Statistics, Data Analysis & Research Methods Expert

Lora Connor Profile Picture

Fourteen years hands-on experience using statistics and data analytics to design, implement, and analyze quantitative and qualitative research projects and surveys for large data sets.

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RESEARCH

COMPLETED PROJECT SAMPLES

Projects Descriptions Samples
  1. Power BI Dashboard: General Relief (GR) Program
  2. Power BI Dashboard: Aging Assistance Programs (AAP) Recipients
  • To obtain data for these maps, I used SQL Queries (i.e., querying specific columns, date ranges, and joins on client ID number for various tables) extracting thousands of cases (rows) through Power BI which was connected to SSA's databases through Oracle.
  • Then I cleaned and organized the data in Power BI.
  • Finally, I created DAX measures to create the final product (i.e., Charts, Tables, Slicers, Map)

Power BI Dashboard for General Relief Program

Power BI Dashboard for Aging Assistance Programs Recipients

  1. ArcGIS StoryMap: Medi-Cal (MC) Program
  2. ArcGIS Dashboards: Various Programs
  • To obtain data for these map, I used SQL Developer, using SQL Queries (i.e., querying specific columns, date ranges, and joins on client ID number for various tables) extracting hundreds of thousands of cases (rows).
  • Most of the data extracted from the Social Services Agency's databases have mispelled city names due to data entry errors, so I use SPSS to clean up the data and create tables for clients' demographic information and cities of residence.
  • I then export the data to a csv file and import it into ArcGIS Pro.
  • Finally, I create the maps in ArcGIS Pro and export them into ArcGIS Online to create the dashboards. Sometimes I use the dashboards to create a StoryMap in ArcGIS Online depending on the wishes of the requester.

ArcGIS StoryMap for Medi-Cal Program

ArcGIS Dashboards: Various Programs

  • Statistical Training for Colleagues
  • I developed a PowerPoint presentation for my colleagues within my research unit at the Social Services Agency. It was to fullfill a yearly requirement to add additional contributions, outside of our regular workload, to our unit or the agency.
  • The main objective was to introduce an alternative way to deal with outliers within our caseload data using data imputation techniques.
  • The presentation begins with a refresher on how to explore data and find outliers within the data.
  • Then several possible imputations methods are examined and compared.
  • Finally, just for fun, I added to the presentation other statistical techniques that are possible when comparing some of the data we typically work with at the Social services agency, such as Path Analysis, Mediation, and Moderation techniques.

Statistical Training for Colleagues

  1. Telecommuters/Teleworkers Survey Report
  2. Telecommute/Telework All-Staff Survey Report
  • As the agency began working into more Telecommuting and Telecommuting options for workers to save on space and office costs, they asked that surveys be created for current Telecommuters and Teleworkers and for all staff whether they Telecommuted or Teleworked to determine various factors concerning this new type of working situation.
  • I developed two different surveys asking questions about various concerns, such as worker satisfaction, moral, connectedness, technology, productivity, etc.
  • I then entered the surveys into SurveyMonky and sent the survey to every worker at the Social Services Agency.
  • After a few weeks, I extracted all of the data from SurveyMonkey into excel files and imported the data into SPSS
  • Then I ran statistical analyses on the data such as t-tests and ANOVA to evaluate each indicator.
  • Finally, I wrote reports for each survey and presented them to the heads of the Agency, making recommendations, such as how to implement oversight.
  • This information became very helpful when COVID-19 began and nearly every worker at the Social Services Agency was forced to work from home informing the leaders on how to implement oversight and provide support to their workers.

Telecommuters/ Teleworkers Survey Report

Telecommute/ Telework-All Staff Surveys Report

  1. Client Focus Groups: SARC, ARC, & CRC Regional Centers
  2. Staff Focus Group: SARC Regional Center
  • I developed a PowerPoint presentation for my colleagues within my research unit at the Social Services Agency. It was to fullfill a yearly requirement to add additional contributions, outside of our regular workload, to our unit or the agency.
  • The main objective was to introduce an alternative way to deal with outliers within our caseload data using data imputation techniques.
  • The presentation begins with a refresher on how to explore data and find outliers within the data.
  • Then several possible imputations are examined and compared.
  • Finally, just for fun, I added to the presentation other statistical techniques that are possible when comparing some of the data we typical deal with at the Social services agency, such as Path Analysis, Mediation, and Moderation techniques.

