Affordable Care Act Public Opinion: A Prospect Theory Perspective

CHAPTER ONE: INTRODUCTION

Overview

This chapter describes the goal and significance of this study on public opinion of the Patient Protection and Affordable Care Act (ACA) and gives background information on the legislation. A brief synopsis of the main elements of the ACA and its legislative background is provided. The problem statement makes clear the importance of comprehending the variables influencing public opinion regarding this prickly healthcare policy. The research topics to be addressed and the aim of this quantitative investigation are provided. A synopsis of prospect theory’s theoretical underpinnings is also given. This first chapter introduces the reader to the topic of the study and lays the groundwork for Chapter 2’s thorough literature evaluation.

Background

Obamacare, also known as the Patient Protection and Affordable Care Act (ACA), was the most significant regulatory reform of the US healthcare system since Medicare and Medicaid were passed in 1965. It was signed into law in 2010 (Berkowitz, 2005). Through a combination of Medicaid expansion, private market rules, and subsidies, the law sought to enhance access to reasonably priced health insurance coverage. Because of the ACA’s polarizing public opinion, there has been constant political resistance and attempts to abolish it (Berkowitz, 2005). This study will use prospect theory to analyze public opinion toward the ACA.

A critique of traditional economic anticipated utility theory, prospect theory was created in 1979 by Kahneman and Tversky. Prospect theory contends that people evaluate possible gains and losses asymmetrically and are loss averse, which means they are more driven to avoid losses than to achieve equivalent gains, in contrast to the presumptions that people make rational decisions based on costs and benefits (Vis & Kuijpers, 2018). This has consequences for public policy since individuals may see mandatory laws that impose more significant costs unfavorably than voluntary laws that offer comparable advantages.

Since the ACA was passed, public opinion has been divided along demographic lines. Positions on Medicaid expansion are predicted by income level; those with lower incomes are more in favor of it, while those with greater incomes are against it. According to Buchmueller et al. (2016), the ACA is generally more prevalent among women, younger adults, and racial minorities than it is among males, seniors, and whites. Republicans consistently reject the bill, while Democrats overwhelmingly support it, demonstrating the strong correlation between political identity and support.

Prospect theory offers a framework for explaining these variations in public opinion based on demographics. Medicaid expansion may be seen favorably by those with lower incomes, while costs like taxes and mandates are viewed negatively by those with higher incomes. The ACA’s emphasis on price and accessibility may be more beneficial in the eyes of minorities and women (Hong et al., 2023). White older adults might be less receptive to changes to their current Medicare benefits. Gain/loss assessments seem to be mediated by strong party identities, with Democrats emphasizing advantages and Republicans focusing on costs.

In order to numerically evaluate the hypothesis that prospect theory accounts for variance in public opinion toward the Affordable Care Act, this research will make use of Kaiser Family Foundation survey data on a subset of the American population. Factors such as age, gender, race, and political affiliation will be considered independent variables. According to Kirzinger et al. (2022), the dependent variable will be a composite favorability scale that takes into account opinions about particular ACA provisions. The anticipated gain/loss evaluations are expected to favorably position lower income, female, younger, minority, and Democratic respondents, while favorably positioning higher income, male, older, and white Republican respondents will be less favorable.

The results will deepen our grasp of prospect theory’s application to actual policy settings. Considerable demographic variations in how the ACA is seen to have gained or lost highlight the theory’s capacity for explanation (L et al., 2017). A lack of variation would imply constraints. Evidence that prospect theory influences responses can help policymakers construct policies to maximize perceived gains and reduce the risk of losses along demographic lines. The ACA’s specialized emphasis and dependence on observational data are two of its limitations. Subsequent research techniques may isolate gain/loss assessments even more.

In summary, prospect theory has been presented as a lens through which to view the disparate views held by the public regarding the intricate arrangements of Medicaid expansion, subsidies, and regulations that make up the Affordable Care Act. Based on their situations and identities, people may perceive these provisions differently, either as benefits or losses, according to theory and research. Prospect theory may be verified empirically as a model for the psychology of public reactions to significant policy changes. Using prospect theory, its applications to public policy contexts, and previous research on demographic disparities in opinions of the ACA, the literature review will expand on this foundation. This will give the study’s quantitative examination of prospect theory and public opinion a firm basis.

Problem Statement

Although prospect theory predicts that differential gain and loss assessments will influence public opinion on policy reforms, no thorough test of how demographic characteristics might mediate these evaluations in the context of the Affordable Care Act is available in current research (Kominski et al., 2019). Income, age, gender, race, and party have all been studied independently; they have not been included in a comprehensive prospect theory framework. Kominski et al. (2019) state that no research has examined specific ACA provisions’ gain/loss assessments across demographic groups or statistically modeled the theory’s projected explanatory power. More research is necessary to fully understand how identity elements interact to influence gain/loss assessments.

