The document summarizes a novel method called Observation Oriented Modeling (OOM) for improving psychological science. OOM aims to address issues with traditional research methods like overreliance on group-level analyses and improper use of null hypothesis significance testing. It uses binary coding of observations and matrix algebra operations to model causal relationships between deep structures in data. Statistics like the Classification Strength Index and Percent Correct Classification evaluate how well a target matrix is conformed by a rotated conforming matrix, indicating causal relationships.
T. Pradeu & M. Lemoine: Philosophy in Science: Definition and Boundariesjemille6
Do philosophers of science frequently contribute to science, and if so how? Bibliometrics helps assess how surprisingly big is the corpus of papers authored or co-authored by philosophers and published in science. Indeed, several hundreds of philosophers have published in scientific journals. It is also possible to assess how influential this work has been in terms of citations, as compared to the average number of citations in the same journals in the same year. Unsurprisingly, many of these papers authored or co-authored by philosophers and published in scientific journals are poorly cited while a handful of them are widely cited. However, the most interesting result is that there is a significant corpus of papers authored by philosophers (both published in science journals and in philosophy journals) and significantly cited in science. It is more difficult, albeit crucial, to identify the most contributive philosophical papers, namely, those which have penetrated science not only through publication or citation in science journals, but also through discussion or endorsement by some scientists.
Based on the identification of this often neglected corpus, which we propose to call "philosophy in science" (PinS), it becomes possible to describe the most central features of this particular way of doing philosophy of science. The first feature is bibliographic: philosophers in science tend to cite little philosophy and a lot of (up-to-date) science. Second, they also address a scientific question rather than a philosophical question. Third, in doing so, they use traditional tools of philosophy of science, typically and mostly, conceptual analysis, explication of implicit claims, examination of the consistency of claims, assessment of the relevance of methods or models. More rarely, but very interestingly, they also make positive and original contributions by bridging domains of science or suggesting hypotheses.
This different context – in particular, the specific requirements for a publication in a peer-reviewed science journal – transforms philosophy of science. Is it still philosophy? What is the difference with approaches such as "philosophy of science in practice", "complementary science", "scientific philosophy", "theory of science", and naturalism? PinS faces a double "impostor syndrome": not entirely philosophical for philosophers, and not entirely scientific for scientists. In conclusion, we will explore how PinS can respond to this double challenge.
What is a theory? What makes a good theory?
We also look at accuracy and precision, historic examples, Karl Popper's ideas of a theories, Occam's razor, the scientific model etc. for an extensive look into the concept of a theory and its place in any discipline.
About your research methodology grounded theory. rica viljoen. eskomDr Rica Viljoen
Presentation made at research workshop of the Da Vinci Institute hosted at Eskom Research Conference. A unique integration of grounded theory and systems thinking are presented.
T. Pradeu & M. Lemoine: Philosophy in Science: Definition and Boundariesjemille6
Do philosophers of science frequently contribute to science, and if so how? Bibliometrics helps assess how surprisingly big is the corpus of papers authored or co-authored by philosophers and published in science. Indeed, several hundreds of philosophers have published in scientific journals. It is also possible to assess how influential this work has been in terms of citations, as compared to the average number of citations in the same journals in the same year. Unsurprisingly, many of these papers authored or co-authored by philosophers and published in scientific journals are poorly cited while a handful of them are widely cited. However, the most interesting result is that there is a significant corpus of papers authored by philosophers (both published in science journals and in philosophy journals) and significantly cited in science. It is more difficult, albeit crucial, to identify the most contributive philosophical papers, namely, those which have penetrated science not only through publication or citation in science journals, but also through discussion or endorsement by some scientists.
Based on the identification of this often neglected corpus, which we propose to call "philosophy in science" (PinS), it becomes possible to describe the most central features of this particular way of doing philosophy of science. The first feature is bibliographic: philosophers in science tend to cite little philosophy and a lot of (up-to-date) science. Second, they also address a scientific question rather than a philosophical question. Third, in doing so, they use traditional tools of philosophy of science, typically and mostly, conceptual analysis, explication of implicit claims, examination of the consistency of claims, assessment of the relevance of methods or models. More rarely, but very interestingly, they also make positive and original contributions by bridging domains of science or suggesting hypotheses.
