The purpose of this study is to determine the association between asthma and smoking status among adult African immigrants in California. I intend to use a quantitative correlation approach to explore the association between asthma and smoking status and investigate the influence of selected demographic variables (age, sex, years since immigration, marital status, alcohol use, education level, income level, and employment status) on this same relationship.
The independent variable will be smoking status, and the dependent variable will be asthma. The hypothesis states that there is an association between asthma and smoking status among African immigrants living in California. The findings of this proposed research could help to fill a research gap, which exists because previous researchers have not extensively explored the relationship between the aforementioned variables among African immigrants in California.
This section contains five parts or sub-sections. The first part is the research design and rationale, which explains the research variables and their connection to the research design. The second part contains details of the methodology used in the research. In this sub-section, the information about the target population, sampling procedures, power analysis, instrumentation, operationalization of constructs, data management, data analysis, procedures for recruitment, participation, and data collection are outlined. The third part of this section outlines the threats to validity, while the fourth part outlines the ethical procedures governing the research process. The last part of this section is a summary of the main tenets of the section.
Research Design and Rationale
As mentioned in the previous section, the purpose of this research is to determine the association between asthma and smoking status among adult African immigrants in California. The dependent variable is asthma status, while the independent variable is smoking status. The moderating variables are age, sex, years since immigration, marital status, alcohol use, education level, income level, and employment status.
The research design for this study is the quantitative correlation approach. This design is often used to explore the association between two or more variables in research (Armijo-Olivo, Stiles, Hagen, Biondo, & Cummings, 2012). There are two main variables (asthma and smoking) in this study, as seen from the research question, which states, “What is the association between asthma and smoking status among adult African immigrants in California?” There is an association between the design and this research question because the variables stated in the latter are quantitative (measurable).
For example, asthma status is often measured in quantitative metrics, and smoking status is also measured quantitatively. Furthermore, the California Health Interview Survey (CHIS) dataset used in this study measures the two variables in numbers (quantitative). These factors limit the application of the qualitative technique, which measures subjective outcomes. Collectively, these factors explain the justification for using a quantitative approach. The use of the correlation design is also appropriate for this study because it measures two or more variables (Chan et al., 2013).
In this study, I seek to explore two variables, which are smoking and asthma status. Indeed, the research design has a connection to the research question. The use of the quantitative correlation approach comes with limited time and resource constraints because the relationship under investigation (the association between asthma and smoking status) usually involves a deep analysis of data and a careful evaluation of demographic data to understand the relationship (Hassan et al., 2015).
However, the use of secondary research data to investigate the same relationship has alleviated this problem and the time taken to explore the association between smoking and asthma is small. Furthermore, since secondary data is used in this research, there are limited resources needed to find out the association between the dependent and independent variables because the data is already published and does not require the researcher to use additional resources to get it.
The quantitative correlation design chosen for this study is consistent with other research designs capable of advancing knowledge in the health care practice because it opens up abundant opportunities for understanding the association between asthma and smoking status for future researchers interested in exploring the association further (Hoffmann, Bennett, & Del Mar, 2013). However, it is important to point out that the research design is a form of descriptive approach and does not necessarily explain which of the variables studied influences the other.
In this regard, it provides a good starting point for investigating the relationship between asthma and smoking by allowing researchers to understand the strength and direction of the association between the two variables without necessarily explaining the details surrounding the causation. Thus, future researchers can narrow the findings down to understand the intricate details surrounding the causation, experimentally or otherwise.
Methodology of Research
There are inadequate research studies that have investigated the association between asthma and smoking among adult African immigrants. Based on this gap, the target population for this research is adult African immigrants in California. This target population comprises of men and women above 18 years of age. Additionally, they must have originally emigrated from Africa and assume the status of first generation immigrants living and possibly working within California, USA.
I will use 11 years CHIS secondary dataset (2005-2015) yielding a sample size of 1,130 respondents. I discern no issues with the internal validity as the California Health Information Survey design and data collection procedure is the same for all the years (2005-2015). I estimate that this sample size is large enough to make inferences about the association between asthma and smoking status among adult African immigrants in California because the CHIS 2005-2015 database includes 12,948 Blacks/African American respondents (UCLA, 2017). Thus, using a sample of 12,948 people, a target population of 1,130 is estimated to be around 9% of the total population sample in the CHIS database.
