Facets of research, problem areas, research techniques and experiments. Each student develops a research project relating to a chosen topic.

About the statistics competency prerequisite
The program expects students entering the program to have achieved master’s level competency in statistics. This is a prerequisite for enrollment in Quantitative Research Methods (16:194:604), a program core methods course option for all students. Competency in statistics will be assessed by the instructor for 604 in the semester prior to enrollment. Students who have not successfully completed graduate level coursework in statistics, or feel unsure about their statistical competency, are strongly encouraged to enroll in a master’s level statistics course as soon as possible. Credits earned in elementary master’s level statistics do not count toward the program’s course work credit requirements. Possible courses at Rutgers include (but are not restricted to) 17:610:511 - Research Methods (in the Master of Information program) and 16:960:532: Statistical Methods in Education (in the Graduate School of Education).

Assessment for statistics competency
Faculty assessing preparation for 16:194:604 will generally ask students to provide the following evidence of competency, among possible others:

Levels of Measurement

Provide brief definitions and examples of nominal, ordinal, interval, and ratio levels of measurement. Possible elaborations include increasing statistical sensitivity, tests of association for nominal and interval/ratio data, and issues in making continuous measures discrete.

Measures of Central Tendency (mean, median, mode)

Provide brief definitions of each, know with what types of data each would be used, what it means when they are all similar, what it signifies when they differ, and why these measures are important descriptors.

Measures of Variance

Define standard deviation, evaluate any frequency distribution in terms of its standard deviations, compare the standard deviation to standard error, and determine the range and variance of a sample.

Variables

Identify independent/predictor, confounding, moderator/intervening, and dependent/criterion variables. Understand appropriate use of the different terms.

Sampling

Understand the difference between probability and nonprobability sampling, samples versus populations, parametric versus nonparametric distributions, types of sampling, assumptions of normal distributions, other types of distributions (e.g., poisson, t, chi-square, etc.)

Error

Understand Type I and Type II errors, sampling and measurement error.

Tests of Association

Understand cross-tabulations and chi-square analyses, t -tests, analyses of variance, and different kinds of correlations.

Significance

Be familiar with p values, degrees of freedom, sample size, relationship of p values to alpha, choosing significance levels, and the relationships among statistical power, significance levels, generality/generalizability, and sample size. Be able to look up critical values on t, chi-square, or normal distribution tables.

Z-scores

Define standard normal curve, standard scores, know formula and appropriate uses