This different context – in particular, the specific requirements for a publication in a peer-reviewed science journal – transforms philosophy of science. Is it still philosophy? What is the difference with approaches such as "philosophy of science in practice", "complementary science", "scientific philosophy", "theory of science", and naturalism? PinS faces a double "impostor syndrome": not entirely philosophical for philosophers, and not entirely scientific for scientists. In conclusion, we will explore how PinS can respond to this double challenge.
What is a theory? What makes a good theory?
We also look at accuracy and precision, historic examples, Karl Popper's ideas of a theories, Occam's razor, the scientific model etc. for an extensive look into the concept of a theory and its place in any discipline.
About your research methodology grounded theory. rica viljoen. eskomDr Rica Viljoen
Presentation made at research workshop of the Da Vinci Institute hosted at Eskom Research Conference. A unique integration of grounded theory and systems thinking are presented.
Research paradigms : simplifying complex debatesThe Free School
This presentation defines the term ‘research paradigm’ with reference to research conducted mostly within human and social sciences disciplines. It also discusses the dominant research paradigms as theorized by leading scholarly publications in these disciplines. This presentation discusses the alternative systems that may aid the researcher to
choose the most appropriate research paradigm.
Introductory discussion provides historical context that explains the reasons why
the notion of the ‘research paradigm’ remains a confusing topic within the
research methods literature. This ambiguity is a core factor that causes this principle to misunderstood by many early-career researchers.
HCI Research as Problem-Solving [CHI'16, presentation slides] Aalto University
Slides from a talk delivered at CHI 2016, San Jose.
Authors: Antti Oulasvirta (Aalto University) and Kasper Hornbaek (University of Copenhagen).
Link to paper: http://users.comnet.aalto.fi/oulasvir/pubs/hci-research-as-problem-solving-chi2016.pdf
Overview: This talk discusses a meta-scientific account of human-computer interaction (HCI) research as problem-solving. We build on the philosophy of Larry Laudan, who develops problem and solution as the foundational concepts of science. We argue that most HCI research is about three main types of problem: empirical, conceptual, and constructive. We elaborate upon Laudan’s concept of problem-solving capacity as a universal criterion for determining the progress of solutions (outcomes): Instead of asking whether research is ‘valid’ or follows the ‘right’ approach, it urges us to ask how its solutions advance our capacity to solve important problems in human use of computers. This offers a rich, generative, and ‘discipline-free’ view of HCI and resolves some existing debates about what HCI is or should be. It may also help unify efforts across nominally disparate traditions in empirical research, theory, design, and engineering.
Outcomes from 45 Years of Clinical Practice (Paul Clement)Scott Miller
Paul Clement is one of my heroes. He's been tracking the outcome of his clinical services for decades. I was stunned when, in 1994, he published results from his private work over a two decades long period. Now, we have the data from 45 years. Read it!
Research paradigms : simplifying complex debatesThe Free School
This presentation defines the term ‘research paradigm’ with reference to research conducted mostly within human and social sciences disciplines. It also discusses the dominant research paradigms as theorized by leading scholarly publications in these disciplines. This presentation discusses the alternative systems that may aid the researcher to
choose the most appropriate research paradigm.
Introductory discussion provides historical context that explains the reasons why
the notion of the ‘research paradigm’ remains a confusing topic within the
research methods literature. This ambiguity is a core factor that causes this principle to misunderstood by many early-career researchers.
HCI Research as Problem-Solving [CHI'16, presentation slides] Aalto University
Slides from a talk delivered at CHI 2016, San Jose.
Authors: Antti Oulasvirta (Aalto University) and Kasper Hornbaek (University of Copenhagen).
