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Introduction to Analysis with R 07/16/2018 - 09:00 to 07/17/2018 - 15:00
 
07/18/2018 - 09:00 to 17:00
 
07/19/2018 - 09:00 to 17:00
 
 
07/20/2018 - 09:00 to 17:00
 
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07/23/2018 - 09:00 to 17:00
 
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07/23/2018 - 09:45 to 17:00
 
07/24/2018 - 09:00 to 17:00
 
07/24/2018 - 09:00 to 17:00
 
Hierarchical Linear Modelling 07/25/2018 - 09:00 to 07/27/2018 - 17:00
 
 
Statistical / Machine Learning 07/25/2018 - 09:00 to 07/27/2018 - 17:00
 
 
 
 
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Course Descriptions

Have you wondered how to determine the best fit of a qualitative research approach with the study purpose for your research idea? Together, in this half day session, we will explore the designs and procedures inherent to five qualitative research approaches: narrative research, phenomenology, grounded theory, ethnography, and case study. The session will be organized around four key questions: What are the origins and defining features of each approach, what types and methods are associated with each approach, what data analysis and writing structures are commonly used for each approach, and what challenges and ethical considerations are likely encountered for each approach? Participants are encouraged to bring their ideas for a qualitative study to explore during the workshop as there will be embedded opportunities for small and large group discussions. No prerequisites but some understanding of the fundamentals of qualitative research is assumed. Participants may wish to explore the following reference as background reading for the session: Creswell, J., & Poth, C. (2017). Qualitative inquiry & research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage.

Instructor: Cheryl Poth
Pre-requisites: Previous experience with qualitative research is an asset
Date: Thursday, July 19, 2018 - 09:00 to 17:00
Location: N1042

Introduction to Digital Scraping: One of the most time-consuming aspects of performing any sort of data analysis is getting that data in the first place. Often, a straightforward, well-structured database doesn't exist, which means you need to build one yourself, from scratch. That's where scraping comes in: you can build a program to automate this collection for you, saving countless hours of boring and imprecise data entry. In this one-day class, you'll learn how to decide on the structure for your data, pick the right scraping approach, create a scraper and systematize your data collection. The class will introduce the basic concepts and strategies behind scraping, and focus on getting data off both websites and offline documents (such as PDFs).

Instructor: Tom Cardoso
Pre-requisites: Basic knowledge and familiarity with R, Python or JavaScript.
Date: Friday, July 20, 2018 - 09:00 to 17:00
Location: K214

This 3-day advanced multivariate statistics course provides an introduction to applied analyses of multilevel models. Students will learn how to use multilevel models for analyzing clustered data (e.g., persons nested in groups) and longitudinal data, such as flexible strategies for modeling change and individual differences in change. Multilevel models are known by many synonyms (hierarchical linear models, general linear mixed models). The defining feature of these models is their capacity to provide quantification and prediction of random variance due to multiple sampling dimensions (across occasions, persons, or groups). Multilevel models are useful in analyzing clustered data (e.g., persons nested in groups), in which one wishes to examine predictors pertaining to individuals or to groups. Multilevel models also offer many advantages for analyzing longitudinal data, such as flexible strategies for modeling change and individual differences in change, the possibility of examining time-invariant or time-varying predictor effects, and the use of all available complete observations. This course will serve as an applied introduction to multilevel models for both longitudinal and clustered data, as well as combinations thereof. We build upon familiar regression models and expand into multilevel regression models from that starting point.

Instructor: Manuel Reimer, PhD
Pre-requisites: Solid knowledge of multiple regression and basic knowledge of SPSS
Date: Wednesday, July 25, 2018 - 09:00 to Friday, July 27, 2018 - 17:00
Location: BA531

R is a powerful and open-source software package for statistical analysis that is, by some measures, becoming the standard for data analysis in the private, public and academic sectors. It’s strengths are its open nature, flexibility, its capacity to produce compelling and informative graphics and the thousands of users that produce an astonishing array of packages that enable the software to perform statistical packages. This day-long session will introduce participants to R tailored to their topics of interest and level of familiarity with statistics and data analysis. Topics will include basic data management strategies, producing descriptive and bivariate statistics, the linear and generalized linear model and data visualization.

Instructor: Simon Kiss, PhD
Pre-requisites: An undergraduate course in statistical analysis
Date: Monday, July 16, 2018 - 09:00 to Tuesday, July 17, 2018 - 15:00
Location: K214

This 1-day workshop is suitable for people working with qualitative data. The first half of the course will cover basic usage of NVIVO software, organizing data and/or literature, basic coding, and methodological considerations. The afternoon portion of the course will cover intermediate-advanced usage of NVIVO such as, creating a codebook, querying/quantifying data, coding audio/video, organizing classifications, and data visualization. This course is taught using an interactive approach and attendees will be able to practice their skills using NVIVO. Individuals are encouraged to bring a working dataset or a selection of literature. If you do not have current data or relevant literature to review, a sample dataset and literature will also be provided.

Instructor: Ellis Furman
Pre-requisites: None
Date: Friday, July 20, 2018 - 09:00 to 17:00
Location: BA113

Qualitative data analysis (QDA) turns qualitative information into findings. It is the process through which we identify, explain, interpret and make sense of qualitative data. This course offers an overview of concepts, approaches, and strategies in QDA, with a focus on analysing data from interviews. It provides a comprehensive introduction to thematic analysis, a method that is broadly applicable. By the end of this course, you have a better understanding of how to plan for QDA, be familiar with basic coding techniques, know how to ensure your findings are trustworthy, and be ready to start analysing qualitative data. This is a hands-on course; you will have opportunity to practice your new knowledge and skill by working with tutorial data. This workshop is appropriate for academics and professionals alike.