Client Focus Groups: SARC, ARC, & CRC Regional Centers

Staff Focus Group: SARC Regional Center

  • 2 Examples: Seasonal Statistical Forcasting: Using Data Imputation for COVID 19 Outliers
  • I developed a PowerPoint presentation for my colleagues within my research unit at the Social Services Agency. It was to fullfill a yearly requirement to add additional contributions, outside of our regular workload, to our unit or the agency.
  • The main objective was to introduce an alternative way to deal with outliers within our caseload data using data imputation techniques.
  • The presentation begins with a refresher on how to explore data and find outliers within the data.
  • Then several possible imputations are examined and compared.
  • Finally, just for fun, I added to the presentation other statistical techniques that are possible when comparing some of the data we typical deal with at the Social services agency, such as Path Analysis, Mediation, and Moderation techniques.

Statistical Forcasting Example 1: Using Data Imputation for COVID 19 Outliers

Statistical Forcasting Example 2: Using Data Imputation for COVID 19 Outliers

  1. Telecommuters/ Teleworkers Survey
  2. Telecommute/ Telework All Staff Survey
  • As the agency began working into more Telecommuting and Telecommuting options for workers to save on space and office costs, they asked that surveys be created for current Telecommuters and Teleworkers and for all staff whether they Telecommuted or Teleworked to determine various factors concerning this new type of working situation.
  • I developed two different surveys asking questions about various concerns, such as worker satisfaction, moral, connectedness, technology, productivity, etc.
  • I then entered the surveys into SurveyMonky and sent the survey to every worker at the Social Services Agency.
  • After a few weeks, I extracted all of the data from SurveyMonkey into excel files and imported the data into SPSS
  • Then I ran statistical analyses on the data such as t-tests and ANOVA to evaluate each indicator.
  • Finally, I wrote reports for each survey and presented them to the heads of the Agency, making recommendations, such as how to implement oversight.
  • This information became very helpful when COVID-19 began and nearly every worker at the Social Services Agency was forced to work from home informing the leaders on how to implement oversight and provide support to their workers.

Telecommuters/ Teleworkers Survey

Telecommute/ Telework All Staff Survey

  1. Pre-Post Visit Coaching Survey
  2. Satisfaction Survey: Visit Coaching
  • To evaluate a new program for the Social Services Agency, they requested that I design a research study to evaluate the new program they had just created and started implementing
  • Special care was taken in the wording of these surveys due to the sensitivity of the subject matter and to compel more forthcoming, truthful answers.
  • The main objective was to eveluate the effectiveness of the program as well as parent participant satisfaction for the new program.
  • To evaluate the effectiveness of the program, I developed pre and post surveys to measure the amount of improvement in participants from the beginning of the program to the completion of the program.
  • To evaluate participant satisfaction, I created satisfaction surveys to be distributed at the completing of the program.
  • Existing data that was kept regarding the successful completion of the program were used for the study as well.

Pre-Post Visit Coaching Survey

Satisfaction Survey: Visit Coaching


Master's Thesis Research Project

CSULB: Psychological Research Graduate Program

Master's Thesis Project (Final Presentation): Conceived, designed, and conducted original research to test the relationship between the neurotic personality trait and menopausal symptoms with stress and exercise as moderators. Wrote and presented the research proposal for approval to proceed; presented the final results for approval by the thesis committee. Designed and coded a web page for survey participants to inform them about the study’s details, to gain their consent to participate, and then to complete the embedded survey using SurveyMonkey. Recruited participants through social media, analyzed date using SPSS, and wrote and presented results of research data for publication.

Connor, L. (2015). The Neuroticism Personality Trait and Its Relationship to Menopausal SymptomsCalifornia State University, Long Beach, CA.

Presentation: Master's Thesis-The Neuroticism Personality Trait and Its Relationship to Menopausal Symptoms


PROJECT MANAGEMENT: Motorcycle Safety Foundation (MSF)

Rider Education Training System, Development and Research

Led the operations, both on-site and in-office, for the Discovery Project, a longitudinal, multi-million dollar, research study for MSF involving a cross-functional team from UNC, NTSB, and DMV. The objective of the project was to research product development and evaluate the effectivemess of MSF's new Rider Education and Training System (RETS)