Link to paper: http://users.comnet.aalto.fi/oulasvir/pubs/hci-research-as-problem-solving-chi2016.pdf
Overview: This talk discusses a meta-scientific account of human-computer interaction (HCI) research as problem-solving. We build on the philosophy of Larry Laudan, who develops problem and solution as the foundational concepts of science. We argue that most HCI research is about three main types of problem: empirical, conceptual, and constructive. We elaborate upon Laudan’s concept of problem-solving capacity as a universal criterion for determining the progress of solutions (outcomes): Instead of asking whether research is ‘valid’ or follows the ‘right’ approach, it urges us to ask how its solutions advance our capacity to solve important problems in human use of computers. This offers a rich, generative, and ‘discipline-free’ view of HCI and resolves some existing debates about what HCI is or should be. It may also help unify efforts across nominally disparate traditions in empirical research, theory, design, and engineering.
Outcomes from 45 Years of Clinical Practice (Paul Clement)Scott Miller
Paul Clement is one of my heroes. He's been tracking the outcome of his clinical services for decades. I was stunned when, in 1994, he published results from his private work over a two decades long period. Now, we have the data from 45 years. Read it!
Logical issues in Social Scientific Approach of Communication ResearchQingjiang (Q. J.) Yao
The study concludes that Conceptual analysis is a critical but skipped step in communication and some other social science research. Efforts like AERA, APA, and NCME’s joint committee’s (2014) Standards for Educational and Psychological Testing should be encouraged in multiple areas of social sciences.
Pure deduction is impossible in scientific research; the H-D model falls in either the falsification model or the abduction model.
Some increasingly popular concepts of research methodology, such as statistical inferencing, data, mining, meta-analysis, are inductive in nature.
Surrogate Science: How Fisher, Neyman-Pearson, and Bayes Were Transformed int...jemille6
Gerd Gigerenzer (Director of Max Planck Institute for Human Development, Berlin, Germany) in the PSA 2016 Symposium:Philosophy of Statistics in the Age of Big Data and Replication Crises
Berlin Summer School Presentation Olsen Data Epistemology and Methods Paradig...Wendy Olsen
Berlin Summer School in Social Science. Presentation by Wendy Olsen on Epistemology (Aspects of Knowing) in Methodological Paradigms (Schools of Thought)
Realism, Constructivism, Positivism, Empiricism
Data, Epistemology, Methodology, and Methods Paradigms. Data Collection [book] London: Sage 2012 Date of presentation, July 23, 2014.
ANYTIME YOU COMPLETE A PAPERESSAY in this course you must follow .docxfestockton
ANYTIME YOU COMPLETE A PAPER/ESSAY in this course you must follow the APA rules. Use these requirements to attain full credit regardless if they are all listed out in the directions within the course.
· Set paper with 1-inch margins all around. Spacing ‘before’ and ‘after’ set at 0. Entire document including reference list and running is double spaced, Times New Roman, font 12 with all paragraphs indented on the first line by 1/2 inch.
· One title page with APA running heads and page numbers and the title, your name, school, professor’s name and credentials, date. Video for running head directions https://www.youtube.com/watch?v=8u47x2dvQHs
· There is NO ABSTRACT in papers for this course. At the top of page 2 you will repeat the title of your assignment (not in bold, but centered) and then write a brief introduction paragraph of the ENTIRE paper (main sections should be mentioned; THIS INCLUDES ANY TOPICS FOR CASE STUDY SECTIONS).
· Intro is followed by a Level 1 subheading (bold and centered) for the first half of the assignment. This week it’s Nursing Past Related to Current Profession. Any question/point you are addressing under this heading should be marked clearly with Level 2 subheading which are bolded and flush left.
· Immediately after the first section above without any spaces, you will also use another Level 1 subheading (bold and centered) prior to the second half of the assignment which is the case study. This week it’s Professional Nursing Organizations. Again, differentiate which question/point you are answering by using a Level 2 subheading (bold and at the left margin).
