Nonprofit Organizations Resources

Abstract

Nonprofit organizations worldwide are struggling to survive the economic effects of the COVID-19 pandemic. This study examines whether nonprofit organizations have become too dependent on a single revenue source and how much they have had to change their mission due to pandemic financial uncertainty. This research surveys diverse nonprofit organizations to understand their resource dependency and funding strategies. A comprehensive questionnaire will collect data on nonprofit organizations’ revenue sources, funding patterns, and mission changes. The survey will be distributed among a representative sample of nonprofit organizations across various sectors, including healthcare, education, environmental conservation, and social services. A mixed-method approach will be employed to gather quantitative data, such as revenue breakdowns, mission adaptation scores, and qualitative insights, through open-ended questions allowing respondents to provide detailed explanations. The survey responses will be analyzed using statistical methods such as descriptive statistics, correlation analysis, and regression analysis. The results will clarify the degree to which charitable organizations depend on a single donor and the funding sources most adversely affected by the pandemic. The research will also look into the methods NGOs employ to diversify their sources of financing and how much this has affected their goals.

Introduction

Charitable organizations must play a significant part in meeting the requirements of society and advancing worthy causes. However, the COVID-19 outbreak and the following economic uncertainty have presented substantial hurdles for these groups, compelling them to reevaluate their capacity to be financially sustainable in the long term. In particular, the question arises as to whether or not charitable organizations have become too dependent on a single source of cash and whether or not this dependency has driven these organizations to change their missions to gain funding (Camm et al., 193). To investigate this matter, surveys and studies have been conducted to determine how much nonprofit organizations have become dependent on a single funder for their resource provision (Camm et al., 193).

Rational Thinking

Rationality underpins good research. It requires objective analysis, evaluation, and logical conclusions. Rational thinking in research helps avoid prejudices and create fact-based conclusions.

Critical Thinking

Critical thinking helps researchers evaluate, analyze, and interpret data. It entails questioning assumptions, assessing underlying reasoning, and recognizing knowledge strengths and flaws. Critical thinking helps academics find study gaps and propose novel solutions (Davis et al., 23).

Possibility Thinking

Possibility thinking opens researchers’ eyes to new ideas and perspectives. It inspires scholars to examine new paradigms. Possibility thinking helps researchers develop new research methods, theories, and approaches (Kumar, 90).

Research Tools

Research tools are as essential as rational, critical, and possibility thinking. Research tools include online databases, statistical software, laboratory equipment, and data visualization tools.

Secondary Data

Secondary data is previously collected or taken from existing sources. Secondary data is cost-effective and time-saving for researchers. Books, government papers, polls, and databases are secondary data sources.

Primary Data

Researchers collect primary data to answer research questions. This data is collected by surveys, experiments, interviews, observations, and focus groups. Researchers can customize data collection to meet their research goals.

Quantitative Data

Quantitative data can be analyzed statistically and mathematically. Structured surveys, experiments, and observations yield objective data (Leedy et al. 234).

Qualitative Data

Qualitative data reveals people’s thoughts, feelings, and actions. Interviews, open-ended surveys, observations, and textual analysis are included.

CHAPTER ONE: SECTION 1: TYPES OF SURVEY RESEARCH

Researchers utilize surveys to gather data from a specific community or sample. Survey research methodologies have evolved to meet varied needs.

Electronic Surveys

Electronic surveys, usually called online surveys, have become increasingly common in recent years due to the proliferation of internet use and other digital technologies.

By Mail

Surveys conducted by mail entail sending questionnaires and other related materials through the regular mail system.

In-Person Surveys

In-person surveys require the researchers and respondents to engage in face-to-face conversation.

Group Clicker

In educational and conference contexts, group clicker surveys are utilized, also known as audience response systems or classroom response systems.

CHAPTER TWO: NEEDED STATISTICS BACKGROUND

In many industries, Statistics are essential for data analysis, decision-making, and complicated problem-solving. Today’s data-driven world requires a strong background in statistics. This essay discusses how a statistical background helps people traverse data, develop meaningful conclusions, and advance numerous fields.

