of observable variables (or "manifest variables", or "indicators") and latent variables. A special kind of statistical analysis corresponds to each kind of the latent variable models. According...
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