Child Care and Early Education Research Connections

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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.

A B C D E F G H I J K L M N O P Q R S T U V W Z
Main Effect
The effect of a predictor (or independent) variable on an outcome (or dependent) variable.
MANOVA (Multivariate Analysis of Variance)
MANOVA is an extension of analysis of variance (ANOVA). ANOVA is used to examine whether there are statistical differences in the group means for a single continuous dependent variable. MANOVA is used when there are two or more dependent variables (outcome variables). It is a statistical test that measures group differences on several dependent variables.
Markov Chain Monte Carlo Methods
Markov chain Monte Carlo (MCMC) methods are used to estimate the properties of a distribution by examining random samples from the distribution. Researchers using a Monte Carlo approach draw a large number of random samples from a normal distribution, and calculate the sample mean of those. The random samples are generated by a special sequential process. Each random sample is used as a stepping stone to generate the next random sample (hence the chain). A special property of the chain is that, while each new sample depends on the one before it, new samples do not depend on any samples before the previous one (this is the "Markov" property).
Matched Samples
Two samples in which the members are paired or matched explicitly by the researcher on specific attributes, such as IQ or income. Also refers to samples in which the same attribute or variable is measured twice on each subject under different circumstances; also referred to as repeated measures.
Maxima
The maxima are points where the value of a function is greater than other surrounding points.
Maximum Likelihood Estimation
Maximum-likelihood estimation (MLE) is one of the most widely used methods for estimating the parameters of a statistical model (for example, means and variances) from sample data. Using the sample data, MLE obtains estimates of the population parameters such that the probability (likelihood) of obtaining the observed data is maximized.
Mean
A descriptive statistic used as a measure of central tendency. To calculate the mean, all the values of a variable are added and then the sum is divided by the number of values. For example, if the age of the respondents in a sample were 21, 35, 40, 46, and 76, the mean age of the sample would be (21+35+40+46+76)/5 = 43.6
Measurement Error
The difference between the value measured in a survey or on a test and the "true: value, if the difference is due to factors beyond the control of the respondent. Some factors that contribute to measurement error include the environment in which a survey or test is administered (e.g., administering a math test in a noisy classroom could lead students to do poorly even though they understand the material), poor measurement tools (e.g., using a ruler that is only marked in feet to measure height would lead to inaccurate measurement), rater effects (e.g., if a police man in uniform conducted interviews with individuals about drug use, they might not feel comfortable revealing their drug use). There are many more such factors that can contribute to measurement error.
Measures of Association
Statistics that measure the strength and nature of the relationship between variables. For example, correlation is a measure of association
Median
A descriptive statistic used to measure central tendency. The median is the value that is the middle value of a set of values. 50% of the values lie above the median, and 50% lie below the median. For example, if a sample of individuals are ages 21, 34, 46, 55, and 76 the median age is 46.