Literature Index

In British social science degree programmes, methods courses have a bad press, and statistics courses in particular are not well-liked by most students. A nationally coordinated, strategic investment in quantitative skills training, Q-Step, is an attempt to address the issues affecting the shortage of quantitatively trained humanities and social science graduates. Pedagogic approaches to teaching statistics and data analysis to social science students are starting to indicate positive outcomes. This paper contributes to these debates by focusing on the perspective of the student experience in different learning environments: first, we explain the approach taken at the University of Manchester to teaching a core quantitative research methods module for second-year sociology students; and second, we introduce case studies of three undergraduates who took that training and went on to work as interns with social research organisations, as part of a Manchester Q-Step Centre initiative to take learning from the classroom into the workplace.

The increasing popularity of R is leading to an increase in its use in undergraduate courses at universities (R Development Core Team 2008). One of the strengths of R is the flexible graphics provided in its base package. However, students often run up against its limitations, or they find the amount of effort to create an interesting plot may be excessive. The grid package (Murrell 2005) has a wealth of graphical tools which are more accessible to such R users than many people may realize. The purpose of this paper is to highlight the main features of this package and to provide some examples to illustrate how students can have fun with this different form of plotting and to see that it can be used directly in the visualization of data.

An experimental college level course, Functions and Statistics with Computers, was designed using the textbook Functions Statistics and Trigonometry with Computers developed by the University of Chicago School Mathematics Project. A case study of this course and its influence on a more traditional course is described. Students in the experimental course were compared with students in the traditional course based on attitude toward mathematics and achievement in mathematics. Experimental course students showed a significant gain in confidence about learning and performing well in mathematics. Final grade distributions for the experimental and traditional courses were similar, although experimental course students entered the course with somewhat weaker mathematical backgrounds. On a course evaluation document, students in the experimental course reported that computer laboratory activities helped them understand course material. Based on an analysis of the attitude, achievement and course evaluation data, the traditional course was modified to deemphasize algebraic manipulation, emphasize modeling and applications and to include computer laboratory activities.

In the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE), statistical literacy featured as a primary goal. The 2016 revision eliminated statistical literacy as a stated goal. Although this looks like a rejection, this paper argues that by including multivariate thinking and – more importantly – confounding as recommended topics in introductory statistics, statistical literacy has in fact been accepted if not promoted. The adoption of the new guidelines will greatly advance students’ statistical literacy: the ability to read and interpret statistics relevant to consumers and decision makers.