· After both sections are discussed at length – there will be ONE Conclusion - needed for all papers as the last Level 1 subheading bold and centered that summarizes the entire paper/knowledge gained
· There will be ONE alphabetized reference page for all sources set “hanging” with references in APA format. All citations need a reference!
· All references listed are cited correctly in APA format in the text! Points are docked for incorrect citations and not meeting the source requirement!
· Should use 3rd person the majority of the time but it is OK to use 1st person when describing a personal experience related to a specific question.
Journal of Theoretical and Philosophical Criminology, Vol 1 (1) 2009
Quantitative versus Qualitative Methods: Understanding Why Quantitative Methods are
Predominant in Criminology and Criminal Justice
George E. Higgins
University of Louisville
Abstract
The development of knowledge is important for criminology and criminal justice. Two
predominant types of methods are available for criminologists’ to use--quantitative and
qualitative methods. A debate is presently taking place in the literature as to which of these
methods is the proper method to provide knowledge in criminology and criminal justice. The
present study outlines the key issues for both methods and suggests that a criminologist’ resea ...
Why have the artists created these works and what are they.docxphilipnelson29183
Why have the artists created these works and what are they saying about their culture?
Explain how each artist has used the following to make that statement.visual elements (shape or form, line texture, light, value, color, space and movement) principles of design
(unity and variety, balance, focal point, scale, proportion, and rhythm) subject mattermaterials and techniques
Sample Essay 1 (25 points) Compare and contrast these works in terms of:
High Renaissance
Raphael, School of Athens, 1509-10. Fresco, 200 x 300 “.
Photorealism
Chuck Close, Big Self-Portrait, 1967-68.
Acrylic on canvas. 107 ½“x 83 1/2”.
*
Why have the artists created these works and what are they saying about their culture?
Explain how each artist has used the following to make that statement.visual elements (shape or form, line texture, light, value, color, space and movement) principles of design
(unity and variety, balance, focal point, scale, proportion, and rhythm) subject mattermaterials and techniques
Sample Essay 2 (25 points) Compare and contrast these works in terms of:
Boticelli
Birth of Venus, 1486. Tempera on canvas, 67.9 × 109.6 ”
Kees Van Dongen
Femme Fatale. Oil on canvas, 32 X 24”.
German Expressionism, 1905
*
Integrative and Biopsychosocial Approaches in Contemporary Clinical Psychology
Chapter Objective
· To highlight and outline how contemporary clinical psychology integrates the major theoretical models using a biopsychosocial approach.
Chapter Outline
· The Call to Integration
· Biopsychosocial Integration
· Synthesizing Biological, Psychological, and Social Factors in Contemporary Integration
· Highlight of a Contemporary Clinical Psychologist: Stephanie Pinder-Amaker, PhD
· Application of the Biopsychosocial Perspective to Contemporary Clinical Psychology Problems
· Conclusion
Having now reviewed the four major theoretical and historical models in psychology in Chapter 5, this chapter illustrates how integration is achieved in the actual science and practice of clinical psychology. In addition to psychological perspectives per se, a full integration of human functioning demands a synthesis of psychological factors with both biological and social elements. This combination of biological, psychological, and social factors comprises an example of contemporary integration in the form of the biopsychosocial perspective. This chapter describes the evolution of individual psychological perspectives into a more comprehensive biopsychosocial synthesis, perhaps first touched upon 2,500 years ago by the Greeks.
The Call to Integration
While there are over 400 different types of approaches to psychotherapy and other professional services offered by clinical psychologists (Karasu, 1986), the major schools of thought reviewed and illustrated in Chapter 5 have emerged during the past century as the primary perspectives in clinical psychology. As mentioned, these include the psychodynamic, cognitive-behavioral, humanistic, and family s.
Similar to Oom not doom a novel method for improving psychological science, Bradley Woods (20)
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Oom not doom a novel method for improving psychological science, Bradley Woods
1. OOM not Doom: A Novel Method for
Improving Psychological Science
Bradley D. Woods
University of Canterbury
2. “”You take the blue pill the story ends, you
wake up in your bed and believe whatever
you want to believe. You take the red pill –
you stay in Wonderland and I show you how
deep the rabbit-hole goes...