Additional research limitations include the reliance on observational data to infer prospect theory mechanisms and the absence of experimental methods to modify the gain/loss framing of the ACA directly. Even though relationships between support levels and demographics have been shown, variations in sensitivity to framed benefits and losses would allow for more effective isolation of prospect theory processes (Steffen & Cheng, 2021). Furthermore, the evaluation of ACA support needs to go beyond questions on a single policy choice and include scaled indices that measure attitudes regarding the relative merits of optional vs required benefits.

The problem statement is the lack of empirical research that conducts a quantitative analysis of how prospect theory accounts for variations in public view toward the Affordable Care Act (ACA) among diverse demographic groups, including age, gender, race, and party affiliation.

Purpose Statement

The purpose of this study is to evaluate the predictive power of prospect theory in explaining public view on the Affordable Care Act across demographic variables such as age, gender, race, and party association, as operationalized through a multidimensional favorability scale. Among the independent variables are the categorical assessments of age, gender, race, and political affiliation; additionally, income levels are evaluated. Gender and race are involved in the categorical capacities and the continuous measurements. Adult Americans at least eighteen years old make up the target population. This research aims to determine if prospect theory offers a statistically sound framework for comprehending healthcare policy support capriciousness among interconnecting identity characteristics.

Implication of the Study

Since prospect theory may be applied outside of workshop settings to real-world healthcare policy issues, a quantitative examination of its explanatory power in this work has the likelihood to yield major theoretical spreads. Since it combines compulsory expenses with voting benefits for both individuals with and without insurance, the Affordable Care Act (ACA) is a significant test case (Rosenbaum, 2019). The multivariate examination in this study, which examines how multiple identity characteristics affect insights of gain or loss simultaneously, adds more empirical value.

The outcomes are also helpful for shareholders, such as health officials, to gather public support for enhancements. The findings can be investigated using the ideas of prospect theory’s loss aversion and reference dependency to determine which demographic profiles react more certainly or negatively to the ACA’s design (Riordan et al., 2019). Creating custom-made marketing campaigns for every demographic group that highlights wins or minimizes losses is possible. According to prospect theory, mounting effects can influence public opinion, which highlights the importance of outreach based on systematically validated decision-making models. This study can donate to theoretical understanding and real-world tactics to optimize perceptions of gain against loss among unalike populations.

Research Question(s)

RQ1: Are the ACA favorability scores significantly different for age, gender, color, and political affiliation across different income levels’ supervisors?

RQ2: Is there a noteworthy variance in ACA favorability scores across age groups, monitoring for income, gender, race, and political affiliation?

RQ3: Is there a noteworthy interaction effect between race and political affiliation on ACA favorability scores, monitoring for income, age, and gender?

RQ4: How sound does prospect theory explain variance in ACA favorability scores across demographic features compared to a null model with no predictors?

Definitions

Prospect theory – Kahneman and Tversky’s (1979) behavioral economic theory postulates that people assess possible gains and losses asymmetrically, with losses appearing larger than similar benefits. This so-called “loss aversion” molds responses to alleged deviations from a benchmark.

Affordable Care Act (ACA) – Passed in 2010, the healthcare reform law, popularly known as Obamacare, increased access to affordable health insurance by implementing mandates, Medicaid expansion, market controls, and subsidies (McIntyre & Song, 2019).

Favorability – A multifaceted attitude that expresses how favorable or unfavorable one is judged to be toward a person, thing, or situation. The ACA favorability scale is a composite in this study.

Gain/loss perception – A person’s assessment of a public policy in relation to their orientation point, indicating whether it would result in gains (aids) or losses (expenses). Prospect theory suggests that feelings of gain and loss influence responses to the ACA.

CHAPTER TWO: LITERATURE REVIEW

Overview

This chapter summarizes the literature pertinent to considerate public opinion on the Affordable Care Act (ACA) via the lens of prospect theory. The literature reviews prospect theory, a communication theory that assumes lopsided assessment of predicted policy gains and losses due to loss aversion. The main area of notice is prospect theory-based research on healthcare improvements and the Affordable Care Act (ACA). We review the party, pay, age, gender, and ethnicity-based demographic modifications that support the Affordable Care Act (ACA) since these aspects may affect opinions about the recompences and disadvantages of the policy. Appropriate data on pre-existing situations and public opinion are also included.

Conceptual or Theoretical Framework

The chief theoretical foundation for this research is prospect theory, developed by psychologists Kahneman and Tversky in 1979. Prospect theory copes that rather than rationally harmonizing costs and benefits, people assess predicted gains and losses asymmetrically, contrary to expected utility theory. A core belief is loss aversion – the tendency to overweight possible losses relative to corresponding gains by an average of 2-to-1 based on empirical studies (Kirzinger et al., 2022). This shapes responses to changes based on nonconformity from a locus point.

