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Introduction
The purpose of this study is to explore 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 (such as 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 explored the relationship between the aforementioned variables among African immigrants in California.
This chapter contains five major sections. The first part is the research design and rationale section. It explains the research variables and their connection to the research design. The second section contains details of the methodology used in the research. In this section, the information about the target population, sampling procedures, power analysis, instrumentation, operationalization of constructs, data management, data analysis, procedures for recruitment, data collection, and participation are outlined. The third section of this chapter outlines the threats to validity, while the fourth section outlines the ethical procedures governing the research process. The last section of this chapter is a summary of the main tenets of the chapter.
Research Design and Rationale
As mentioned in other sections of this paper, the purpose of this paper is to explore 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 smoking status, 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 variables 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 incidences are often measured in quantitative metrics, while smoking incidences are also measured quantitatively.
Furthermore, the dataset (CHIS) used in this study measures the two variables in numbers. 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, we seek to explore two variables, which are smoking and asthma status. Based on these findings, we see that 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 because the analysis is already done and the time is 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 a plethora of opportunities for understanding the association between asthma and smoking status for future researchers who may want to explore 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
Target Population
In the context of this study, the target population refers to the selected immigrant group, which we are studying. As highlighted in the first chapter of this paper, 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 State.
The target population size would be 20,000 people. This number is expected to include the entire set of respondents for which the research data would be based on. We estimate that a sample size of 20,000 respondents is large enough to make inferences about the association between asthma and smoking status among adult African immigrants in California because about 158,953 African immigrants are living and residing in California (UCLA, 2017). Thus, a sample of 20,000 people is estimated to be around 13% of the total population. 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).
Sampling and Sampling Procedures used to Collect Data
As highlighted in other sections of this report, the main sources of data for this study would be secondary research obtained from three documents titled, CHIS 2015 Adult Data.sav, CHIS 2015 PUF Data Dictionary Adult, and CHIS 2015 Adult Questionnaire. Since our main sources of information are attributed to these three sources of secondary data, the sampling strategy mirrors the same plan present in the secondary data. There are four main types of sampling strategies random sampling, stratified random sampling, systematic sampling, and rational sub-grouping (Mathie, Roniger, & Van Wassenhoven, 2012).
The CHIS 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
Power analysis is an important aspect of this methodology because it outlines the procedures used to select the 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 sample size, we used a reliable calculator developed by Creative Research Systems. Using this calculator, we obtained a sample size of 20,000 based on a confidence level of 95% and a confidence interval of 0.65. This confidence level represents our level of surety that the inferences we will make after assessing the available data are reliable. The confidence level also insinuates the same position because it outlines the degree to which we are confident that our findings are true and reliable.
We used a confidence level of 95% because we wanted to have a reliable sample. Indeed, a confidence interval of less than 1 denotes a high level of precision for the sample used. Similarly, a confidence level of more than 1 symbolizes a lower degree of precision. We assume that the effect size of 20,000 people is adequate for drawing reliable conclusions. The confidence level of 95% was used because many researchers who have done similar research use it as a standard measure of confidence for their findings (UCLA, 2017).
As highlighted above, these calculations were derived from a sample size calculator developed by Creative Research System. The findings of this power analysis will be instrumental in the data analysis section because they would help us to understand the criteria used to come up with the research findings. Furthermore, they would be useful in understanding the threshold for determining the validity of the findings.
Procedures for Recruitment, Participation and Data Collection
As mentioned in this study, data were collected using secondary research. The secondary research was developed as a survey that included the views of a sample of households living in California. The respondents were chosen because they were the head of households. In cases where the parents were unavailable, guardians took their place.
Data Management
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. We 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 articles.
Although it is common practice that researchers get special permission to use secondary research data (Jirojwong, Johnson, & Welch, 2014), this requirement is mostly applied to research articles that are not freely available online. However, this requirement did not apply to our review because the information used is publicly available.