Variables

Variables help researchers grasp phenomena’s linkages and patterns. Variables might be independent or dependent. Researchers change independent factors to study their effects on the dependent variable. However, independent variables affect dependent variables.

Research questions

These questions should be concise, specific, and focused on study goals. They should support data gathering and analysis and match the variables of interest.

Hypothesis statements

Hypothesis statements explain or predict variable relationships based on study questions. They are usually testable null or alternative hypotheses. Null hypotheses say variables have no meaningful association, while alternative hypotheses say they do.

Dependent Variables

Research studies measure or observe dependent variables. Independent variables and exhibit manipulation or intervention effects impact them. Depending on the investigation, dependent variables are measured quantitatively or qualitatively.

Independent variables

The variables of interest are predicted to affect the dependent variable. The research design can modify or assess categorical (e.g., gender, treatment group) or continuous (e.g., age, income) independent variables.

Mediating, Moderating Intervening Variables

Mediating factors describe how independent variables affect dependent variables. They explain the causal pathway and the link between the variables. Moderating variables affect the independent-dependent connection (Habib et al., 244). They illustrate the effect’s strengths and weaknesses.

Confounding and Control Variables

Confounding variables are outside factors that can affect the independent-dependent connection. Uncontrolled, they can influence outcomes. To correctly understand study results, confounding variables must be identified and controlled. To isolate the effect of the independent variable on the dependent variable, researchers control variables.

Types of Variable Measures

Statisticians use variables to analyze and interpret data. Their qualities and measurement level classify variables. This essay will discuss continuous, dichotomous, nominal, ordinal, interval, and ratio scales. Descriptive statistics and central tendency measurements like mean, mode, and median will be covered.

• Continuous variables

Continuous variables have any value within a range. They may be measured precisely and expressed numerically. Height, weight, temperature, and time are continuous variables. Correlation, regression, and t-tests investigate continuous variables.

• Dichotomous variable

Dichotomous variables are categorical variables with only two levels. They usually have binary values like “yes” or “no,” “true” or “false,” or “success” or “failure.” Dichotomous variables include gender, traits, and coin tosses. Chi-square testing and logistic regression evaluate dichotomous variables.

Nominal

Nominal variables are unordered categorical variables. They are not quantified. Ethnicity, marital status, and car color are nominal variables. Chi-square and frequency tables analyze nominal variables.

Ordinal

Ordinal variables represent ordered or ranked data. Their categories have a coherent structure but no regular interval between them. Ordinal variables include Likert scale responses (strongly agree, agree, neutral, disagree, strongly disagree), educational attainment (high school, college, postgraduate), and customer satisfaction ratings (very satisfied, satisfied, neutral, dissatisfied, very dissatisfied) (Muriuki & Mowing, 76).

Interval

Numeric interval variables feature constant intervals between values but no genuine zero point. Positive and negative interval variables exist. Temperature, calendar dates, and standardized test scores are interval variables. Regression and ANOVA can evaluate interval variables.

Ratio

Ratio variables are like interval variables, but their zero point indicates the absence of the observed property. Ratio variables can perform multiplication and division. Height, weight, income, and sales are ratio factors (Ebrahim et al., 233). T-tests, correlation, and regression can assess ratio variables.

Descriptive Statistics

Descriptive statistics summarize data so researchers may understand it. Central tendency measures are descriptive statistics that show a dataset’s average value. Mean, mode and median are the most popular central tendency measures.

Measures of Central Tendency

Mean

The average is derived by summing all values in a dataset and dividing by the number of observations. It measures central value using all data points. However, outliers can greatly affect the mean.

Mode

A dataset’s mode is its most frequent value. It finds the most general category or value. Multimodal datasets have values with the same highest frequency.

Median

The median is the mean value when sorted from lowest to highest. It is beneficial for skewed distributions and ordinal data since it is unaffected by extreme values.

