Jamie Hale

Jamie Hale

Tuesday, October 25, 2016

Science and Rationality in Modern Society

Science and rationality are important in modern, technologically advanced, industrial societies. Science is a large enterprise consisting of multiple components. Science, although fallible, is the great reality detector. Rationality, in this context, refers to rationality as it is conceptualized in cognitive science. Rationality is concerned with judgment and decision making. Rationality consists of two main categories- instrumental and epistemic. Instrumental rationality reflects goal optimization, and epistemic reflects evidence based beliefs. There is overlap between the two categories of rationality. In my most recent book- In Evidence We Trust: The need for science, rationality & statistics- I provide information on various aspects of science, rationality and mathematical procedures (statistics) used in describing and making inferences in the context of scientific research. 

In Evidence We Trust

 It is often said we live in the information age, but we also live in the mis-information age.  How do we decide what constitutes knowledge and what constitutes nonsense?  Maybe there are no wrong or right answers, and just opinions?  This notion is fallacious.  There are facts and opinions, right and wrong answers.  There is a reality that extends beyond personal comforts and opinions (Mitchell & Jolley, 2010).  In the context of science  facts are tentative.  They are assertions that are supported by the preponderance of evidence.  Facts in the context of science (primary concern in this book) are based on levels of certainty, but absolute certainty is never attained.  Scientific findings are presented in terms of probabilities and data (e.g. laws, principles, theories, etc.) is revised in accordance to findings.

Testimonials, anecdotes, they-says, wishful thinking and so on do not count for evidence.  If  these types of claims and feelings are labeled  as evidence then any discussion of evidence is vacuous.  Testimonials exist for almost any claim you can imagine.  That does not mean that claims of this sort have no value.    Experiences are confounded (confused by alternative explanations). Experiences may be very important in some contexts, and they may serve as meaningful research questions.  However, a meaningful question or a possible future finding is not synonymous with evidence. Scientific evidence is drastically different than evidence as it relates to everyday discourse.  As Joy Victoria points out- it should be obvious from the book's title that the type of evidence I am referring to in the book is derived from scientific findings (paraphrased). 
The content in chapter one includes short-articles (old, new & revised), a science discussion roundtable (featuring individuals from various fields) and a nonsense detection kit. Some of the short articles presented in chapter one have been published on various internet sites, and some of the same or similar information may be discussed in across different articles.  There are at least two key benefits that can occur when presenting similar information across different articles (in different contexts): strengthening of memory connections, and each article can be read as a stand-alone article.  In the science discussion roundtable participants are asked two questions.  One) Do you have any tips for people that are interested in enhancing their ability to read scientific research?  Two) What is the biggest (or at least one of the biggest misconceptions) misconception about science? The Nonsense Detection Kit is presented at the end of chapter one.  The impetus for designing the Nonsense Detection Kit was similar kits devised by Sagan, Shermer, and Lilienfeld.

Chapter two features short articles on rationality.  Some of the same or similar information is contained across different articles.  There are at least a couple of advantages to presenting information in this manner (refer to previously mentioned advantages in chapter one).  Many of the articles focus on the rationality intelligence dichotomy.  Also included in this chapter are interviews with Keith Stanovich and the Stanovich Research Lab (Keith Stanovich, Richard West and Maggie Toplak).  In the interview with Stanovich, he discusses the development of an RQ Test. In the interview with the Stanovich lab, rationality and intelligence are discussed. Since the publication of the book Stanovich, West and Toplak have designed the first comprehensive test for rational thinking

Chapter three features frequently asked questions about research methods and statistics. Many of the questions are questions I have received in the past from my students.  Some of the questions address basic research and statistics problems, while other questions are more complex.  At the end of the chapter recommended sources are provided for readers that are interested in furthering their studies on research methods and statistics. 
The book ends with an appendices section. Practice problems, and guidelines regarding APA citations and reference lists are given.

The content in this book may be difficult for some to comprehend. However, with some effort and patience the content is learnable for most people. In the words of Albert Einstein “Things should be made as simple as possible, but not any simpler.” Science, rationality and statistics can be simplified to a degree, but relative to most other topics these topics are difficult.  This book is not written for cognitive misers (the cognitively lazy).  This book is written for individuals that are interested in separating knowledge and nonsense, and are willing to put forth at least a moderate level of cognitive effort.  This book is not written in the format often used by pop science writers.
I would like to thank Joy Victoria, Kitty Mervine, Jason Silvernail and Coert Visser for the review articles of IEWT they have written. 
RecommendedResources: In Evidence We Trust by Joy Victoria 

In Evidence We Trust (Review) by Coert Visser  

Tuesday, October 11, 2016

More Than Scientific Literacy

Discussions involving scientific literacy are ubiquitous. Scientific literacy is conceptualized and operationalized  in various ways (see; Norris & Phillips, 2002).  Examples used in defining scientific literacy include: understanding science and its applications, knowledge of what counts as science, general scientific knowledge, knowledge of risks and benefits of science, etc. Numerous scales are used to measure scientific literacy.   In my current research scientific literacy is synonymous with general scientific knowledge, that involves various domains.  This form of literacy is sometimes referred to as derived scientific literacy. The various forms of scientific literacy are important, but there are many other relevant science related concepts, that are as important or maybe more important.

What about scientific cognition (thinking)? Scientific cognition is not the same thing as scientific literacy; it involves multiple components and sub-components (Feist, 2006).  Deanna Kuhn asserts that the essence of scientific thinking is coordinating belief with evidence (2001).  At the very least scientific cognition involves philosophy of science, scientific methodology, quantitative reasoning, probabilistic reasoning and elements of logic. Scientific cognition requires specific cognitive abilities and cognitive style (thinking disposition). 
Various scales have been developed to measure scientific thinking / reasoning / cognition.  Kahan developed a scale called the Ordinary Science Intelligence Scale (OSI_2.0, Kahan, 2014).  Drummond and Fischhoff (2015) developed the Scientific Reasoning Scale.  Drummond and Fischhoff found that measures of scientific reasoning were distinct from measures of scientific literacy.  Kevin Dunbar (2000) and Zimmerman (2005) have also conducted research on scientific thinking.  Dunbar's research mostly involves examining cognitive processes underpinning thinking during the research process, while Zimmerman's research is broader, examining various scales, and development patterns of scientific thought.  Fugelsang et al. (2004) have examined strategies that scientists and non-scientists use to evaluate data that is consistent or non-consistent with expectations.
Attitudes about science, predictors of scientific eminence, association between scientific measures, rudimentary knowledge regarding meta-sciences  and group difference relating to scientific concepts are other important topics, that receive less attention than general scientific knowledge.  All of these topics are important!  A comprehensive understanding and appreciation of science and its wide range of implications is a complex task.  
Current Research
Myself and colleagues are developing and modifying instruments for the assessment of scientific cognition and scientific literacy (general scientific knowledge).  We have completed a prototype for each, and we are currently using the instruments in a study examining the relationship between scientific cognition and scientific literacy. Each instrument consists of 14 questions. The scales are derivations from previously used scales. Upon completion of the study we will probably modify the instruments accordingly.  We plan on running a statistical analysis of internal consistency once the measures are complete. 
I am working with a colleague on an additional paper that involves measures of general scientific knowledge, attitudes toward science, relationships between / among various science concepts and group differences regarding science outcomes. This is a relatively long paper that presents a relatively large number of statistics.   
These studies are part of "Project: thinking about science."  We are also in the intermediate stages of the development of a seminar that will encompass information on the vast goals and implications of "Project: thinking about science." 

References are available upon request
You can contact me jamie.hale1@gmail.com if interested in hosting a seminar.