This percentage is within the sample size requirements outlined in most social research studies needed to make inferences about statistically significant relationships (Faber & Fonseca, 2014). I used various sample size calculators (Creative Research Systems, Raosoft, Survey Monkey, GPower, and National Statistic Service) for my target population and yielded no more than 400 respondents. Thus, an actual sample size of 1,130 for my target population is three times the minimum required sample size.
Sampling and Sampling Procedures used to Collect Data
The main source of data and information for this study is the CHIS database. Since my main source of information is attributed to the source of secondary data, the sampling strategy mirrors the same plan present in the original secondary data. The CHIS 2005-2015 dataset was developed through a random sampling technique where respondents had an equal chance of being selected for the study (UCLA, 2017).
This sampling strategy is often lauded for reducing selection bias and improving the reliability of the associated findings. Additionally, the CHIS dataset was developed by randomly selecting one adult respondent in each household that chose to participate in the study. The sampling strategy employed in the study was designed to meet two main goals. The first one was to provide local estimates of population-based health data for comparison across different counties in California, while the second one was to provide statewide population-based health data for all the ethnic and racial groups in California (UCLA, 2017).
The sampling strategy adopted in the CHIS dataset was drawn from the dual-frame random-digit-dial (RDD) method, which included a sample of the respondents’ views using telephone surveys (UCLA, 2017). Besides the landline sample, the CHIS dataset also included a statewide cell phone sample of the overall population. The landline and cell phone samples described two separate groups of respondents that characterized the data.
The researchers administered these samples through a computer-assisted telephone interview that included both the statewide landline random digital dial and the statewide cell phone sample. The landline sample was stratified according to county demarcations, groups of small counties and sub-county areas (UCLA, 2017). Based on the nature of the data collection method, only those households that had a landline telephone were included in this sample.
The sampling frame used to develop the CHIS dataset involved the use of traditional random digit dial and cell phone random digit dial sampling frames. The inclusion criterion was households, which had a landline or cell phone. The inclusion criterion also included the views of respondents who were adults (18 years and above) (UCLA, 2017). Comparatively, the exclusion criterion included respondents who were under 18 years and households that lacked either a cell phone or a landline. These frames were used to get the views of different respondents in the state of California by understanding the source materials and devices from which the sampling population was drawn from.
Power analysis is an important aspect of this methodology because it outlines the procedures used to select a right sample size that could be reliably used to find out if there is an association between asthma and smoking status among adult African immigrants in California. To determine the minimum sample size, I used various reliable calculators developed by Creative Research Systems, GPower, Raosoft, Survey Monkey, and National Statistic Service.
Using these tools, I obtained a minimum sample size requirement of 385 respondents based on a 95% confidence level, a 5% confidence interval, and a population of 2,000,000. This confidence level represents the level of surety that the inferences I will make after assessing the available data are reliable. The confidence level also insinuates the same position because it outlines the degree to which I am confident that my findings are true and reliable.
I used a confidence level of 95% because I wanted to have a reliable sample and it denotes a high level of precision for the sample used. I assume that the effect size of 1,130 people is adequate for drawing reliable conclusions about the relationship between the dependent and independent variables for the target population – African immigrants. The confidence level of 95% was used because many researchers who have done similar studies use it as a standard measure of confidence for their findings (UCLA, 2017).
Procedures for Recruitment, Participation and Data Collection
This study is a secondary analysis of archived data (CHIS secondary dataset). CHIS researchers originally collected this data, which is the largest database in California. The main variables analyzed from the database include asthma status and smoking status. The analysis will include an attempt to statistically interpret the relationship between both variables and mediating variables. The benefits of using this data for this type of analysis are clear, but the barriers are also many, as argued by Faber and Fonseca (2014) who say the success of this type of data collection depends on the efficiency of health agencies and researchers to conduct reliable public health studies.
Explaining the procedures for data management is important in this research study because it affects the credibility of research information (Jain et al., 2015). Data management also involves explaining the procedures for accessing data and the necessary permissions required to obtain such information. In the context of this study, the data analysis process explains the reputability of the information retrieved from the CHIS, contains an explanation of why the information retrieved from this secondary data source is important, and represents the best source of data for the research. I gained access to the database through an open access online platform available at CHIS. Therefore, there was no need for getting special permission to access the information.
Although it is common practice that researchers get special permission to use secondary research data, this requirement is mostly applied to research information that are not freely available online (Jirojwong, Johnson, & Welch, 2014). As mentioned above, this requirement was not applicable in my review because the information used is publicly available. However, I will seek permission before analyzing this CHIS secondary data.