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 we will produce, will only be as good as the reputation of the secondary data used. As mentioned in this paper, the main data source was the CHIS. 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 2010 with relative success. Based on its good reputation, different professionals, including journalists, policymakers, and health experts, use its information. Thus, this source of data is appropriate for this study.
Instrumentation and Operationalization of Constructs
Instrumentation
As mentioned in this paper, 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 report was published in 2015.
If we look at the appropriateness of the CHIS data to the current study, we 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 our 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 our attention to the appropriateness of the data to the current study.
As highlighted in this chapter, 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 policymakers, 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 revealed 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 evidence has 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.
As highlighted in this paper, 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 about different ethnic and racial groups in the state.
The CHIS data has sufficient instrumentation to answer the research question because of some sampling and methodological issues highlighted in this chapter. For example, we have established that the dataset contains health data about different ethnic and racial groups in California. This one instrumentation is useful in answering our 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 we can narrow down to a specific locality (California), which is at the core of our analysis. This type of instrumentation is often unavailable in national health data (Liamputtong, 2013).
Operationalization of Constructs
Dependent Variable: As mentioned in other sections of this paper, the main variables for this paper are asthma status (dependent variable) and smoking status (independent variable). These variables are operationalized depending on how well they are categorized into measurable parts. The process of measuring the variables will be done empirically and quantitatively. The operationalization of asthma status as the dependent variable lies in our understanding of the textbook definition of the health condition, which manifests as a long-term inflammatory disease of the respiratory tract. These symptoms make it difficult for their victims to breathe.
In the context of our analysis, the dependent variable would be operationalized by noting the changes in the number of people who present the symptoms of the disease. These symptoms would have to be clinically proven to reach the threshold for diagnosing a patient as suffering from the condition. Changes in asthma incidences within the sampled population would also be measured as an indicator of the number of people who manifest long-term symptoms of the condition and not necessarily those who manifest the same symptoms in the short-term. Thus, the population determined to be falling under this health group must have manifested long-term symptoms of the condition.
The variable would be measured or manipulated by observing changes in the percentage of African immigrants who have asthma and are smoking. Efforts would be made to observe whether there are noticeable patterns between the two variables. The variable/score would be calculated by analyzing changes in asthma cases among people who smoke and those who do not smoke. This score would be represented as a percentage because we will describe the percentage of people who smoke, viz-a-viz those who do not smoke. In this regard, there would be two scores. One of them would be the percentage of African immigrants who have asthma and smoke, while the other will be a percentage of African immigrants who have asthma and do not smoke.
Independent Variable: Comparatively, the dependent variable will be the smoking status. This variable will be operationalized by noting that the associated changes are reflective of the number of people who inhale tobacco smoke. Therefore, the measure does not include people who smoke other substances, such as marijuana, or those who partake in other types of smoking, such as smoking cigars, light cigarettes, pipes, and menthol cigarettes.
This variable is also operationalized based on the frequency of smoking because there is no criterion for categorizing people as smokers, based on the times or frequency they engage in the habit. For example, a person who has smoked for only one week and another who has smoked for several years are included as equals when operationalizing this variable. However, the age criterion for including the same respondents is valid because, in this study, we only include adult smokers and not teenage smokers.
In other words, this variable will only include people who are smoking and above 18 years. Similarly, this analysis does not have a gender criterion because both male and female smokers are included in the assessment. At the same time, changes in smoking status are also observed in people who have quit smoking and are currently smoking. Therefore, people who have smoked at any point in their lifetime are included as part of the review.
The variable would be measured by counting the number of people who smoke or who have smoked in the past. This measurement would be a numeric figure. The scale score for this variable will be calculated based on the number of people who smoke, compared to the population of African immigrants who do not smoke. In this regard, the computation would assume the form of a fraction, where the numerator would be the number of people who smoke and the denominator would be the number of people who do not smoke.
Data Analysis Plan
The data analysis plan is an important part of this research process because it would help us to get useful information/findings from the research inputs. To undertake this process, we will use the SPSS software tool. The 2015 version will apply to the study because it contains the latest updates and tools for data analysis (Arkkelin, 2014).
