Actions speak louder than words : a series of pilot studies developing novel approaches to measuring implicit attitudes
Keywords: Education.

The following pages will present a review of three papers that examine the measurement of bias in cognitive processing. These studies build upon one another to propose various methodological approaches for understanding the relationships between endorsed identities, learned familiarity with the ideas we interact with each day, and the personally endorsed attitudes formed in social contexts. Historically, behaviorist theories dominated education research, proponents such as Thorndike and Skinner argued that the mind was an enigmatic "black box" and that the only reliable measure of intention was its outward behavioral manifestations. This perspective was driven in part by the belief that "seeing is believing," for much of the sighted human population. Sloganized turns of phrase like this serve as an example of a cognitive shortcut using language to create a mental bias for preferring one choice over another; that simple idea ends up being the focus of this dissertation by exploring the measurement of cognitive biases rooted in near-automatic responses to stimuli. The integration of behaviorism and cognitivism in psychological assessment is a current topic of interest for researchers. This investigation aims to bridge the gap between these two fields by improving the current approaches to measuring attitudes. Understanding of human experiences has expanded leading to various learning theories emerging to connect physical behavior and internal mental experiences. While the importance of observable behavior in understanding our internal processes cannot be denied, it is also important to consider the influence of internal experiences on observable behavior. Endpoint analysis, a term I use here to refer to the study of final decision outcomes while seeking to understand internal processes, is common in educational and psychological research. Examples of endpoint analysis include the use of brain imaging to assess the effectiveness of interventions (e.g. Iuculano et al., 2015), self-report questionnaires to measure psychological indicators (e.g. Gosling, Rentfrow & Swann, 2003), and reaction time comparisons to assess bias (e.g. Greenwald, McGhee & Schwartz, 1998). However, these approaches often only allow for limited examination of the facets that more current methods can readily detect such as hesitation, ease of cognitive activation, and increased cognitive complexity during decision-making processes. Methods using endpoint analysis approaches have numerous appropriate applications and have contributed significantly to a range of scientific inquiries. In contrast, process tracing as described by Collier (2011), involves tracking the development of a phenomenon using markers that are collected during the process leading to final outcomes. The present dissertation samples from these existing fields to present three papers that explore different approaches to understanding complex psychological and socially grounded processes, with methods that can be implemented at scale within existing learning environments. In paper one, the predictive power of self-identity and connections to math attitudes are examined. In paper two, the Implicit Association Test methodology used in paper one is adapted to develop a process tracing analogy to the original Implicit Association Test procedure. The methods-focused pilot study uses Finger Tracking: a method for collecting data about implicit cognitive processes coded by Ethan Roy in Pavlovia to allow the collection of implicit cognitive process data on touchscreen devices. In paper three, a process-tracing method called cognitive surveys are introduced to measure math mindsets, combining elements of self-report surveys and IAT categorical comparisons while collecting data in a Finger Tracking environment; a method that shows promise for repeated assessments of mindsets and other related psychological constructs of interest to researchers. Each paper builds on the last in a very specific way. Paper one uses a combination of self-report and implicit measures to show how self-identification as a "math person" is associated with both explicit math anxiety self-report data and implicit math/art IAT comparisons. Paper two examines the IAT as a tool, providing evidence and methods to interpret IAT procedures administered in the Finger Tracking environment. The evidence shows how an existing implicit social cognition metric can be adapted and extended through the use of movement tracking to infer cognitive processing stages. In the third paper, a novel procedure is introduced to bridge the gap between implicit and explicit survey methods. Each prompt responded to in paper three's data set used a bank of learning endorsement statements to understand group dispositions; derived from an existing math mindset survey and the identity statement shown in paper one to have predictive power for math-learning attitudes. The jump between IAT methodology and Finger Tracking is gleaned from paper two where the analogies to reaction time with Finger Tracking provide evidence showing how convergent metrics from tracking movement serve to bolster implicit valenced attitude data. Paper three uses the lessons from the previous two papers to propose the Cognitive Survey paradigm, a new method of measuring attitude endorsement which is accessible to the average researcher willing to incorporate new research methods. Techniques such as functional magnetic resonance imaging and electroencephalography offer valuable insights into the location and time scale processes in the brain respectively, and costs are coming down, but both are often too expensive for use in educational settings due to the infrastructure and staff requirements needed for both measurement and analysis stages. In light of the growing interest in neurological information and the prohibitive expense of many existing neurological probing methods, alternative approaches such as facial recognition, eye tracking, and body tracking have become viable options for expanding research avenues in recent years. The forthcoming investigation explores implicit cognition, first using implicit association test methodology, then a form of movement tracking called Finger Tracking, a form of movement tracking as a tool for understanding complex psychological and social processes. Finger tracking is a method that utilizes an assumption of a brain-body connection to examine the relationship between movement and cognitive processing. Based on the idea that changes in the direction and velocity of finger movements reflect the mental effort involved during the task. The approach builds on body tracking research adapted to touchscreens in a coding framework developed by Ethan Roy for use in Pavlovia. The recombination allows a touchscreen interface to conduct Finger Tracking experiments and record the data, which can then be analyzed using an R studio package called mousetrap (Kieslich & Henninger, 2017). One benefit of Finger Tracking is that it can provide a wealth of process data that can be analyzed using traditional statistical methods and offers opportunities to probe previously unexplored relationships connected to cognitive processes. In paper three short statements elicit responses while making fast choice selections allows probing identity-based questions in Finger Tracking studies that facilitate the collection of both implicit and explicit responses within a single sitting. The method shows promise to provide a comprehensive understanding of an individual's experiences and cultural context. The effectiveness of Finger Tracking as a tool for analyzing the brain-body connection and cognitive processing is still emerging research and has not been fully established in the psychological sciences. Further empirical evidence and analysis are needed to fully understand the potential of this approach through a better understanding of how to interpret interactions and trends provided by the numerous available data streams. A small step in that direction is undertaken here. The development of trace process methods for this inquiry aims to advance the assessment of cognitive bias in educational settings and suggests tools that can be utilized in the broader landscape of psychological research. Finger tracking, which involves data collection on touchscreen devices and allows the automation of analysis based on quantitative metrics collected in flight, has the potential to bridge the gap between cutting-edge neuroscience and behavioral measures for education research. Finger Tracking allows for the accurate inference of cognitive processing stages at the trial level of granularity, providing greater specificity than most available methods currently in popular use. The first paper included in this inquiry utilizes an Implicit Association Test to examine the relationship between mathematical bias and self-reported math anxiety. Results show how math identity is proportionally related to cognitive bias through a series of self-report and implicit math attitude instruments. The data demonstrates the known relationship between math anxiety and implicit math attitudes measures evidenced in previous research, providing data to show how important math identity is in relationship to math anxiety both implicitly and explicitly. The discussion explores the bias of the crowd model presented by Payne, Vuletich & Lundberg (2017) to explain how social norms present in classroom situations can create the cultural inputs that structure internal narratives important for the acquisition of foundational knowledge in cognitively demanding contexts. The second paper introduces a revised version of the Implicit Association Test administered on tablet computers, which allows for the derivation of new metrics using multiple cognitively relevant processing markers. This revised tool has the potential for transformative