Frequency of Distribution

Research relies on distribution frequency. It analyzes data distribution across values or categories. Researchers can uncover data set patterns, trends, and proportions by evaluating variable or value frequency. Histograms and bar charts show data distribution.

Measures of Variability

Research requires variability measures to quantify data point dispersion within a distribution. Range, variance, and standard deviation quantify variability. The difference between the highest and lowest values gives the range. However, squared deviations from the mean makes the variance and standard deviation more robust.

Inferential Statistics

Inferential statistics uses samples to draw population conclusions. It includes concluding a population from a subset of data. Hypothesis testing and confidence intervals evaluate parameters and determine significance in inferential statistics (Muriuki & Mowing, 76). Using appropriate sampling strategies and statistical tests, researchers can generate population conclusions with minimal data.

Hypothesis Testing

Hypothesis testing evaluates population claims or hypotheses. It involves creating a null and alternative hypothesis, collecting data, and running statistical tests to determine the likelihood of the observed outcomes under the null hypothesis. These tests’ p-values indicate null hypothesis evidence. If the p-value is below a threshold (e.g., 0.05), the null hypothesis is rejected, confirming the alternative hypothesis (Muriuki & Mowing, 76).

Confidence Intervals

The research uses confidence intervals to estimate population parameters. A confidence interval gives a plausible range of values for the true population parameter and a confidence level. A 95% confidence interval means that if the sampling process were repeated many times, 95% of the intervals would include the true population parameter.

Regression Analysis

Regression analysis investigates the relationship between a dependent variable and one or more independent variables. It shows researchers how independent variables affect dependent variables. Researchers can estimate independent variable coefficients, assess significance, and make predictions or conclusions by fitting a regression model to the data (Muriuki & Mowing, 76).

Sampling Designs

Research requires sampling a portion of a larger population to examine and draw conclusions about the whole. Sample designs guarantee that study findings are representative and generalizable.

1. Simple Random Sample

The simple random sample is a popular sampling design. This design gives everyone an equal chance of being chosen. Each sample is selected randomly and independently. Simple random sampling eliminates bias by giving each population member an equal chance of inclusion.

2. Stratified Random Sample

Stratified random sampling divides the population by age, gender, or socioeconomic level. The researcher then randomly selects stratum samples based on population representation. When researching populations with large internal variations, this method ensures that each subgroup is sufficiently represented in the sample.

3. Cluster Sample

Cluster sampling divides the population into natural clusters like schools, neighborhoods, or organizations. Researchers randomly select a selection of clusters and sample all members. Cluster sampling is beneficial when it is hard to get a list of all population members or when direct sampling is expensive.

4. Systematic Sampling

Systematic sampling involves periodic population selection. Starting from a random point, every kth element is selected to form the sample, where k is the sampling interval computed by dividing the population by the required sample size.

5. Nonprobability Sampling

Nonprobability sampling uses nonrandom selection and probability theory. When identifying or reaching the whole population is challenging, or when the researcher prioritizes specific persons or groups, several designs are used.

Validity Testing

Validity assessment determines the correctness and suitability of research measurements and findings. Validity tests have different purposes.

• Face Validity

Face validity is how well a measure looks to measure its intended concept. Researchers or specialists make a subjective evaluation based on their understanding of the construct. Face validity is a simple test of whether a measure “looks like.” It measures what it purports to measure.

• Content Validity

Content validity measures how well a measure covers the construct being measured. Assessing a measure’s items’ relevancy, representativeness, and comprehensiveness. Experts assess content validity by assessing item appropriateness and construct coverage.

• Criterion Validity

The extent to which a measure relates to an external criterion or outcome can be evaluated using criterion validity.

• Construct Validity

Construct validity is whether a measure accurately assesses the theoretical construct or concept. It entails investigating the measure’s correlations with theoretically linked construct variables. Construct validity proves the measure is measuring the target construct.

• Internal Validity

Internal validity measures how well a study draws cause-and-effect findings. It proves that manipulating the independent variable caused practical consequences. Experiments require internal validity.