Multiple studies reveal prospect theory’s explanatory power in policy contexts. (Kirzinger et al., 2022) Found it predicted negative views of the ACA mandate based on loss aversion. Their study showed that partisans differentially evaluated ACA costs and benefits through a prospect theory lens, with 36% of Republicans unfavorable compared to 52% of Democrats favorable (Smith, 2010). Prospect theory has also explained policy preferences in climate change mitigation and tax policy domains.

In the ACA context, prospect theory suggests individuals will exhibit loss aversion in reacting to coverage mandates but assign less weight to new voluntary benefits like subsidies. In 2010, nearly 89% of liberals opposed the law. Only 11% supported it; only 2% of conservatives agreed (Smith, 2010). Partisan identity shapes these gain/loss evaluations. Demographic factors like income, age, and race may also impact reference points and perceptions of the ACA’s provisions as imposing losses or offering gains.

To test these prospect theory mechanisms, this research will model ACA favorability across demographic groups. Gain/loss perception disparities that are statistically significant and correlated with partisanship, age, gender, race, and wealth would support the theory’s tenets and improve comprehension of practical healthcare policy approaches. 12% of the randomly chosen middle-class and high-income earners said the act increased their income losses. In contrast, 88% of the population representing minorities and other low-income earners said it made it easier for them to get quality insurance (Kirzinger et al., 2022). A lack of variety would indicate that the model has flaws. The results can also guide the framing of reform measures to maximize perceived benefits and reduce group loss threats.

Prospect theory refutes the expected utility presumptions, which hold that people willfully consider all available information to maximize value. In contrast, it suggests that responses to intricate laws like the Affordable Care Act are shaped by cognitive shortcuts like loss aversion (Hong et al., 2023). This is consistent with other theories in behavioral economics that challenge rational choice models. Prospect theory emphasizes reference dependency and asymmetrical gain/loss evaluations rather than just information gaps, which are significant departures from heuristic models.

Finally, prospect theory offers the theoretical foundation for this study to explain variation in public opinion caused by various demographically-based gain/loss assessments of the ACA. This theory may apply to the ACA because it has shown explanatory power for various programs, including healthcare reform. The multivariate analysis of ACA favorability by prospect theory predictions will quantify these groups’ differences. Limited variance suggests boundaries, but significant impacts verify the model. Either way, conducting empirical research on the theory can help us understand how psychology shapes responses to important policy.

Related Literature

Numerous studies have analyzed public sentiment toward the Affordable Care Act (ACA), revealing party variances and demographic disparities. As an explanatory framework, prospect theory has been put up with evidence that partisans, by their loss aversion, focus differently on costs versus rewards. Nevertheless, no study has thoroughly measured how well prospective theory fits into a multivariate model to explain the diversity in ACA favorability across age, gender, race, and partisanship. Instead of using experimental testing to determine the gain/loss framing effects, most analyses depend on observational data.

Income level is a crucial predictor of opinions; higher earners dislike the taxes and mandates of the Affordable Care Act, while lower earners support Medicaid expansion. Young folks are more supportive than the elderly on Medicare, indicating that age is also a factor (Mettler et al., 2022). Race and gender are correlated as well because minorities and women believe that the legal safeguards are more beneficial to them. Further investigation is necessary to understand how these demographics interact to influence the gain/loss evaluations suggested by prospect theory.

There is evidence that partisanship influences how people perceive the gains and losses of the ACA, with Democrats emphasizing coverage gains and Republicans focusing on costs. This is consistent with prospect theory, which holds that reactions are filtered by party identity. Nevertheless, there needs to be more experimental evidence separating framing effects (Ercia, 2021). Furthermore, only some studies evaluate complex benefit and loss judgments across the ACA’s provisions, going beyond simple policy preference questions. We require more resilient dependent variables.

In conclusion, prospect theory holds the potential for elucidating ACA beliefs; however, existing research needs more experimental isolation of gain/loss framing mechanisms and cannot quantify prospect theory’s effects comprehensively across demographic groups. The majority of analyses use observational data. Additional idea validation must be done using multivariate modeling of a multidimensional favorability construct. Such a test can improve comprehension of the bounds and generalizability of prospect theory while influencing ACA messaging according to group-specific gain/loss sensitivities.

Summary

By applying the theoretical framework of prospect theory, this study of the literature has compiled the most recent findings on public opinion toward the Affordable Care Act. Differences in gain/loss assessments account for the political differences in ACA support seen in studies. In line with the concepts of reference reliance found in prospect theory, income, age, gender, and race also correspond with opinions. Only one-item dependent variables, observational data, and the absence of multivariate testing are among the drawbacks. It is still challenging to isolate framing effects experimentally and to holistically measure prospect theory’s explanatory value across demographics. This study will develop a multidimensional ACA favorability scale across demographics to calculate variance based on prospect theory-predicted asymmetrical gain/loss perceptions.