Instructor: Sue Weare, MSW
Pre-requisites: None
Date: Tuesday, July 24, 2018 - 09:00 to 17:00
Location: K214

This one-day interactive workshop is ideal for anyone interested in collecting quality quantitative and qualitative survey data, without the hassle of data entry. This workshop introduces both basic and advanced features of Qualtrics, a powerful survey tool that allows users to build complex online surveys. The first half of the day will cover basic features, such as creating and managing surveys, organizing account libraries and adding and editing questions. The second half will cover more advanced features, such as advanced question options, loop and merge, skip logic, display logic, carry-forward, block options and survey flow. Participants will receive hands-on experience working through example surveys and in-class projects. By the end of the workshop, participants will be able to navigate Qualtrics, create new surveys, add a variety of different question types, and edit the survey flow as needed.

Instructor: Bianca Dreyer
Pre-requisites: None
Date: Thursday, July 19, 2018 - 09:00 to 17:00
Location: K214

This 1-day workshop is geared toward those hoping to gain a basic understanding of regression analysis using SPSS. The workshop will start with simple bivariate linear regression and move onto multivariate regression with continuous, discrete and binary dependent variables. By the end of the workshop, participants will be able to import data into SPSS, recode variables, run regression models and interpret results.

Instructor: Andrea Lawlor, PhD
Pre-requisites: Undergraduate course in research methods or statistical analysis is advised, but not required
Date: Tuesday, July 24, 2018 - 09:00 to 17:00
Location: BA206

This 1-day workshop is ideal for anyone working, or planning to work, with survey data. No background in research methods or statistical analysis is required. This workshop introduces basic skills in data analysis using SPSS. The day will mix discussion of statistical analysis principles (e.g. variables, formulating hypotheses, levels of measurement, univariate statistics, bivariate statistics, etc.) with hands-on training. By the end of the workshop, participants will be able to import data into SPSS, recode variables, generate tables, and execute some basic descriptive analysis.

Instructor: Anthony Piscitelli
Pre-requisites: None
Date: Monday, July 23, 2018 - 09:00 to 17:00
Location: N1055

Knowledge translation involves moving your research out of the “academic” realm and to those who can put the results of your research inquiries into action. This process begins when you begin your research and continues through the conclusion of the data analysis and production of findings. This course will take you through the knowledge translation planning process focused on social sciences and humanities based research topics.

Instructor: Shawna Reibling, MA
Pre-requisites: None
Date: Saturday, July 21, 2018 - 09:00 to 17:00
Location: K214

Whenever theoretical arguments are contradictory and a large body of empirical research exists on a topic that shows inconclusive results a meta-analysis can shed more light on the true relationship between variables as it allows researchers to synthesize empirical research and to quantify effects. This interactive 1-day workshop will help participants to understand the method of meta-analysis. The course is structured along the different phases of a meta-analysis including the study location process, coding, bivariate analyses and multivariate analyses. Participants will apply their knowledge in hands-on exercises.

Instructor: Nina Rosenbusch, PhD
Pre-requisites: None
Date: Monday, July 23, 2018 - 09:00 to 17:00
Location: N2005

Many people underestimate the art and science that goes into developing and designing a good survey. This 1-day course will help you develop surveys that will help your respondents to understand and answer the questions the way you intended it. The instructor will draw on several decades of psychological research on the survey response process as well as his own experience as a researcher, teacher and consultant in survey design. The course is structured along 10 core principles of good survey design and is easy to follow with no prior knowledge needed. Among others, topics include the cognitive-affective survey response process, the survey development process, survey structure and format, question wording, and online surveys. The course is conducted in an interactive way and participants will be able to apply their knowledge through practice exercises.

Instructor: Sean Simpson, MA
Pre-requisites: None
Date: Wednesday, July 18, 2018 - 09:00 to 17:00
Location: N1044

Interviewing is the most common method of data collection used in qualitative research. This 1-day course provides a detailed overview of individual and group interviews, which are the most common methods of data collection used in qualitative research. By the end of this session, you will be familiar with benefits and challenges of interviewing, how to create an interview protocol, how to craft effective questions, key skills in conducting the interview, and how to assess the quality of an interview. This is a hands-on course; you will have opportunity to practice your new knowledge and skill through role-playing in small groups. This workshop is appropriate for academics and professionals alike.

Instructor: Sue Weare, MSW
Pre-requisites: None
Date: Monday, July 23, 2018 - 09:45 to 17:00
Location: K214

The 'big data' revolution has required us to rethink not only how we conceive of and collect data, but also the methods we use to analyze our data. In this workshop, we will put the parametric statistical models with which we are all familiar (e.g., OLS, logit, probit) in the context of this larger discussion about statistical learning. These ubiquitous models are simple cases of statistical learning algorithims that can be extended in lots of interesting directions. In the course, we will discuss both supervised learning (models that have a dependent variable, like those for regression and classification) and unsupervised learning (models without a dependent variable, like principal components analysis, and clustering). In particular, we will briefly cover OLS regression and GLMs as simple regression/classification tools. We will then turn to extensions of these models with automatic variable selection (ridge regression and the lasso). Next, we will then branch out to cover semi- and non-parametric alternatives, like generalized additive models (GAMs), kernel regularized least squares, multivariate adaptive regression splines and tree-based regression models. Finally, we will cover clustering, principal components analysis and some extensions like finite mixture models. The course will have both theoretical and applied aspects, though it tends to focus more on applications. All of the models mentioned above have straightforward implementation in R, which we will use thorughout the course. The course will have a lecture component, structured labs and more flexible time where you can try out what you're learning on your own data.

Instructor: Dave Armstrong
Pre-requisites: Familiarity with parametric statistics
Date: Wednesday, July 25, 2018 - 09:00 to Friday, July 27, 2018 - 17:00
Location: BA206