SECTION THREE: CONSTRUCTING A RELIABLE SURVEY INSTRUMENT

Surveys help gather data and understand occurrences. A dependable survey instrument is needed to ensure the results’ reliability and validity. This essay discusses sample selection, precision, variability, and sample size in instrument construction.

Samples

Survey participants are recruited from a sample. To get trustworthy findings, choose a representative sample of the target population. Consider homogenous and heterogeneous samples.

Homogeneous population

Homogeneous populations have similar traits. A simple random sample may represent a homogeneous population. This method selects population members at random. It represents the population impartially, improving survey findings.

Heterogeneous population

Heterogeneous populations have varied traits. Stratified sampling may work better. Stratified sampling divides the population into subgroups or strata based on attributes and proportionately samples each grouping (Blog, 12). This method ensures subgroup representation, reflecting population variety.

Level of Precision

A reliable survey device requires determining precision. Precision is the allowed margin of error or confidence interval around survey estimates. Precision increases with lower error. The survey’s objective, resources, and accuracy should determine the desired precision.

Degree of Variability

Variability is the population’s variation or dispersion on the survey topic or variables. To maintain precision, more variability necessitates a bigger sample size. Estimating sample size requires understanding variability.

Methods for Determining Sample Size

A reliable survey instrument requires a proper sample size. Calculating sample size has several methods:

The following formula determines sample size for population proportion or percentage estimation:

n = (Z^2*p*q) / E^2.

Where:

Sample size n

Z is the confidence-level Z-score.

Prior information or a pilot study estimates p.

q equals p minus p.

E is the desired error or tolerable precision.

The formula estimates sample size when Z, p, q, and E are entered.

Matching the Statistic to the Variable Type

Question Construction: Ensuring the survey questions are clear, unbiased, and aligned with the research objectives and variables of interest.

Scale Designs for Responses: Select appropriate scale designs (e.g., Likert scales, semantic differentials) to capture the desired level of measurement for the variables being assessed.

Testing for Bias: Assessing and addressing potential biases in survey questions, such as social desirability or response bias, to ensure the responses’ validity.

Matching Questions to Variables: Ensuring that the survey questions effectively capture the constructs or variables of interest and provide meaningful data for analysis.

Matching Questions to Statistics: Select appropriate statistical analyses that align with the measurement scale and characteristics of the variables being assessed.

Limitations of Survey Research: Recognizing and acknowledging the inherent limitations of survey research, such as potential response biases, reliance on self-reported data, and limited causal inference.

CHAPTER THREE. METHODOLOGY

The methodology chapter describes how a research question or problem is investigated. This chapter outlines the research strategy, data gathering, and analysis methodologies. Researchers validate the reliability and credibility of their findings by describing their approach.

Methodology Chapter Components:

Research Design: This section describes the study’s approach. It specifies if the research is exploratory, descriptive, experimental, or a mix. Research design should meet objectives and resolve biases and limits.

Sampling Procedure: This section covers how to choose study participants. It describes the sampling frame, size, and method. To ensure generalizability, researchers should justify their sampling decisions and show that the sample is typical of the target population.

Data Collection Methods: This section describes how the research questions were answered. Surveys, interviews, observations, or experiments may be used.

Data Analysis Approaches: Researchers describe the statistical or qualitative approaches used to interpret the data. Descriptive and inferential statistics, content analysis, thematic analysis, and other methods may be used.

Ethical Considerations: This section describes the research’s ethical principles to protect and care for participants. It addresses informed consent, privacy, secrecy, and conflicts of interest. Researchers must adhere to ethical standards and institutional policies.

Conclusion

The topic of this survey aims to understand the challenges faced by nonprofit organizations in fundraising and community engagement. By conducting survey research, we expect to gain insights into the strategies employed by nonprofit organizations, their effectiveness, and the impact they have on the communities they serve. This information will guide the development of resources and support systems to enhance the sustainability and impact of nonprofit organizations.

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