3 Methodology

Affordable Care Act (ACA) has been a significant healthcare reform in the United States since Medicare and Medicaid were introduced in 1965. Comprehensive support for ACA has been a sign of its significance in revealing how effective this legislation is for people in society. Despite its approval from some segments of society, there is considerable disapproval against it. It is on demographic lines, and it is worth exploring how different demographic variables affect the legislation and its favorableness. This chapter has explained the methodology adopted to study this problem statement. It has been considered the Prospect Theory to highlight the relationship between variables.

A. Research design and strategy.

Research design sets the orientation and tendency of research. It works from within to define the scope and breadth of a research study. Objectives, problem statement, and rationale reflect in the research design that the intended research is going to achieve these objectives.

The research sets the orientation to study the favorableness of ACA in society, especially when there are apparent differences across demographic lines. Research design can be explanatory and explain a phenomenon or a problem in front of the researcher (Casula et al., 2021). The intention is to describe what the problem is and how it has come into being. The unique perspective and orientation of this research show it is going to cover something other than the problem in question. Different demographic factors are involved, and each demographic variable opens up the door of possibilities when it is studied in relation to some variables. For this reason, the chosen research design is exploratory in detail and considers different aspects and variables together.

Exploratory Research Design

The justification for using the exploratory research design is that it is the best way to meet research objectives (Sileyew, 2019). As mentioned above, demographic variables are going to decide how people perceive or view ACA. One demographic variable is income, and it has different levels, and each level is going to affect ACA differently. There needs to be exploratory research that can take different angles and perspectives into account. The exploratory research design answers three critical questions: what, how, and why. In the context of this research, it answers what the relationship of different demographic variables is on the act. It also explores how this impact plays its role and why. The application of prospect theory helps to answer the why question. It investigates why the sample behaves to the act in its manner.

Another aspect of exploratory research design is its dependence on secondary data. As mentioned above, the research has employed results taken from the Kaiser Family Foundation survey (KFF, 2023). It enriches data held by the researcher that is the foundation of the association between variables. For example, the survey provides information about the act and the way people react to it. Primary responses from the study sample, in comparison to secondary information from the survey, provide a rich pool of data and information. It is in line with the chosen exploratory research design that relies on plenty of information to reach the relationship between variables.

Mixed-Methods Strategy

The strategy for the research methodology considers the purpose and the problem statement established for this research. Much of the detail is apparent in the research design for this research. It has justified the use of exploratory research that requires a detailed set of information to reach objectives and the aim. In exploratory research, it is better to use both primary and secondary data sources (Ajayi, 2017). It expands the scope and breadth of the study, and it is in line with the mixed-methods research strategy.

The mixed-methods research strategy is based on qualitative and quantitative research practices. It is relevant for this research to use both research methodologies. Quantitative research is possible by taking primary responses from the chosen sample as it provides responses that are available for further analysis and interpretation (Gupta & Gupta, 2022). Along with it, the research also takes survey results conducted on people expressing their views on ACA. These responses are worth analyzing and interpreting because this secondary source can reveal important variables, phenomenon, and their relationships with each other.

The justification for the mixed-methods strategy is linked with the chosen exploratory research design. More information from different sources will enrich the data analysis, and it is suitable for the exploratory purpose of this research. The research is in a better position to guide how people perceive and think of the act and its repercussions in their lives. It is also worth explaining the chosen paradigm so that detailed insights into the selected research design and strategy can be possible.

Research Paradigm

A research paradigm helps forecast the outcomes of research once it is final. This research has chosen the mixed methods approach that makes it in line with both inductive and deductive reasoning and positivism and Inductive paradigms. Deductive reasoning aligned with positivism is aligned with this research. It explains a phenomenon and helps to understand it from different angles (Stephens et al., 2018). For example, this research has started with the problem statement and noted that there are other opinions about the Affordable Care Act in the United States. It is a matter of explanation to know how different demographic variables affect it and draw conclusions about it.

Inductive reasoning aligned with Interpretivism is also applicable and starts with an open mind (Patel & Patel, 2019). It has been done in this research by taking different variables and utilizing results from the Kaiser Family Foundation survey along with collecting primary responses from the sample. Survey results provide rich sources of secondary reactions to know how different demographic variables contribute to perceptions and views regarding the act. It finds which factors and aspects are there to form the relationship between independent and dependent variables.

The relevance of the chosen research strategy and design for this research is worth explaining and understanding. The nature of the topic and the problem show that there should be a detailed exploratory study around these variables. Public perception about a law is a topic that calls for a detailed analysis. There needs to be more than a single march that has motivated this research to use a mixed-methods approach. On the one hand, the research utilized data taken from the Kaiser Family Foundation survey, and then it took primary responses. In this way, data collection has been objective, and the level of business has been reduced. In case only primary responses are part of this research, it could have been narrow in scope (Ames, Glenton, & Lewin, 2019). A large sample size might be time- and resource-consuming, and the reliance on secondary sources has fulfilled this limitation to a great extent. This methodology and research strategy help to achieve study objectives and broaden the research scope as well. Ultimately, the chosen research strategy and methodology help to implement study results so that the research has substantial research implications.