3. Overview
• The Blue Pill:
Measurement
NHST
IEV vs. IAV Research Methods
2. The Red Pill:
Observation Oriented Modeling
4. Measurement
• Modern psychological measurement paradigm really began to take shape and
evolve in the 1940s from events associated with publication of the Ferguson
Report which charged that psychophysical measurement was impossible because
of the lack of fundamental A units of mass, length, and time upon which all B
units of temperature, pressure, etc rely.
• S.S. Stevens replied by redefining measurement. Assuming isomorphism between
properties of objects and the properties of the number system he constructed an
operational interpretation of representational number theory.
• Stevens' Dictum: “The number system is merely a model, to be used in whatever
way we please. It is a rich model, to be sure, and one or another aspect of its
syntax can often be made to portray one or another property of objects or events.
It is a useful convention, therefore, to define as measurement the assigning of
numbers to objects or events in accordance with a systematic rule. ””(Stevens,
1959, p.609).”
• Which led to creation of the NOIR scales and the various differing permissible
operations of identity, order, difference, and equality. It also wedded
measurement to the popular statistical methodology of the Pearson-Fisherian
tradition, virtually guaranteeing widespread adoption (Grice, 2011).
5. Measurement
• Stevens, thus, turned the classical definition of measurement on its head. The
classical concept asserted that numerical measurements supervene on quantitative
attributes. According to Stevens, however, measurable attributes supervene on
numerical assessment.
• Aside from the major philosophical problem associated with operationalism, that
of theoretical pluralism, Stevens' definition is simply wrong! And which had
already been proved some 50 years earlier by Holder.
• Building on Euclid, whom in modern terms had located the ratio of magnitudes
relative to the series of rational numbers, Holder (1901) proved that if an attribute
is quantitative then it is, in principle, measurable. Using the example of a
continuous series of points on a straight line, and invoking ten axioms, Holder
(1901) showed that a relation of addition amongst a series of three points must
implicitly exist.
• The possession of quantitative structure then is the precise reason why some
attributes are measurable and others not. Hence, scientific measurement is
properly defined as “…the estimation or discovery of the ratio of some magnitude
of a quantitative attribute to a unit of the same attribute.” (Michell, 1997).
6. Measurement
• As Holder’s axioms prove there is no logical necessity for any attribute to possess
a quantitative structure, even an ordered series of attributes. To think so commits
the psychometrician’s fallacy (Michell, 2007) Thus, the contention that any
attribute possesses quantitative structure is one that cannot be assumed outright
and must be subject to validation.
• In psychology such validation has developed along two main paths, namely. the
Theory of Conjoint Measurement and Rasch Analysis.
• The former tries to attribute quantitative structure via derived measurement, the
same as in physics, by showing equivalence between an undetermined structure
and two or more quantitative structures. The problem is there are few examples of
already existing quantitative structures in psychology. This has resulted in a
paucity of development so striking it has been described as a revolution that never
happened (Cliff, 1992).
• Rasch analysis involves the formal testing of a measurement scale against a
mathematical model that purports to meet the fundamental axioms of
measurement. However, it is taken as given, and has never been proved, that test
performance is a conjoint structure comprising only person ability and item
difficulty. It also hostage to several conceptual conundrums such as the Rasch
Paradox.
7. Measurement
• The measurement problem is that, aside from a very few examples, we have
no foundation or basis for our claim that we are really accurately measuring
psychological variables. Some might raise an argument for justification
along the lines that measurement predicts behaviour. But that raises an
important scientific question, it doesn’t answer one. Without being able to
show how or why we remain open to criticism such as Trendler (2009),
amongst others, who assert that few, if any, psychological variables have ever
been measured and will ever be able to.
