The Nonparametric Approach in Elementary Statistics

1974 ◽  
Vol 67 (2) ◽  
pp. 123-126
Author(s):  
Gottfried E. Noether
Keyword(s):  
Social Sciences ◽  
Great Majority ◽  
Graduation Requirements ◽  
Introductory Statistics ◽  
Nonparametric Approach ◽  
The Social ◽  
Statistics Course ◽  
Statistics Courses ◽  
Carry Over ◽  
Elementary Statistics

Introductory statistics courses are taken each year by hundreds of thousands of students across the country. These students come from many fields: the life sciences, humanities, education, agriculture, business, but above all from the social sciences. They rarely take statistics voluntarily. They sign up for the course because of departmental or graduation requirements. The great majority has minimal preparation in mathematics, rarely more than they bring along from high school. They carry over into statistics their prejudices of mathematics and quite often, justifiably so. Teachers of statistics courses should then ask themselves how they can make the introductory statistics course statistically meaningful and not simply an exercise in mathematics or, what may even be worse, a meaningless compendium of statistical techniques.

Teaching Data Reproducibility through Service Learning

2019 ◽  
Vol 47 (4) ◽  
pp. 350-357
Author(s):  
Anne M. Nurse ◽  
Trish Staiger
Keyword(s):  
Social Sciences ◽  
Service Learning ◽  
Quantitative Data ◽  
Central Mechanism ◽  
Learning Activity ◽  
Introductory Statistics ◽  
The Social ◽  
Statistics Course ◽  
Data Reproducibility

Data reproducibility is becoming increasingly important in the social sciences, but it has yet to be incorporated into many undergraduate sociology programs. This note describes a service-learning activity that can be added to an introductory statistics course. Students partner with a nonprofit and analyze quantitative data to answer questions selected by the agency. Reproducibility is the central mechanism of communication between the nonprofit, the students, and the course instructor. An assessment of the project suggests that students achieve an understanding of how to create reproducible data. They also come to see its value as a method of communication about data decisions.

scholarly journals A Mixed Method Investigation of Social Science Graduate Students’ Statistics Anxiety Conditions Before and After the Introductory Statistics Course

2018 ◽  
Vol 7 (3) ◽  
pp. 156
Author(s):  
Liuli Huang
Keyword(s):  
Social Sciences ◽  
Graduate Students ◽  
Research Method ◽  
Introductory Statistics ◽  
Statistics Anxiety ◽  
The Social ◽  
Statistics Course ◽  
Level Statistics ◽  
Before And After ◽  
Anxiety Levels

Research frequently uses quantitative approach to explore undergraduates’ statistics anxiety conditions. However, few studies of adults’ statistics anxiety use qualitative method or focus solely on graduate students. Moreover, even less studies focus on comparing adults’ anxiety levels before and after the introductory statistics course. This line of study is important to pursue since the introductory statistics course should play the very important roles of both preparing students’ the foundation knowledge of higher level statistics course, and inspiring students’ interests for higher level course. In addition, graduate students tend to have different backgrounds, learning motivations, and learning habits compared to their undergraduate counterparts. Overall, limited mixed research method is available on social sciences graduate students’ (1) statistics anxiety before and after the introductory statistics course and (2) actions taken to decrease the anxiety. This study seeks to fill this gap by incorporating a mixed research method to explore social sciences graduate students’ statistics learning processes. Findings suggest that the social sciences graduate students’ anxiety levels diminished after the introductory statistics course, even though they also experienced severe statistics anxiety at the very beginning. These findings became essential for institutions, higher education instructors, and social sciences statistics learners to consider.

“All Models Are Wrong”

PMLA ◽  
2017 ◽  
Vol 132 (3) ◽  
pp. 668-673 ◽  
Author(s):  
Richard Jean So
Keyword(s):  
Social Sciences ◽  
Statistical Models ◽  
Test Score ◽  
Quantitative Model ◽  
Introductory Statistics ◽  
Social Scientists ◽  
Test Taking ◽  
The Social ◽  
Math Test ◽  
Statistical Graphic

Several years ago, the first thing i learned in my introductory statistics class was the following declaration, which the instructor had written in capital letters on the blackboard: “all models are wrong.” Models are statistical, graphic, or physical objects, and their primary quality is that they can be manipulated. Scientists and social scientists use them to think about the social or natural worlds and to represent those worlds in a simplified manner. Statistical models, which dominate the social sciences, particularly in economics, are typically equations with response and predictor variables. Specifically, a researcher seeks to understand some social phenomenon, such as the relation between students' scores on a math test and how many hours the students spent preparing for the exam. To predict or describe this relation, the researcher constructs a quantitative model with quantitative inputs (the number of hours each student spent studying) and outputs (each student's test score). The researcher hopes that the number of hours a student spent preparing for the exam will correlate with the student's score. If it does, this quantified relation can help describe the overall dynamics of test taking.