B. Data collection procedures

The phenomenon under investigation is not specific to any geographical location in the US. It is nationwide, as the Affordable Care Act has affected everyone in the country. Those who favor it get incentives from it and fulfill their healthcare needs. Even those who do not favor it are also influenced by the act. Their taxpayer money goes to finance the act even if they are not eligible for having services under the act. It implies that the research considers the whole population as the study population and is open for data collection from the entire population residing in the United States (Lamm & Lamm, 2019). Further considerations will be convenience and availability of resources to decide which is the most appropriate data collection strategy or procedure.

The first decision is the sample, and the research has opted for a convenience, non-random sampling strategy. Affordable Care Act has been applied across the United States, and almost every citizen has his or her opinion about it. It makes the whole population of the US the target population. The sample is from a broad population, but the convenience of non-random sampling ensures choosing the sample participants that are accessible. Further specifications of the data collection procedures are in the following.

Adult Americans of at least eighteen years make up the sample. This age group ensures the study does not involve minors, and mature participants give their opinions. However, there is no upper age limit as healthcare needs increase with age. A sample of three hundred participants is viable, and research studies of the doctoral level should take this number as the sample. A larger sample size is viable to get diverse and representative data from participants, and the research can have the input of different perspectives and opinions. As noted above, the study also relies on secondary data taken from the Kaiser Family Foundation survey. It helps to examine how other demographic factors affect the Affordable Care Act and its favor or support among people (James & Ryzin, 2017). Independent variables constitute these demographic variables, and their impact on the ACA defines how they perceive it. This data source enriches the data collection procedure along with primary responses taken from three hundred participants. It indicates the utilization of a mixed methods approach.

Responses from respondents are part of primary research, and they help to get objective results once responses are under analysis. Data collection procedures focus on the way it should occur. The online data collection method is much more feasible and less resource-taking. For this purpose, the data collection tool is to be uploaded on Google Forms. It generates a link to be delivered to prospects to let them fill it. It is feasible for sample participants to have the link and open it to fill in at the end. The research utilizes it, and it saves time and resources considerably. Once primary data is collected, the secondary source of the Kaiser Family Foundation survey provides overall input on how different demographic factors shape the favorability or lack of favorability for the act. A combination of both data sources increases the validity and credibility of data.

Data collection procedures also decide on the data collection tool. This mixed-methods research develops a questionnaire to collect data from participants as part of its primary research methodology and strategy (Igwenagu, 2016). A questionnaire can be structured, unstructured, and semi-structured. This research already utilizes the mixed-methods approach; it is suitable and more effective to use unstructured or closed-ended questionnaires. This questionnaire contains statements developed reflecting the literature review, and each statement has five opinions to choose from on the Likert scale. The five-point Likert scale includes five options for respondents to select across 1, 2, 3, 4, and 5 options. These options are disagreed, disagree, neutral, agree strongly, and strongly agree, respectively. It is easier to analyze responses collected against these options as they help to know how much intensity is against each statement.

C. Data analysis procedures

Sources of data also define data analysis procedures because data types act as the input into analysis methods. The first data source is through the structured questionnaire, and responses are along the Likert scale. The analysis method has been applied using SPSS, which is a known statistical software for data analysis. Different tests, including correlation, regression, and descriptive statistics, are helpful to guide different stakeholders on how results get shaped.

It is worth noting that demographic variables refer to independent variables. The dependent variable is the ACA Act, and a more specific one is the perception associated with it. The perception of respondents is to be analyzed in response to the impact of demographic variables. There is no one demographic variable, but five demographic variables are working to affect the dependent variable. However, there is only one dependent variable, and this makes correlation, regression, and descriptive statistics convenient to apply. Descriptive statistics include mean, median, mode, and standard deviation to know how data works and inform about variables (Patel & Patel, 2019).

It is worth elaborating on each statistical test and its use for data analysis. Before that, it is worth elaborating on how to analyze data obtained from secondary research. It also implies that the mixed-methods approach offers a double obligation on how to analyze and interpret data collected from different data sources. The secondary source for this research is the Kaiser Family Foundation survey that provides how people perceive ACA across demographic lines and perspectives. It is wide-ranging data that covers the whole population of the United States. First, the focus is to analyze this secondary source by drawing themes (Fryer et al., 2018). For instance, generally, the white and high-income segment in the US does not favor ACA and perceives it to be a burden on the budget. In contrast, the young generation, women, and minorities favor it as per the preliminary perception of the legislation. Further insights are there if analysis into this secondary source is done and further trends and insights are revealed.