Although using freely accessible data is acceptable in this study, it is important to use reputable data sources when undertaking this type of research. In the context of this study, the reputation of the findings, which I will produce, will only be as good as the reputation of the secondary data used. As mentioned in this study, the main data source was the CHIS dataset. This dataset was appropriate because it is statewide and is undertaken by a reputable organization. Furthermore, UCLA (2017), which undertook the study, has been collecting similar data since 2001 with relative success. Based on its good reputation, different professionals, including journalists, policy makers and health experts, use its information. Thus, this source of data is appropriate for this study.
Instrumentation and Operationalization of Constructs
Instrumentation of Constructs
As mentioned in this study, the main source of data is CHIS. It is among the largest health interview survey in America and is conducted on an annual basis to provide information about different health topics (UCLA, 2017). Although the CHIS publish health data relating to different years, the data used in this study is CHIS 2005-2015.
Looking at the appropriateness of the CHIS data to the current study, I find that it is relevant and specific to the topic under investigation because the dataset provides health data relating to different ethnic and racial groups in California. A focus on African immigrants as one cohort in the study is ideal for my analysis because the current study focuses on this ethnic group as the target population. The dataset is also appropriate to the current study because asthma is a health topic investigated in the research data. Other health issues highlighted in the dataset include diabetes and obesity (UCLA, 2017). The inclusion of asthma as a relevant health issue in the dataset and the provision of health data relating to immigrants bring my attention to the appropriateness of the data to the current study.
The findings contained in the CHIS document are freely available to the public. Therefore, there is no special permission from the researcher needed to use the instruments. Based on the availability of the health data outlined by CHIS, different people, including policy makers, state agencies and community organizations, find the resource useful in improving the health outcomes of their subjects.
A review of the reliability and validity of the values outlined in the research study reveal that both metrics are desirable because the CHIS data acts as a model for collecting state and local health data. This attribute demonstrates its reliability. Its use in advanced sampling and application methodologies also adds to the same metric because such evidences have been used to influence key policy changes in different areas of research (Mertz et al., 2014). Its credibility is also supported by the fact that the database contains information that would appeal to key stakeholders in the health sector. These published reliability and validity values show that the sources of data are relevant to the current research.
The CHIS database was used to collect health data across different ethnic and racial groups in California. In the past, it has been used to collect health data from all 58 counties in the state of California (UCLA, 2017).
However, there are cases where the researchers have oversampled specific areas within the state that are heavily populated (such as Los Angeles and San Diego). The reliability and validity of the findings developed from the past use of CHIS have been confirmed through the involvement of large and diverse samples (UCLA, 2017). In other words, past users of the data established that the samples used in the dataset were representative of the ethnic and racial diversity of the state, particularly because the findings could be used to answer specific and important health questions pertaining to different ethnic and racial groups in the state.
The CHIS data has sufficient instrumentation to answer the research question. For example, I have established that the dataset contains health data about different ethnic and racial groups in California. This one instrumentation is useful in answering my research question, which focuses on one ethnic group – African immigrants. Other aspects of the instrumentation used to develop the CHIS are its consistency, flexibility, and adaptability.
These attributes mean that the findings included in the report can be used to investigate new health issues and emerging trends among specific racial or ethnic groups. Lastly, the inclusion of spatial and geographic data in the CHIS dataset is another aspect of its instrumentation that would help to answer the research question because I am able to narrow down to a specific locality (California), which is at the core of my analysis. This type of instrumentation is often unavailable in national health data (Liamputtong, 2013).
Operationalization of Constructs
The research variables identified in the CHIS database were numerous though I utilized only few variables that are related to my research question. In this study, two main variables were relied on to complete the research. They include the independent variable (smoking) and the dependent variable (asthma). In the CHIS database, race emerged as one variable that helped to define the target population – African immigrants. It was denoted by the term Self-reported African American (SRAA).
Smoking is also another variable that was included in the CHIS database and it was denoted by the code SMKCUR. This variable indicated the number of people who were currently smoking. The same was true for asthma as a dependent variable because it was denoted by code ASTCUR. This variable helped to ascertain the current asthma status of the respondents.
The dependent variable for this paper is asthma. Simply, asthma is a health condition, which manifests as a long-term inflammatory disease of the respiratory tract (Tamimi, Serdarevic, & Hanania, 2012). The original researchers operationalized this variable according to the guidelines of the International Classification of Disease, which defines asthma as a chronic disease that causes pulmonary obstructions (WHO, 2016).