8. NHST
• Hubbard, Parsa, and Luthy (1997) tracked the uptake of NHST in the Journal of
Applied Psychology from 1917 to 1994 and documented an increasing reliance on
statistical significance tests. Referencing more than a 1,000 articles, NHST was
found to appear in only 17% of articles during the 1920s but over 90% of articles
in the 1990s.
• The story is the same for other psychology journals. Hubbard (2008) provides
more recent figures. Sampling 1,750 papers 12 psychology journals Hubbard
(2008) found the use of NHST averaged 94% between 1990 and 2002. Indeed,
The Journal of Developmental Psychology and The Journal of Abnormal
Psychology both averaged more than 99%.
• Efforts at reforming, supplementing, and mitigating the use of NHST such as
changes in editorial policy and publication standards have gained little traction.
Effect sizes, confidence intervals, and validation of the assumptions
underpinning NHST are severely under represented in the psychological literature
(Fidler et al., 2005).
• This echoed and confirmed earlier sentiments of Finch, Cumming, and Thomason
(2001) who, in noting that many important aspects of statistical inference in
psychology remained the same today as it was in the 1940s, concluded: “the
cogent, sustained efforts of the reformers have been a dismal failure.” (p. 205).
9. NHST
• Keselman et al. (1998) reviewed more than 400 analyses published in psychology
journals during 1994 and 1995 and found few studies validated the assumptions
of the statistical tests employed. Similarly, Max and Onghena (1999) found
corresponding levels of neglect in speech, language, and hearing research
journals, while Glover and Dixon (2004) found only 35% of the tests employed in
a range of psychology journals were utilised in a manner consistent with the logic
of NHST.
• Keselman et al. (1998) conducted a Variance Ratio (VR) review of ANOVA
analyses in 17 educational and child psychology journals. In studies using a one-
way design, the mean VR was found to be 4:1 and in factorial studies, the mean
VR was even higher at 7.84:1. This is well outside the 1:1 required ratio
(Keselman et al., 1998). Similar results have been evidenced in a range of other
clinical and experimental journals (Erceg-Hurn & Mirosevich, 2008).
• Micceri (1989) examined 440 large data sets from the psychological and
educational literatures which encompassed a wide range of ability, aptitude and
psychometric measures. None of the data were found to be normally distributed,
and few distributions even remotely resembled the normal curve. Instead, the
distributions were frequently multimodal, skewed, and heavy-tailed. This again
violates a fundamental tenant of NHST.
10. NHST
• Haller and Krauss (2002), provided psychology faculty with a statistical print out
and asked them to answer questions concerning interpretation of the results,
Depressingly, the best result from all groups was that of lecturers teaching
statistical methods classes, 80% of whom were found to agree with at least one
erroneous statistical conception (Haller & Krauss, 2002).
• This follows on from earlier work by Oakes (1986) who asked 70 academic
psychologists for their interpretation of p <.01 and found only 8 (11%) gave the
correct interpretation. Lest it be thought that these results were an aberration,
similar findings have been established by many other authors (Gigerenzer,
Krauss, & Vitouch 2004; Hubbard & Bayarri, 2003).
• These and other similar examples lend credence to Kline’s (2004) contention that
a substantial gap exists between NHST as described in the literature, and its use
in practice.
• Aside from the well known conceptual problems with NHST being a poor
method, it’s poorly understood and even more poorly implemented by
psychologists. To top it off it’s over utilised. As Hubbard (2008) duly
summarises: “The end result is that applications of classical statistical testing
in psychology are largely meaningless.” (p.297).
11. IEV vs. IAV
• An issue of real import for psychological research is that of granularity of
research methods, i.e., at which level, and by which methods, should research be
conducted to generate robust psychological knowledge. Traditionally, there have
been two competing and contrasting approaches and which have led to the
fragmentation of psychological science (Salvatore & Valsiner, 2010).