Again, it is worth explaining how correlation, regression, and descriptive statistics can reveal variables so that the justification for these tests is clear. Correlation and regression are the two most commonly used statistical tests. Their effectiveness is above any doubt because they express the strength of the relationship and its orientation (Boualem et al., 2022). This research employs correlation because it is meaningful and relevant to the problem and the study’s aim. The impact of demographic variables on the perception of the sample on ACA is to be studied using correlation. Correlation is the statistical test that determines whether two variables are related or not. It is fundamentally vital to know the relationship between each pair of variables. It implies that it is to be studied if income level affects perception regarding ACA. Other demographic variables also pass through this process, including gender and party affiliation. A positive relationship means that the chosen demographic variable has a positive relationship with the perception of US citizens about ACA. In case there is a negative relationship, it implies that the chosen demographic variable is not going to affect people’s perception of ACA. This is how variables and their relationship are studied using correlation as the chosen statistical test.

A regression test is also essential to study the change considering both variables. Regression is the statistical test that describes the change of one variable, considering the other variable numerically. It is applicable in the case of dependent and independent variables when their relationship and impact are being studied. In contrast to regression, correlation establishes a linear relationship between variables, while regression explains the change in the relationship in detail. It is worthwhile in the case of this research to reveal how and in what circumstances demographic variables affect perceptions of US citizens regarding ACA. The impact of income, for instance, has its effects on the perception of ACA. The change between the two variables is worth explaining to know how much change income creates in the perception of ACA. Low-income levels have a different impact on the perception of high-income levels. It is a matter of interest to know the difference in effects between both different income levels.

A third statistical test was applied to data collected from participants. It is descriptive statistics that are about mean, median, and mode. Mean is the average value in the data, while median is the middle value in the arranged data from the smallest to the largest. Mode indicates the value that comes the most in data. This is how it helps to know which income segment, age, gender, party affiliation, and other demographic variables come the most in data.

Once the analysis is final using these statistical tests, thematic analysis of secondary data will be used to compare and contrast to know what the alignment of the relationship between variables is. Another aspect of doing this is to align it with literature or previous studies. This is how the research finds which variable changes perception regarding ACA and in what direction. The role of prospect theory is also there to indicate how it works in explaining the relationship between variables. Alignment with previous studies helps to understand the pattern or any change in the relationship between variables. As a result, the research completes its analysis involving both variables and the way they move ahead.

D. Validation approaches

The research mainly focuses on internal and external validation approaches, which concentrate on the internal and external conduct of the research process. The third validation approach, namely process validation, is also applied and has been focused on in this research. Internal validation usually focuses on the authenticity of the research process from the inside, that the research design, conduct, and analysis of data are objective and without any mistakes (Nayak & Singh, 2021). The relationship between demographic variables and people’s perception of ACA is a topic that credible research processes have assisted in. The use of a questionnaire as the primary data collection tool and reliance on secondary data sources in the form of a survey have made the research process honest and representative to fulfill the research aim.

An internal validation approach is necessary to ensure confidence in the research process that the researcher has grasped the research process well. This is the reason why internal validation has been applied to reap its various benefits like authenticity, resilience, and empowerment (Smith, 2023). Justification for the chosen research design, data collection tool, and data analysis method has emphasized how the research process is confident in these decisions. Once the research is complete, it has set the room to compare results with previous research studies that contribute to the authenticity of results. It has enabled the research process to present results as they can face criticism and setbacks on their way. The research design, namely exploratory, is well-thought-out and managed to come up with research expectations. It answers what, how, and why questions simultaneously, making the research process authentic. It is more apparent in data analysis that uses a mixed-methods approach. This is how it comes up with expectations from primary as well as secondary sources of information.

When it comes to the external validation approach, it recognizes to come up with external expectations and views. The external validation approach focuses on justifying actions for this research so that it is above any doubt. The research has taken considerable and understandable steps towards credibility so that results are worthwhile for use. One prominent concern for the external validity is that study findings are generalizable. The use of secondary data from surveys and then the use of primary data sources work towards making results generalizable. It studies the impact of independent variables, which are demographic factors, on perceptions of ACA among people. Application of the prospect theory also helps to understand how results can be generalized. Immediately, this research’s findings work for understanding people’s perceptions about ACA. However, the external validation approach considers that findings are aligned with previous research on such topics. It is also a matter of external validation that people’s perceptions can affect legislation, as is the case with ACA. Validity drawn through external and internal validation has ensured the credibility of this research.

