The research glossary defines terms used in conducting social science and policy research, for example those describing methods, measurements, statistical procedures, and other aspects of research; the child care glossary defines terms used to describe aspects of child care and early education practice and policy.
Member Checking
During open-ended interviews, the practice of a researcher restating, summarizing, or paraphrasing the information received from a respondent to ensure that what was heard or written down is in fact correct.
Meta-Analysis
A statistical technique that combines and analyzes data across multiple studies on a topic. In early childhood and education research, a meta-analysis combines a number of studies (usually conducted by a number of different researchers in a variety of contexts) to quantify the effect a given independent or treatment variable (e.g., full-day versus part-day kindergarten and class size) has on a given outcome (e.g., children's academic skills and prevalence of positive and negative classroom behavior).
Methodology
The principles, procedures, and strategies of research used in a study for gathering information, analyzing data, and drawing conclusions. There are broad categories of methodology such as qualitative methods or quantitative methods; and there are particular types of methodologies such as survey research, case study, and participant observation, among many others.
Metropolitan Statistical Area (MSA)
A term used by the U.S. Census Bureau to designate an area of adjacent counties (except in New England where they are defined by adjacent cities). Metropolitan Statistical Areas (MSAs) are often used to geographically understand labor markets because individuals often look for work outside of the city or county in which they live.
Micro-Ethnography
Micro-ethnography, also known as ethnographic microanalysis of interaction, describes how interaction is socially and culturally organized in particular situational settings (e.g., classrooms or neighborhoods). It is a process that includes data collection, content analysis, and comparative analysis of everyday situations for the purpose of formulating insights.
Minima
The minima are points where the value of a function is less than other surrounding points.
Missing Completely at Random (MCAR)
The term implies that all respondents are equally likely/unlikely to respond to the item and that the estimate is approximately unbiased. To ignore the missing data and restrict analyses to those records with reported values for the variables in the analysis, implicitly invokes the assumption that the missing cases are a random subsample of the full sample, that is, they are missing completely at random (MCAR). This is a strong assumption.
Missing Data
Values in a data set values that were not recorded. Missing values can have many causes including a respondent's refusal to answer survey questions, an interviewer incorrectly coding a response, or questions that do not apply to a respondent. The more missing data there are in a data set, the greater the likelihood of bias. There are several coding strategies that can "fill in" missing data for statistical analyses. These strategies are called imputation (see Data Imputation).
Missing Data Imputation
A method used to fill in missing values (due to nonresponse) in surveys. The method is based on careful analysis of patterns of missing data. Types of data imputation include mean imputation, multiple imputation, hot deck and cold deck imputation. Data imputation is done to allow for statistical analysis of surveys that were only partially completed.
Misspecification
Misspecification occurs when the predictor (independent) variables in a statistical model are incorrect. The most common cause of model misspecification is that important predictor (independent) variables are left out of the model. Misspecification often leads to incorrect estimates of the effects of the predictor (independent) variables that are included in the model on the outcome (dependent) variable.