NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 8 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Jopke, Nikolaus; Gerrits, Lasse – International Journal of Social Research Methodology, 2019
There is a need to improve the ways in which Qualitative Comparative Analysis (QCA) handles qualitative data. To this end, we propose to include ideas and routines from Grounded Theory (GT) in QCA. We will first argue that there is a natural fit between the two on the ontological level. On the methodological level, we will demonstrate in what ways…
Descriptors: Qualitative Research, Comparative Analysis, Grounded Theory, Sampling
Peer reviewed Peer reviewed
Direct linkDirect link
Bennett, Kimberley Ann – Teaching Statistics: An International Journal for Teachers, 2015
Students may need explicit training in informal statistical reasoning in order to design experiments or use formal statistical tests effectively. By using scientific scandals and media misinterpretation, we can explore the need for good experimental design in an informal way. This article describes the use of a paper that reviews the measles mumps…
Descriptors: Statistical Analysis, Thinking Skills, Research Design, Data Interpretation
Peer reviewed Peer reviewed
Direct linkDirect link
Thissen, David – Measurement: Interdisciplinary Research and Perspectives, 2015
In "Using Learning Progressions to Design Vertical Scales that Support Coherent Inferences about Student Growth" (hereafter ULR), Briggs and Peck suggest that learning progressions could be used as the basis of vertical scales with naturally benchmarked descriptions of student proficiency. They propose and provide a single example of a…
Descriptors: Academic Achievement, Achievement Gains, Achievement Rating, Psychometrics
Peer reviewed Peer reviewed
Direct linkDirect link
Hanushek, Eric A.; Warren, John Robert; Grodsky, Eric – Educational Policy, 2012
This exchange represents a follow-up to an article on the effects of state high school exit examinations that previously appeared in this journal (Warren, Grodsky, & Kalogrides 2009). That 2009 article was featured prominently in a report by the National Research Council (NRC) that evaluated the efficacy of test-based accountability systems.…
Descriptors: High School Seniors, High Schools, Exit Examinations, Context Effect
Peer reviewed Peer reviewed
Direct linkDirect link
Zientek, Linda Reichwein; Ozel, Z. Ebrar Yetkiner; Ozel, Serkan; Allen, Jeff – Career and Technical Education Research, 2012
Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…
Descriptors: Vocational Education, Effect Size, Intervals, Self Esteem
Peer reviewed Peer reviewed
Direct linkDirect link
Pruzek, Robert M.; Helmreich, James E. – Journal of Statistics Education, 2009
A standard topic in many Introductory Statistics courses is the analysis of dependent samples. A simple graphical approach that is particularly relevant to dependent sample comparisons is presented, illustrated and discussed in the context of analyzing five real data sets. Each data set to be presented has been published in a textbook, usually…
Descriptors: Statistics, Introductory Courses, Sampling, Data Analysis
Peer reviewed Peer reviewed
Anderson, Margo; Fienberg, Stephen E. – Society, 1997
Describes the role and function of the census and discusses census taking and decision making about "counting" from two perspectives: the Supreme Court decision in Wisconsin vs New York, and the Census Bureau's current plans for the year 2000. Concluding comments explore the lessons learned from the 1990 census and the effect on the 2000…
Descriptors: Computation, Court Litigation, Data Interpretation, Planning
Peer reviewed Peer reviewed
Barbella, Peter; And Others – Mathematics Teacher, 1990
Demonstrates a statistically valid method allowing students to explore randomization. Described are two examples: counting techniques for a small set of data and simulation for a large sample. (YP)
Descriptors: Data Analysis, Data Interpretation, Mathematical Concepts, Mathematical Logic