scholarly journals A RESPONSE TO WHITE AND GORARD: AGAINST INFERENTIAL STATISTICS: HOW AND WHY CURRENT STATISTICS TEACHING GETS IT WRONG

2017 ◽  
Vol 16 (1) ◽  
pp. 66-73
Author(s):  
JAMES NICHOLSON ◽  
JIM RIDGWAY
Keyword(s):  
Education Research ◽  
Social Science ◽  
Statistics Education ◽  
Statistical Tests ◽  
Social Science Research ◽  
Science Research ◽  
Inferential Statistics ◽  
P Values ◽  
Logical Basis ◽  
Research Journal

White and Gorard make important and relevant criticisms of some of the methods commonly used in social science research, but go further by criticising the logical basis for inferential statistical tests. This paper comments briefly on matters we broadly agree on with them and more fully on matters where we disagree. We agree that too little attention is paid to the assumptions underlying inferential statistical tests, to the design of studies, and that p-values are often misinterpreted. We show why we believe their argument concerning the logic of inferential statistical tests is flawed, and how White and Gorard misrepresent the protocols of inferential statistical tests, and make brief suggestions for rebalancing the statistics curriculum. First published May 2017 at Statistics Education Research Journal Archives

scholarly journals AGAINST INFERENTIAL STATISTICS: HOW AND WHY CURRENT STATISTICS TEACHING GETS IT WRONG

2017 ◽  
Vol 16 (1) ◽  
pp. 55-65
Author(s):  
PATRICK WHITE ◽  
STEPHEN GORARD
Keyword(s):  
Education Research ◽  
Statistical Analysis ◽  
Statistics Education ◽  
Statistical Tests ◽  
Standard Errors ◽  
Science Students ◽  
P Values ◽  
Logical Problem ◽  
Research Journal ◽  
Business As Usual

Recent concerns about a shortage of capacity for statistical and numerical analysis skills among social science students and researchers have prompted a range of initiatives aiming to improve teaching in this area. However, these projects have rarely re-evaluated the content of what is taught to students and have instead focussed primarily on delivery. The emphasis has generally been on increased use of complex techniques, specialist software and, most importantly in the context of this paper, a continued focus on inferential statistical tests, often at the expense of other types of analysis. We argue that this ‘business as usual’ approach to the content of statistics teaching is problematic for several reasons. First, the assumptions underlying inferential statistical tests are rarely met, meaning that students are being taught analyses that should only be used very rarely. Secondly, all of the most common outputs of inferential statistical tests – p-values, standard errors and confidence intervals – suffer from a similar logical problem that renders them at best useless and at worst misleading. Eliminating inferential statistical tests from statistics teaching (and practice) would avoid the creation of another generation of researchers who either do not understand, or knowingly misuse, these techniques. It would also have the benefit of removing one of the key barriers to students’ understanding of statistical analysis. First published May 2017 at Statistics Education Research Journal Archives

scholarly journals Challenges in Education Research in Taiwan: Research Institutes and Organizations, Research Policies, and Problems

2017 ◽  
Vol 7 (1&2) ◽  
Author(s):  
Jia Li Huang
Keyword(s):  
Education Research ◽  
Social Science ◽  
Research Policy ◽  
Research Performance ◽  
Social Science Research ◽  
Academic Research ◽  
Science Research ◽  
Academic Education ◽  
Research Institutes

Since the 1990s, many education researchers and policy makers worldwide have reviewed education research to attempt to provide strategies to improve the quality of such research in their countries. Taiwan’s government has launched policies and funded support to set the benchmark for Taiwan’s leading universities in international academic competition. The external environment of global competition based on research policy influences the ecosystem of social science research production. To assure the quality of education policy, peer review from within the education community is one approach to supplementing the government’s governance, including the establishment of research institutes, promotion, rewards, and research value. This study tracked the mode of academic research and provides an overview of the status of academic education research in Taiwan. Because education research is part of the humanities and social sciences fields, this study identified the challenges in educational research by examining the trend of social science research and by analyzing research organizations, policy, and the evaluation of research performance. Due to the environment of education research in Taiwan is not friendly to education researcher to accumulate papers in SSCI or international journal, additional concerns entail how education research communities can develop and agree on its quality.

scholarly journals STUDENTS’ REASONING ABOUT p-VALUES

2015 ◽  
Vol 14 (2) ◽  
pp. 7-27
Author(s):  
BIRGIT C. AQUILONIUS ◽  
MARY E. BRENNER
Keyword(s):  
College Students ◽  
Community College ◽  
Education Research ◽  
Hypothesis Testing ◽  
Statistics Education ◽  
Research Question ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
P Values ◽  
Research Journal

Results from a study of 16 community college students are presented. The research question concerned how students reasoned about p-values. Students' approach to p-values in hypothesis testing was procedural. Students viewed p-values as something that one compares to alpha values in order to arrive at an answer and did not attach much meaning to p-values as an independent concept. Therefore it is not surprising that students often were puzzled over how to translate their statistical answer to an answer of the question asked in the problem. Some reflections on how instruction in statistical hypothesis testing can be improved are given. First published November 2015 at Statistics Education Research Journal Archives

scholarly journals STILL AGAINST INFERENTIAL STATISTICS: REJOINDER TO NICHOLSON AND RIDGWAY

2017 ◽  
Vol 16 (1) ◽  
pp. 74-79
Author(s):  
STEPHEN GORARD ◽  
PATRICK WHITE
Keyword(s):  
Education Research ◽  
Statistics Education ◽  
Inferential Statistics ◽  
Statistics Education Research ◽  
Research Journal

In their response to our paper, Nicholson and Ridgway agree with the majority of what we wrote. They echo our concerns about the misuse of inferential statistics and NHST in particular. Very little of their response explicitly challenges the points we made but where it does their defence of the use of inferential techniques does not stand up to scrutiny. Their statements are either contradictory, agreement ‘dressed up’ as disagreement, appeals to authority, semantic slights of hand, or irrelevant to our original claims. It is not clear why such a response was needed. First published May 2017 at Statistics Education Research Journal Archives

scholarly journals INTERACTIVE VISUALISATIONS AND STATISTICAL LITERACY

2017 ◽  
Vol 16 (1) ◽  
pp. 26-30
Author(s):  
SINCLAIR SUTHERLAND ◽  
JIM RIDGWAY
Keyword(s):  
Education Research ◽  
Data Analysis ◽  
Social Science ◽  
Statistics Education ◽  
Statistical Literacy ◽  
Statistics Education Research ◽  
Research Journal ◽  
Interactive Data ◽  
The 1960S

Statistical literacy involves engagement with the data one encounters. New forms of data and new ways to engage with data – notably via interactive data visualisations – are emerging. Some of the skills required to work effectively with these new visualisation tools are described. We argue that interactive data visualisations will have as profound an effect on statistical literacy as the introduction of statistics packages had on statistics in social science in the 1960s. Current conceptualisations of statistical literacy are too passive, lacking the exploration part in data analysis. Statistical literacy should be conceived of as empowerment to engage effectively with evidence, and educators should seek to move students along a pathway from using interactive data visualisations to building them and interpreting what they see. First published May 2017 at Statistics Education Research Journal Archives

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