Introduction

1980 ◽  
Vol 25 (S1) ◽  
pp. vii-xxii
Author(s):  
J. K. Chambers
Keyword(s):  
Social Sciences ◽  
First Generation ◽  
Graduate Programs ◽  
Great Majority ◽  
National Consciousness ◽  
Third Generation ◽  
Canadian Universities ◽  
The Third ◽  
The Social ◽  
The Way

Nature leads the way. Man emerges on the scene, follows her footprints, marks and registers them in language, and makes a Science of Nature. Then he looks back and discovers that Language, while following the path of Nature, has left a trail of her own. He returns on this new trail, again marks and registers its footprints, and makes a Science of Language.The Birth of Language (1937)The great majority of linguists in Canada today belong to only the second academic generation of linguists in Canadian universities. Members of the first generation are, of course, still active—in some cases more active than the younger members of their departments. They are characterized, roughly, as founding members of the Canadian Linguistic Association, or as members of long standing. They are also characterized in a few cases as having been the teachers of junior members of the profession, although this is less often the case than it is in other disciplines, partly because there have been very few graduate programs in Linguistics until recently, and partly because there has been little demand for linguists trained in the specialty of the first generation anyway, which is almost unanimously dialect geography, and partly because there has been a decided tendency toward hiring non-Canadians in the social sciences to fill positions in Canadian universities. Now, with the increase in graduate programs in Linguistics, the more diverse specializations, and the national consciousness that Canadian universities can also be served by Canadians, the third generation of linguists will increasingly be selected from the students of the present academic generation, which is how academic generations have been gauged in other cultures for centuries.

Introduction to Statistics: A Nonparametric Approach for the Social Sciences

Technometrics ◽  
1980 ◽  
Vol 22 (3) ◽  
pp. 445
Author(s):  
John E. Boyer ◽  
Chris Leach
Keyword(s):  
Social Sciences ◽  
Nonparametric Approach ◽  
The Social

COMPARISON OF FUTURE COURSE ENROLLMENT AMONG STUDENTS COMPLETING ONE OF FOUR DIFFERENT INTRODUCTORY STATISTICS COURSES

2021 ◽  
Vol 19 (3) ◽  
Author(s):  
VALERIE NAZZARO ◽  
JENNIFER ROSE ◽  
LISA DIERKER
Keyword(s):  
Education Research ◽  
Statistics Education ◽  
Introductory Statistics ◽  
Course Enrollment ◽  
Research Journal ◽  
Statistics Course ◽  
Potential Impact ◽  
Statistics Courses ◽  
Subsequent Selection ◽  
Selection Of

A central challenge of introductory statistics is the development of curricula that not only serve diverse students, but also leave them wanting more. To evaluate the potential impact of a multidisciplinary, project-based introductory statistics course, students’ future course decisions were compared against traditional statistics courses using administrative data from the fall 2009 through spring 2018 semesters. Results indicated that the project-based course helped promote continued interest in the field of statistics and data analysis based on subsequent selection of courses in the field. First published December 2020 at Statistics Education Research Journal: Archives

Teaching Statistics

2017 ◽  
Author(s):  
Andrew Gelman ◽  
Deborah Nolan
Keyword(s):  
Social Sciences ◽  
Data Science ◽  
Teaching Experience ◽  
Introductory Statistics ◽  
Social Scientists ◽  
What Works ◽  
Years Of Teaching Experience ◽  
Statistics Course ◽  
Set Up ◽  
Selection Of

Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, activities, examples and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and has chapters such as ‘First week of class’- with exercises to break the ice and get students talking; then descriptive statistics, graphics, linear regression, data collection (sampling and experimentation), probability, inference, and statistical communication. Part II gives tips on what works and what doesn’t, how to set up effective demonstrations, how to encourage students to participate in class and to work effectively in group projects. Course plans for introductory statistics, statistics for social scientists, and communication and graphics are provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics, sampling, and data science.

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