Blog post #1: “A Predictive Model of the Non-Profit Sector’s revenues in the US”

Blog post #1.- Andrea Katherine Quevedo-Prince Graphic Model of the Non-Profit Sector-2m7o3ix

Please describe the title and purpose of your project as well as the goals and objectives of your research.

Title: “A Predictive Model of the Non-Profit Sector’s revenues in the US: From a macro to a micro perspective”

General description: Our research aims to define predictive models of the non-profit sector, from a macro to a micro perspective. Accordingly, it comprises three stages: (1) A macro phase, focused on the non-profit sector’s total revenues, as the dependent variable; these are close to one trillion dollars a year, (2) a segmented phase, focusing on fundraising for amateur sports, health and/or education, among other social causes, and (3) a micro phase that would look into individual donor motivations.

Research goals:

1.            To define a predictive model that integrates the variables, and identifies moderating factors, that come into play to determine the ups and downs of the non-profit sector, as measured by total revenues, from the macro perspective.

2.            To define a predictive model that integrates the variables, and identifies moderating factors that come into play to determine the ups and downs of fundraising for amateur sports, and other segments of the non-profit market as health or education, as measured by social cause specific revenues.

3.            To define a predictive model that integrates the variables, and identifies any moderating factors that come into play to determine individual donor motivations for amateur sports, and other segments of the non-profit market as health or education.

Our hypothesis: On a macro or segmented, social cause-specific level, is that total non-profit revenues will respond more greatly to public awareness, among other variables that may include the country’s per capita disposable income, tax incentives, and the legal framework. See enclosed graph of our model.

On a micro level, we posit that as public awareness cascades down into the individual donor’s own awareness of a specific social cause, his or her relationship the cause and non-profit organization would greatly influence his commitment to its cause, again, among other factors. 

Research objectives:

1.            To obtain a clear understanding of the theory and dynamics of the non-profit sector.

2.            To clearly identify the variables and moderating factors that determine non-profit revenues.

3.            To secure reliable statistical sources.

4.            To select the appropriate tests and statistical techniques to determine the weights and interactions of the determining variables and moderating factors.

5.     To select the appropriate scale and questionnaire to determinate individual donor motivations.

6.     To apply all of the above to test our research hypotheses.

Caveats: We worry about the abstract nature of “public awareness” at the macro level. We feel this concept may drive us into a segment-specific metric (ex: concern about health, education, or sports), but the review of the literature and statistical sources should provide early orientations.

Also, we understand total revenues are a combination of individual, corporate, government and NGO donations, sales and fees, plus other fundraising sources. Though we would take all into account, our focus will be on donations, which stand close to $300 billion per year in the US alone.

 

Highlight what you expect to achieve or learn from this project.

We should get a clear understanding of the dynamics of the non-profit sector, its variables, sources of funds, and its players.

We believe that the combination of these predictive models would provide great insight and guidelines for non-profit organizations to properly adjust their fundraising strategies and processes to the dynamics of their sector, segment, and target donor.

 

Explain what methods you will use to answer your research questions.

Our work schedule entails the following. Points 4.1., 4.2., and 5.1. to 5.4. address the question:

1.            Theoretical Grounding, involving the review of close to 100 academic articles.

2.            Research Design, properly sustained hypotheses, research questions, and methodology.

3.            Statistical sources.

4.            Statistical tests, tools and methods.

4.1.         Pace University’s SPSS license

4.2.         Factor Analysis to sort out the variables that determine total revenues, specify their weight and statistical significance; this on the macro phase of the project. 

5.            On a micro level, individual interviews and or surveys among donors of NGOs would be utilized. This would require:

5.1.         Sampling

5.2.         Questionnaire design

5.3.         Appropriate scale

5.4.         Pre-test, and other steps.

Currently, we are deep into the review of the literature. We have sorted out over 100 academic articles and statistical sources, and cited over 25 already, in the development of our theoretical grounding.

Gaps in the literature: We are finding an absence of macro models for the non-profit sector. An overwhelming majority of the articles refer to donor motivations. The amateur sports’ segment is rarely reported on.

We would expect our second post to include a detailed review of the literature, an analysis of non-profit sector statistics, and a clear orientation of our research.

A Probabilistic Model for the Occupancy-Abundance Relationship of Species Populations using Taylor’s Law

“A Probabilistic Model for the Occupancy-Abundance Relationship of Species Populations using Taylor’s Law” is the title of the project that I and Dr. Xu are conducting in this summer. The goals of our project are to 1: clarify the roles of Taylor’s law and negative binomial distribution in He-Gaston model; 2: derive an occupancy-abundance probability model intrinsically from Taylor’s law; and 3: compare our model with the traditional occupancy-abundance model based on negative binomial distribution.

During the project, I participate in data analysis, research design and manuscript writing of the proposed research. I am expected to improve my analysis skills, enrich my knowledge in statistics, ecology and software R,  and learn more practical problem-solving methods in the research.

To answer our research questions, we investigate different models for Occupancy-Abundance relationship using statistical theory and species count data. All analysis are conducted in software R. Currently, we have retested some species count data to access the established models and we have been trying to figure out the insight behind the numbers we gather from the data.

How the nature of work relationships influences the benefits of positive support in the workplace

     The title of the project I am doing with Dr. Gosnell is “how the nature of work relationships influences the benefits of positive support in the workplace.” Our project will examine whether providing support for positive events will benefit the group through greater organizational identification, commitment, and satisfaction. My component will be examining the differences in benefits based on the nature of the relationship. The goal of this research is to further understand workplace support and connectivity. I hypothesize that providing positive support will help people feel more connected to their workplace and people will feel better about their achievements if the support came from a superior. Although, I also think that people will be more likely to share their positive and negative events with coworkers.

     To conduct this study, we had to finalize and submit an IRB. To answer our research question, Dr. Gosnell and I put together a daily diary survey that will be emailed to our participants every day for two weeks. This diary will track how daily fluctuations in event disclosures and support received influences organizational outcomes.  I already entered the survey questions in Qualtrics and we ran several trails to make sure the survey is functioning correctly and doesn’t take too much time to complete. Our questions range from basic demographics to asking whether people had positive or negative events that day, whether that was shared with anyone, and how their mood was affected. Currently, we are finding places to advertise our study and recruit participants. Once we recruit participants we will administer the dairies and later analyze the data to help us understand how support received influences workplace dynamics.

By: Christina Marciante