However, broadly, the International Classification of Disease segments this disease in two categories. In the first one, three types of asthma are explained – extrinsic asthma, intrinsic asthma and unspecified types of asthma and in the second category, asthma is defined in four segments that include unspecified asthma, mixed asthma, non-allergic asthma, and predominantly allergic asthma (WHO, 2016).
Similar to how the original researchers operationalized this variable, I will also use the International Classification of Disease to operationalize the dependent variable because this standard is globally accepted and approved by the CDC (2017). The International Classification of Diseases is also approved by the World Health Organization and is globally accepted by practitioners in the health field, including physicians, nurses and health information providers (WHO, 2016).
The independent variable will be smoking status. The original researchers used the International Classification of Disease to operationalize smoking as one of the variables in the dataset. This classification is enshrined in the diagnostic code ICD – 9 of the International Classification of Disease database, which states that smoking is the inhalation of tobacco smoke (WHO, 2016). This code has also been used to cover several aspects of smoking behaviors, including nicotine dependence, newborns affected by smoking during pregnancy, and occupational exposure to tobacco smoke. I will also use the same operationalization metric to operationalize smoking as the independent variable.
The reason for upholding the same metric is to maintain consistency in the operationalization of variables between the original and the current research. Furthermore, similar to how the merits of using the International Classification of Disease emerged in the analysis of the dependent variable (asthma), the same merits justify the use of the same criteria for operationalizing variables in this study.
The mediating variables in this paper include age, sex, years since immigration, marital status, alcohol abuse, education level, income level, and employment status. The CHIS database, which is the original source of data for this paper, defined these mediating factors as independent variables and not mediating variables as described above. Therefore, the above-mentioned variables were used independently to explain different health outcomes without any relation to the core variables under analysis in this paper – asthma and smoking.
The same variables also had no relation with the target population – African immigrants. Additionally, the CHIS database used the above-mentioned mediating variables to explain different metrics associated with the sample population. Most of these metrics helped to provide valuable data such as the health status, insurance status, and lifestyle behaviors affecting the health outcomes of California residents.
The variables were also used by the researchers to provide important descriptors surrounding the health of the sample population, such as their gender, ethnicities, age and such like demographic factors. Thus, as opposed to having the mediating factors explaining specific health outcomes, the researchers used the mediating variables for descriptive purposes.
The strategy for operationalizing the mediating variables, as described in the CHIS database, will not be the same one applied in this study. Instead, the moderating variables applied in the current research would be to understand the factors affecting the relationship between the independent and dependent variables. These variables would be important in making sense of the findings, with a specific focus on how they affect the outcomes observed. Furthermore, the moderating variables would be instrumental in understanding the social, economic, and political dynamics of the target population (African immigrants) that would further help to understand the findings.
The mediating effect that is caused by the mediating factors would be developed from the mediation model as espoused by MacKinnon (2012) who says the model helps to explain causation. In the context of this paper, causation would be applied to the independent and dependent variables, which are asthma and smoking. This analysis means that the mediating variables would be assumed to have led to the relationship between the dependent and independent variables and not the other way around where the dependent and independent variables affect the mediating variables. A mediation variable would be considered as so if it satisfies one of the following three conditions:
- If a change in the independent variable is responsible for a significant change in the dependent variable
- If it significantly dominates the relationship between the dependent and independent variables
- If the lack of it causes the disappearance of the relationship between the independent and the dependent variables
Data Analysis Plan
The data analysis plan is an important part of this research process because it would help me get useful information/findings from the research inputs. For purposes of undertaking this process, I will use the SPSS statistical software tool.
Data Cleaning and Preparation
Before analyzing data with the SPSS software tool, I will participate in data cleaning and screening procedures to make sure that the inputs of the data analysis process are reliable and credible. However, I do not expect to have many errors or mistakes associated with the data inputs (used in the data analysis process) because the dataset used is from the CHIS, which is often a reliable and valid source of information. Nonetheless, to make sure that the information keyed in the SPSS software is credible, I will look out for the range data. This process will also involve checking for the minimum and maximum values associated with the variables of interest by analyzing scores associated with the descriptive data (Murari, 2013).
To make sure that the data is clean, I will also look out for “abnormal” responses. This process will help me understand whether the responses included in the analysis are legitimate, or not. My criterion for doing so would be counterchecking suspect responses with those of the majority respondents. The data screening process will also involve looking out for identical responses from the data to avoid cases of duplication (Perry, Barak, Neumann, & Levy, 2014).