Before using the aforementioned software tool, we will participate in data cleaning and screening procedures to make sure that the inputs of the data analysis process are reliable and credible. However, we 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, we 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, we will also look out for abnormal responses. This process will help us to understand whether the responses included in the analysis are legitimate, or not. Our 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, we 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, we 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 we would 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.
As highlighted in previous sections of this paper, the data analysis process will be aimed at answering the main research question which appears below,
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RQ: What is the association between asthma and smoking status among adult African immigrants in California?
The hypotheses are as outlined below
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Null Hypotheses (H0): There is no association between asthma and smoking status among adult African immigrants in California.
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Alternative Hypothesis (H1): There is an association between asthma and smoking status among adult African immigrants in California.
As part of the data analysis plan, the use of inferential tests will be central to the whole process because it would help to test the hypothesis. Although inferential statistics often involve different types of tests, such as the chi-square tests, and t-tests, the data analysis section will only involve testing for correlations, which will help us to understand whether there is a linear relationship between the independent and dependent variables (Polit & Hungler, 2013). To do so, we will develop a scatter plot diagram to inspect the relationship between the dependent variable and the independent variable. This scatter plot will be developed from the SPSS software package under the graphs tab.
Although we will use multiple statistical tests for this review, we will account for the possible errors that may occur from doing so by putting a stricter significance threshold for each test. For example, to affirm a positive association between asthma and smoking status, we will only consider values that are 0.5 to 1. This range is stricter than the normal range of proving such a relationship, which often varies from 0 to 1 (Wright-St Clair, Reid, Shaw, & Ramsbotham, 2014).
Part of the data analysis process will also involve the inclusion of mediating factors, such as sex, income, and education (among others), in the analysis. The justification for including these variables is their significant influence on smoking behavior and asthma development among the target population. Furthermore, it is difficult to have a perfect experiment because the compliance rate will not always be absolute and neither is the dropout rate. Consequently, introducing covariates in the analysis will help to clarify the relationship between the dependent and independent variables (Arkkelin, 2014).
As highlighted in this chapter, the data analysis process will involve inferential analyses using correlation tests. Testing for correlations will involve analyzing values that range from -1 to 1. If we get values that are closer to 1, we will deduce that there is a strong relationship between smoking and asthma status among African immigrants in California (Rezaei et al., 2017). The opposite is also true because if we deduce a value that is closer to -1, we will assume that the two variables share a weak relationship. A value that is closer to 0 or the same number would mean that there is minimal or no linear relationship between the two values as well (Mohini & Prajakt, 2012).
Threats to Validity
Threats to validity may significantly undermine the reliability of any given research design. In the current study, there are specific threats to internal and external validity. One threat to internal validity is instrumentation, which often occurs when different tools are used to analyze the same relationship. 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 persons smoking status may quickly change within the time a study is undertaken. This change may significantly affect the outcomes of the study. Lastly, another threat to internal validity could arise from the possibility that some researchers could be lying about their smoking status (Nieswiadomy, 2012). Indeed, there is no proven mechanism for establishing whether the respondents were telling the truth about their smoking status, or not, mostly because the secondary data used in this study was developed from a telephone interview, where the researchers were not in a position to establish the validity of what their respondents were saying.
To overcome these threats to internal validity, we will obtain as much information about our sample population (African immigrants) 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 we will use to overcome the same threat. In this approach, we will strive to consider the conditions under which the original study was carried out when developing the research findings.
Lastly, to overcome the problem of a possible lack of truthfulness about the smoking or asthma status of respondents, it would be important to put a disclaimer in the research that would outline the basic assumptions underlying the study. This means that the researcher would present an assumption that the participants gave accurate responses. Therefore, consumers of the research data may use the data with the understanding that there is a possibility the respondents were not telling the truth.
We 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 we 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. This means that the findings may not apply to any other period under review. We may also experience the same problem with instrumentation because we 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, we 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 t
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