• The first is concerned with the averages and aggregates of people and groups of
people, interindividual variation (IEV), whereas a contrasting approach focuses
on the study of events and dispositions within a single individual history,
intraindividual variation (IAV) (Krauss, 2008; Molenaar, 2004; Salvatore &
Valsiner, 2010). In the former case, regression analysis, correlations, t-Tests, and
ANOVAs are the most common analytical tools; in the latter, visual analysis of
single-case designs predominates (Saville, 2008).
• Danziger (1990), has shown that during the period 1914-1916 the ratio of
published IAV to IEV research was more than 2 to 1 in several leading
psychology journals. These numbers shifted dramatically, however, and by the
1950s IEV research comprised more than 80% of articles in the same journals.
The situation remains the same today and is described by Danziger as “the
triumph of the aggregate” (p.68).
12. IEV vs. IAV
• Although contemporary psychology has progressively identified itself as a
nomothetic science, nomotheticity has been taken to mean ergodicity of
psychological phenomena. That is, an individual’s variability (IAV) has been
assumed to be identical to the variation between persons within a given
population (IEV) (Molenaar, 2004; Salvatore & Valsiner, 2010).
• Classical theorems of ergodic mathematics illustrate that analysis of IAV will fail
to correspond to the pattern of IEV when; a mean trend changes over time, a
covariance structure changes over time, or when a process occurs differently for
different members of the population. Unfortunately, these are precisely the
conditions that characterise much psychological research (Krauss, 2008;
Molenaar, 2004; Salvatore & Valsiner, 2010).
• Borkenau and Ostendorf (1998) had participants self report items indicative of the
Big 5 factors of personality, daily, over a period of 90 days. Though substantial
consistency was evidenced for the factor structure of longitudinal rotations that
had been averaged across all participants, the five-factor model only fitted the
intraindividual organisation of psychological dispositions for fewer than 10% of
the individual participants. That is, for most participants the supposed 5 factors of
personality gave way to 2, 3, 6 and even 8 factor structures.
13. IEV vs. IAV
• Similar findings have been obtained by Cervone (2005), Cervone and Shoda,
(1999), Epstein, (2010), Grice (2004), and Grice, Jackson, and McDaniel (2006).
• The implication with the overemphasis on IEV research is that aside from
neglecting a valuable research methodology and a potential treasure trove of
psychological knowledge with IAV methods, we’re drawing false inferences
from purely IEV research. Loftus (1996) summarises succinctly: “What we
do, I sometimes think, is akin to trying to build a violin using a stone mallet
and a chain saw. The tool-to-task fit is not very good, and, as a result, we
wind up building a lot of poor quality violins.” (p.161).
14. OOM
• The challenges posed by the preceding issues result largely from the mindless,
ritualised, one-size-fits-all application of traditional research approaches and
methods to psychological research, without regard for relevant conceptual and
philosophical considerations. This view echoes the earlier sentiments of editors of
24 scientific journals who asserted that traditional, variable-orientated, sample-
based, research strategies were ill-suited to accounting for the complex causal
processes undergirding psychological phenomena (NIMH consortium of editors
on development and psychopathology, 2000).
• The trick then is to get the tool to task fit correct.
• Created in direct response to criticisms of the prevailing research practices,
Observation Oriented Modeling (OOM) is a novel methodology and software
program for conceptualising and evaluating psychological data created by James
Grice PhD.
• Premised on realism, seven principles of OOM result, whereby primacy is given
to real, accurate, repeated, observable events that allows for an alternative method
of explaining patterns of observations in terms of their causal structure.
15.
16. OOM
• At the core of OOM are the deep structures of qualitatively and quantitatively
ordered observations. Such structures are obtained by translating data elements
into binary form. For example, the deep structure of biological sex can be
represented “1 0” for females and “0 1” for males. Similarly, in considering a 5-
point Likert scale with highly disagree, disagree, neutral, agree, and highly agree
categories, a highly disagree response would be recorded as “1 0 0 0 0” whereas
an agree response would be coded “0 0 0 1 0”.
