DATALAB PROJECT DESCRIPTIONS – SPRING 2022
The psychology department has three honors students who are developing and conducting independent research projects under faculty supervision. All three are using SHU-supported Data Services software, including Qualtrics and SPSS. As part of this initiative, students will attend one Data Services class on a program they plan to use and, after consultation with their advisor, choose another class to learn a different technology that is relevant to their research project.
Paul Corrente is studying the role of face masks in recognition memory. The study is an experimental design in which participants study masked and unmasked faces at study and at test have to identify the faces they studied both when they appear again as at study (e.g., masked at both) and when they switch (e.g., masked at study unmasked at test). The study may be programmed using Qualtrics and Paul will learn how to counterbalance and randomize within a single survey link. He will use Excel for cleaning and sorting the data output. With support from the grant, Paul could use SPSS in addition to JAMOVi in order to analyze the data and create figures for his thesis. In addition he may be interested in trying out Tableau. Paul's faculty mentor is Dr. Marianne Lloyd.
Shelby Carlock will examine the influence of accent bias on students’ perception of success. She also wants to find out if intercultural contacts reduce the accent bias. The study will be an experimental design with scenario-based audio manipulation. Four audio versions of the scenario will be created. Participants will listen to various passages with a standard American accent or a Chinese foreign accent. Within each condition, grammar accuracy will be varied as well (e.g., accurate vs. inaccurate). After listening to the scenario, the participants will answer questions about manipulation checks, perception of success toward the reader, and their intercultural contacts. Shelby will use Qualtrics to design the experiment and collect the data online. She will use SPSS to analyze the data. Shelby's faculty mentor is Dr. Fanli Jia.
Autumn Cataldo is examining how parents teach children to solve spatial problems and whether such knowledge transfers to a novel spatial problem. Parents and children will complete 5 worksheets together. The worksheets contain path-following problems for children to solve. The worksheets increase in difficulty, requiring parent assistance throughout. Afterwards, children will complete a related, but novel spatial problem independently during a virtual video call with Autumn. The secondary goal of the study is to examine the feasibility of a completely virtual data collection method for examining parent-child interactions and children’s spatial performance. Therefore, all data will be collected virtually. Autumn has assisted with the study design and piloting the methods. She will continue to work on various aspects of the study related to data, including data collections, coding, and analysis. Autumn is using several University-supported software programs, such as Teams, Excel, SPSS, and Qualtrics, to implement the study. Autumn's faculty mentor is Dr. Amy Joh.