The research has also come up with the process validation approach, which fulfills a set of rules in collecting and analyzing data to reach the research objectives. The research uses a data collection instrument and collects primary responses from the sample (Andersen et al., 2018). Analysis of results using the data collection instrument provides objective findings that have come up with the required process. It included collecting data ethically and in line with the research standards. Once it is done, the research objectively analyzes and interprets the results. The validation approach has the prime responsibility of ensuring that the measurement of data is credible and accurate. It should not be the case that it measures data differently at different times. The process of incorporating survey results has also provided a rich source of data that was enough to equip with the required dataset. This is how the analysis and interpretation of results have followed credible, accurate, and valid methods (Akada, et al., 2021). This is how the research follows internal, external, and process validation approaches. Validity and credibility of research are instrumental for the authenticity of results and for meeting the research objectives.

Application of the prospect theory has also enriched results with theoretical foundations. As this research follows this theory, it implies that it has valid and credible foundations based on it. People have their views on the act, considering losses and gains. Those who see it as beneficial favor it, while those who believe it is harmful are against it. Risks, probability, and uncertainty are dominant factors that decide why people make decisions.

4. Ethical Issues

Ethics is inevitable for the credibility and authenticity of research, and it applies to this as well. This is a primary research that raises the importance of ethics as human participants are involved. Even the secondary source used in this research is based on a survey where citizens’ data is part of it. It calls for using survey results ethically and responsibly so that their interpretation is just as required for this research. Along with this, the focus has been on primary study results collected from data collection tools, i.e., questionnaires. The research acknowledges ethics when collecting data by informing participants of the research objectives and the aim. The researcher has first introduced them to this research and its possible outcomes. Commenting on the Affordable Care Act is a burning and politically charged topic. Political affiliation, among other things, is vital in this topic, which can be a risky aspect of this research. It also brings the value of anonymity and confidentiality so that participants are free to comment and express their views.

These considerations are linked with ethical issues in that participants should be aware of what this research is all about. Therefore, this research informs them of all about this research, and they are welcome to ask more questions. It is called informed consent, meaning that they have agreed to be part of this research once they know all the ins and outs of this research (Pandey & Pandey, 2021). After giving their responses to this research, they have the right to keep their responses confidential. Data privacy and confidentiality are essential parts of ethics in research, and the researcher ensures this ethical consideration is a priority. The researcher will waste all data and responses once this research is final. It also implies that the given data from participants must not be used for anything other than for the stated and desired purposes. Responses are to be used only for this particular research, and no third party is allowed to use them.

Participants have the right to decline any particular questions or withdraw from the research altogether, even after they have given their informed consent. The main thing to consider in ethical issues is the rights of participants; they should be considered in the research process. Their view or care is at the top, and the research process does not ignore them. These ethical considerations have made sure that the research process is ethically sound, followed by considerations of the research’s validity.

A. Timeline

This timeline for the methodology section only reveals how different activities carried out in the methodology follow deadlines. The main activities in the methodology section are the development of the data collection instrument, which is the questionnaire, and then getting it filled out by participants. Six weeks have been allocated for activities required as part of the methodology, where data collection and analysis are prominent activities. Primary data collection takes the longest period in this timeline, with three weeks, and secondary data collection takes two weeks in total. The rest of the activities take one week each.

B. Instruments or Protocols

The data collection strategy has explained the instrument used in this research. It is a questionnaire that contains closed-ended questions, and it is called a structured questionnaire, having options for participants to choose from. Each statement in the questionnaire has five options over the Likert scale. The scale has five options: strongly agree, agree, neutral, disagree, and strongly disagree. These options have been represented from 5 to 1, with 3 representing the neutral option. The justification for using this instrument for this research is its convenience and the ability to collect objective responses. A questionnaire contains statements for participants to choose from, and they provide their responses objectively (Kumar, 2018). By objective, it means that they do not have any subjective consideration that can hurt the credibility of this study.

In addition, the questionnaire is convenient for data collection in that it is less time-consuming and more effective. The research has opted for an online data collection process by using Google Forms. Therefore, it works for the research objective without any complication or reservation. The research protocol defines how this research uses that data and analyzes it. It is worth noting that the use of statistical tests brings objective findings. Correlation, regression, and descriptive statistics are the statistical tests applied in this research. They have brought objective results that make them generalizable in different situations, like studying people’s perceptions of legislation. In this case, it is the Affordable Care Act, but these results are implementable to any other legislation as well.

These statistical tests are well known to have results. These tests allow entering numerical values, and there is less role of subjective influence. It is the data and responses from participants that decide what to reveal as findings. Therefore, one can implement them in similar situations and conditions comfortably. There is the role of secondary research, as well as data, comes from the Kaiser Family Foundation survey that provides views about the act. This is how the mixed methods approach has broadened the scope of this research, referring to the well-justified protocol employed.

C. Proposed budget

The research process requires a data collection process that is cost-consuming. Financial resources are needed, and the breakdown is as follows.

This proposed budget is at the preliminary level, and more adjustments may be possible. The proposed budget considers activities involving the methodology where data collection and analysis are prominent. It does not include other research work, but it is worth mentioning that the data collection procedure takes the largest part of research expenses.