• In matrix form, deep structures can be manipulated according to the rules of
matrix algebra which allows for addition, subtraction, and other logical
operations such as if, and, not, etc. The primary mathematical technique
employed in OOM analysis, however, is referred to as Binary Procrustes
Rotation, a modified form of Procrustes rotation often used in personality
pschology and factor analysis in which sets of vectors are rotated to maximal
agreement to establish common/ latent variables (Grice, 2011)
• The goal of OOM is to align the column (units) in such a way that 1s in the
conforming matrix (Likert response) are maximised with co-occurrence of 1s in
the target matrix (gender). This is accomplished using a transformation matrix
which is multiplied against the conforming matrix to establish the fully rotated
deep structure.
18. OOM
The fully rotated matrix compared to the original target matrix. As can be
seen below, they are identical indicating that the 7 unit deep structure
depression ratings could be conformed and reduced to their 2 unit gender
deep structures. That is, gender is the causal factor for depression in this
model.
19. OOM
• Three statistics are employed in OOM to ascertain the success of the rotation and,
thus, how confident we can be about attributing causes from the conforming
matrix back to the target matrix.
• Classification Strength Index (CSI): This measures the the degree to which
values from the target and rotated matrices matched, and range from 0 no match
to 1 complete agreement.
• Percent Correct Classification (PCC): This measures the similitude between
rows of the transformed and target matrices i.e. 0 1 0 vs. 0 0 1 or 0 .8 0 etc. Note
that these are classified as correct, incorrect and ambiguous depending on
whether a perfect, imperfect or partial match has been obtained.
• Chance Value (CVAL): Randomised resampling technique that reshuffles rows
and recalculates the PCC values to see how likely it is that obtained values are at
least as accurate as the actual results. A lower CVAL indicates a more unique
result.
• In the OOM software all this information is summarised in an output screen and
graphically via a multilevel frequency histogram or mutligram.
22. OOM
• Matrix Restrictions: To allow for successful rotation, there must be common
elements between the target and conforming ratios. For example, though a 2x6
matrix could be rotated with a 10x6 matrix, a 4x3 matrix could not be conformed
to a 2x5 matrix.
• CVAL Assumptions: While fewer than traditional statistical analysis,
particularly NHST, the CVAL procedure assumes that observations between the
target and conforming matrices are independent.
• Rich Analysis Options: Aside from a full suite of descriptive statistics OOM
also includes other analysis tools such as model observation separation and
pairwise rotation analyses. OOM can replace Chi Square tests, t-Tests, correlation
analysis, ANOVAs, MANOVAs, bivariate multiple regression and item
comparison procedures of traditional statistical inference (Grice, 2011).
• Ease of use and Efficiency: In replacing many analysis with one tool, and
avoiding the activity required with learning and validating traditional statistical
analysis, OOM can greatly simplify and economise the data analytic process. It
also makes it very easy to learn and use (Grice, 2011).
• The greatest advantage of OOM, however, is its respect for the philosophical and
conceptual considerations of the issues outlined above.
23. OOM
• OOM avoids the dubious and oft violated assumptions of NHST and respects the
mathematical theorems of ergodicity by working at the observational level and
using replication as the means of generalisation.
• Potentially the most important advantage of OOM is that it allows for an alternate
method of inferring causal attributions without assuming quantitative structure. If,
in fact, many psychological variables are not quantitative then we still have a
powerful method for undertaking robust psychological research.
• OOM has been used to challenge conclusions drawn from previous psychological
studies, which have relied on traditional methods of statistical inference, such as
the bystander effect (Grice, 2011).
• Grice, J., Barrett, P., Schlimgen, L., & Abramson, C. (2012). Toward a brighter
future for psychology as an observation oriented science. Behavioral Sciences, 2,
1-22.
• Grice, J. (2011). Observation orientated modeling: Analysis of cause in the
behavioral sciences. New York: Elsevier.
• The OOM software and excerpts from the accompanying text book can be
downloaded free from: http: booksite.academicpress.com/grice/oom
• Which leaves just one question....