To make sure that this process is effective, I will investigate if there are cases of identical values, or near-identical values, to establish whether they have been posted erroneously, or they represent the same scores. If such cases are confirmed, I will choose to maintain one case/value and remove the duplicate value. The last step in the data screening process will involve manually checking for errors and mistakes associated with the data.
Unlike other data screening processes, there would be no specific analysis aimed at detecting specific issues because the focus would be to investigate whether there are any cases of oddities (Shaughnessy, Zechmeister, & Zechmeister, 2014). One criterion I will be using is looking out for “empty cases” where the values involved would be missing or insignificant. When such cases are established, such data would be omitted from the data analysis process.
Statistical Analyses Plan
Descriptive Statistical Analysis
The dependent variable, independent variable and mediating variables would be described using descriptive statistical methods that include frequency tables with ranges and percentages. I will specifically use mean values as a measure of central tendency for mediating variables like age, income and years since immigration. The frequency statistics would simply help to point out the number of times each variable occurs. Also, cross-tabulations will yield the frequencies and percentages for the variables.
For example, it is instrumental in explaining the frequency of asthma among people who smoke. This statistical tool would be useful in describing the personal information of the sample. For example, researchers have used it to provide details about gender, educational qualifications, income and such like demographic data. This type of information is applicable in understanding some of the moderating variables such as age, sex, years since immigration, marital status, alcohol use, education level, income level, and employment status that are highlighted in this study.
Inferential Statistical Analysis (Binary Logistic Regression)
For this study, I will utilize Binary Logistic Regression as the main inferential statistical analysis. As part of the data analysis plan, the use of Binary Logistic Regression will be central to the whole process because it would help to test the hypothesis, which states, “There is an association between asthma and smoking status among adult African immigrants in California.” The Binary Logistic Regression method is applicable in this study because there are two levels of the dependent variable (asthma) (Polit & Hungler, 2013).
In other words, the probability of asthma occurring because of smoking could only take two forms “yes” and “no.” This type of analysis will help us to mimic the real-life scenario of mediating factors that affect the relationship between the dependent variable (asthma) and independent variable (smoking). This analysis will be crucial in investigating the effect of mediating factors such as age, sex, years since immigration, marital status, alcohol use, education level, income level, and employment status on the dependent and independent variables. Its significance would be hinged on investigating whether there is a significant difference between two or more variables.
To further understand the relationship between the dependent and independent variables, I would further use the binary logistic regression method to estimate the probability that asthma would occur because of smoking using a classification plot for purposes of category prediction. In most studies, the probability of an outcome occurring should typically be more than 0.5 (this number is even higher than the probability of the event occurring by chance) (Laerd Statistics, 2017).
Since the “event” under analysis in this paper is asthma, an output of more than 0.5 would mean that smoking leads to the condition. This figure would help to predict the category of variables (Laerd Statistics, 2017). Carrying out a predictive analysis would also be useful in understanding important attributes about the variables, including the percentage accuracy in classification, sensitivity (percentage of cases that manifest specific and observable characteristics), specificity (percentage of cases that do not manifest observable characteristics), the positive predictive value, and the negative predictive value. The effect of the mediating variables would be useful in articulating these indices of analysis (Laerd Statistics, 2017).
The binary logistic regression analysis would also be instrumental in explaining different variables in the equation which would further help to clarify the contribution of the independent variable (smoking) to the equation under investigation and to evaluate its statistical significance.
The statistical significance would be conducted using the Wald test. Lastly, variance in asthma would be explained using a model summary that includes cox and Snell R Square and the Nagelkerke R Square, which are different techniques available in the SPSS software for analyzing variations and proposed by Laerd Statistics (2017). Collectively, using the binary logistic regression technique, we could include results of the classification analysis, variables in the equation analysis, and assumption tests. These techniques would be instrumental in developing predictions, based on an analysis of odds ratios.
Threats to Validity
Threats to validity are issues that could affect the credibility of the research findings. Indeed, as Nieswiadomy (2012) observers, threats to validity may significantly undermine the reliability of any given research design. The main threat to validity for the current study is centered on the use of the CHIS database, which was the main source of information for this study. The failure to be part of the original research is one threat that suffices in this context because it means I was not privy to pertinent research information that could be instrumental to the current research. One threat to the external validity of the research is that it could not be applicable to migrant populations outside the context of the research region – California.
Furthermore, it could possibly be inapplicable to other racial or immigrant groups living in California because the current research only focuses on African immigrants in the state. The specificity of variables is also another threat to the internal validity of the study because different criteria for analyzing the variables may cause distortions in the findings. For example, a person’s smoking status may quickly change within the time a study is undertaken. This change may significantly affect the outcomes of the study.