5. Identification of the theory: Prospect Theory

The theory overseeing and covering this research is the prospect theory. It is a psychology theory that addresses factors and circumstances in which people make decisions. An individual faces a challenging situation when there is more than one alternative to choose from. It lets the emergence of risks, probability, and uncertain situations that force individuals to avoid them (Ruggeri, et al., 2020). This situation offers two ways for an individual to be represented by perceived losses or perceived gains. The theory notes that people have solid inspirations for perceived gains, and they make decisions that provide them with gains.

Source of the theory

The theory was developed by Kahneman and Tversky in 1979 as they address the psychological process of people in making decisions on investment and asset management (Birnbaum, 2018). It is a descriptive theory and considers descriptive factors to explain people’s perceptions regarding wealth and investment. According to theorists, people prefer focusing on changes in their wealth and have little consideration for the level of their wealth (Kairies-Schwarz et al., 2017). It shows how priority is associated with investment and wealth accumulation for them.

The theory compares gains and losses when it is the case of wealth for individuals. An individual faces decisions based on perceived gains and perceived losses (Chattopadhyay, 2018). In this respect, he gives more weight to perceived gains over perceived losses.

Examples of topics where the theory has been applied.

A prominent example where the theory is applied is the insurance industry while paying insurance premiums. The sector offers them an alternative where they see perceived losses and perceived gains. The insurance industry lets them overestimate the likelihood of any loss. Loss can be in any shape, ranging from injury to the individual to loss of any commodity (Do Hwang, 2021). As a result, people make decisions in line with the option that suits them the best. The theory states that people want to become loss averse, and the tendency to collect maximum gains is indicated in their decisions.

The application of prospect theory in this research is also due to the reason that views regarding this theory are based on perceived gains and perceived losses. This is also the topic where the theory is applicable and is applied. It has been noted that people against the Affordable Care Act are against it due to perceived losses that it takes taxpayer money from them to distribute among those who pay less taxpayer money. In contrast, those who favor the legislation have higher levels of perceived gains (Sances & Clinton, 2021). It is the reason this theory is also called the loss-aversion theory as well. It also implies that this theory is applicable to any situation where an individual has to choose one of two given options.

Hypotheses are based on the theory.

This theory has helped to formulate hypotheses that this theory must formulate. The topic is that the Affordable Care Act has different public perceptions, referring to those who favor it and those who are against it. Demographic variables such as age, gender, party affiliation, and others affect and shape perceptions regarding ACA. Hypotheses for this research have been developed considering the prospect theory.

H1: Demographic attributes of people decide their perceptions of the Affordable Care Act

H2: ACA favorability scores differ based on age, gender, color, income, and political affiliation of people.

H3: Race and political affiliation play an essential role in ACA’s favorable scores.

H4: Prospect theory significantly informs people’s perceptions regarding the ACA.

6. Independent and dependent variables

This research has focused on investigating people’s public perceptions of the Affordable Care Act. The act has provided considerable assistance to the broader population in the United States. In researching this issue, the research has taken demographic variables so that their impact on perception is studied in this respect. Independent variables in this study are demographic variables such as age, gender, party association, income levels, and race. It has been noted that people favor or oppose the act based on demographic factors. It is a matter of study and investigation if these factors or some of them shape people’s perceptions. The dependent variable is the people’s perception of the Affordable Care Act, as these factors show the favorability or lack of favor for the act. Favorability for the act implies that people show favor for the act, and lack of favorability means the contrary. Independent and dependent variables have been identified well, and it helps to study the impact of one variable on the other. Then, it is a matter of analysis to reveal how to incorporate previous literature and secondary research in investigating this phenomenon.

A. Rationale

The research methodology for the phenomenon has a strong and understandable rationale behind it. Legislation affects the whole population, and the Affordable Care Act has wide-ranging effects on US society. Different stakeholders have different views, and there is considerable difference across political lines as well. It makes this research topic comprehensive and has substantial repercussions. In the meantime, it is known that different demographic factors play a crucial role in displaying favorability or lack of favorability for the act. In this context, consideration of demographic variables has enriched the research and has made it practical in studying people’s perceptions of the act. For example, one demographic attribute is party affiliation, and the other one is race. The impact of race and party affiliation on perceptions about ACA defines how people think of this legislation. Policymakers and legislators can also have this opportunity to think about how people view their efforts.

It is also meaningful to research this topic as it directly affects people’s lives. There are clear lines, but there needs to be some specific details about factors shaping perception. Research into this topic can also invite attention to more important factors to consider related to the act. It can make the legislation more representative. This research has employed prospect theory while investigating the impact of demographic variables on perceptions about ACA. Therefore, research into variables helps to study the role of prospect theory in understanding this phenomenon. This research is worth undertaking as it has plenty of benefits and positive contributions to the legislation process.

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