To overcome threats to internal validity, I will obtain as much information about my sample population (African immigrants in California) as possible to get a better understanding of the respondents and to evaluate how their social, political and environmental dynamics affect the study. This measure is useful in solving the internal threat associated with instrumentation (Lee, Crawford, & Wallerstedt, 2012). Standardization is also another technique I will use to overcome the same threat. In this approach, I will strive to consider the conditions under which the original study was carried out when developing the research findings because I am depending on the CHIS methods and data for my analysis.
I could also experience some of the same validity problems when analyzing external validity issues associated with the study because there is a problem associated with applying the findings of this study beyond the selected sample. One problem I could experience from this issue is the possibility that the secondary data used to develop the research findings may not apply to a different time, outside of the publication date (Fernandez-Hermida, Calafat, Becoña, Tsertsvadze, & Foxcroft, 2012).
The secondary data used in this study was published in 2015, and this means that the findings may not apply to any other period under review. I may also experience the same problem with instrumentation because I have already established that the secondary data used was developed from telephone and cell phone surveys. The use of different instrumentation techniques in future research may yield different results.
For example, if researchers used one-on-one interviews to collect data, as opposed to the telephone and cell phone surveys, the relationship between asthma and smoking status may change because the context of the investigation would change, possibly in favor of respondents feeling more obligated to take the research more seriously, as opposed to a phone interview (Arkkelin, 2014).
To overcome the external validity issues mentioned above, I will point out, in the findings section, that the results apply to a specific period. This way, the users would know the limitations of the study and consider the same when making future or past inferences about the relationship between asthma and smoking status among African immigrants living in California (Lang & Altman, 2014).
This solution would be instrumental in solving external validity issues associated with the publication date for the CHIS data. To solve the problem of instrumentation, it is essential to include co-variation in the study when analyzing the data to understand the effects of the use of different instrumentation techniques when developing the findings (Lang & Altman, 2014). Similarly, standardizing the research process could help to avoid this issue because it would make sure researchers replicate the same context when conducting future studies (Lang & Altman, 2014).
Lastly, I do not anticipate any threats to construct or statistical conclusion validity because I have already explained how each variable will be operationalized. Similarly, in this paper, I have shown that the findings would only be limited to the African immigrants in California and those who are either smoking, or have been diagnosed to have asthma.
Any study that involves human participants is often subject to several ethical issues (Ndebele et al., 2014; Nicholls et al., 2015). The CHIS dataset used in this research, as the main data for review, is subject to these ethical issues. In this section of the study, I will explain how these ethical issues affect the treatment of data and the recruitment of participants and materials when formulating the research findings.
The ethical procedures used by the CHIS researchers to collect data were approved by UCLA. The ethical procedures that will be followed in the current study will also be based on the guidelines outlined by the Institutional Review Board (IRB) because I will apply to this institution to analyze data before conducting the statistical analysis. The current study will also involve an analysis of de-identified data to avoid cases of privacy infringement or confidentiality breaches.
Data was stored safely in a computer and protected by a password, which only the researcher could gain access to. This safeguard ensured that no other person had access to the information besides the researcher (Nursing and Midwifery Board of Australia, 2012). Lastly, credit will also be given to the original authors of the research to avoid cases where the readers could assume that the findings of the current study are the author’s. This way, cases of plagiarism are avoided.
This section shows that the methodology of this research was intended to answer the main research question, which centered on investigating the association between asthma and smoking status among adult African immigrants in California. The independent variable is smoking and the dependent variable is asthma. The moderating factors are age, sex, years since immigration, marital status, alcohol use, education level, income level, and employment status. Secondary analysis of the archived data will be conducted after receiving IRB approval. I will conduct both descriptive and inferential statistical analyses and complete a descriptive analysis using frequency tables and measures of central tendency. I will also conduct the inferential analysis using bivariate and multivariate analyses.
The quantitative correlation research is the main design used to answer this research question and it is based on the understanding that the research topic is a correlation study because the current research explores the association between asthma and smoking status among African immigrants in California.
The main source of information is secondary research data, which comes from an independent survey by the CHIS. The SPSS software tool is also the main data analysis technique used in this study because of its ability to analyze large amounts of data. The correlation technique is the main SPSS tool applied in this study because of its ability to identify associations between two or more variables. The results of the data analysis process are presented in the next section, which outlines the results and findings of the study.
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