Quantitative research is gathered data that involves measurements of phenomena using numbers, counts, and the measures of things. Social scientists tend to use quantitative research when observing deductive, nomothetic explanations, experimental designs, and survey research (Monette, Sullivan, & DeJong, 2013). This method’s data can consist of the race, age, sex, diagnosis, IQ, physical impairments, level of self-help skills, and possible other factors. All the participants’ information is considered valuable contributions to determine the relationship between paternal stress and disabled children. Quantitative research comes in many different approaches and can be conducted with four different designs; descriptive, correlational, quasi-experimental, and experimental. First, descriptive design describes a current status of a phenomenon. Second, correlational design observes the relationship between the variables within the research study by utilizing statistical analyses. Third, quasi-experiment design searches to establish a relationship between variables that are not randomly selected. Finally, experimental design (true experiment) where the individuals in the research study are randomly selected, where it is possible that some of the participants have no relation to the research study. The researcher in an experimental design manipulates the independent variable to observe the effect on the dependent variable in order to control the external factors from influences the results (Shuttleworth, M, 2019). Based on this research, I believe that utilizing the quasi-experiment design, a quantitative approach will benefit this research on paternal stress of parents with disabled children.
As explained before, quasi-experiment design searches to establish a relationship between variables that are not randomly selected. The researcher of their study does not manipulate the independent variable. The independent variable is usually an innate characteristic; something internal rather than from external sources. Thus, the research proposal will observe what the impact of paternal stress has on a parent of a disabled child. As previously mentioned in the research proposal, “Parental stress, from the parent of a disabled child, exhibit higher levels of stress compared to a parent with a non-disabled child (Cuzzocrea, Murdaca., Filippello, & Larcan, 2016). Depending on the child’s diagnosis, challenges can be redirecting a child from repeatedly hitting their face (self-harm), low cognitive development, nonverbal no matter how many communication lessons, physical impairments that some children cannot walk or in some cases need assistance feeding themselves, and so on. Due to the unique and various physical and mental impairments, parents need to adjust their lifestyle so they can accommodate their disabled child. Accommodating their child is not as simple as most may observe. Parents can experience financial burdens, depression, exhaustion, stereotyped (some religious beliefs view disability as punishment), feelings of social isolation (cultures’ pity and stigma), and concerns about lifelong care or other challenges of the child (Lindo, Kliemann, Combes, & Frank, 2016). When a parent experiences stress, the child can also be affected by it. Affectively addressing parental stress is crucial for the enhancement of a child’s well‐being and functioning within the family and larger society as well as for the parents’ well‐being (Lindo, Kliemann, Combes, & Frank, 2016).” It is appropriate that participants are the parents (or other guardians) of disabled children to ensure positive outcomes. The quasi-experiment design will be able to gather the factors and it will then be imputed as numbers, counts, and other measures of things. For example, the quasi-experiment design can gather information of the parent’s race, ages of their children, sex of the children, the diagnosis of each children (or if each child has more than one diagnosis), IQ, and physical disabilities, level of self-help skills the children need are data for research.
To begin the quasi-experiment, selecting the rightful participants by conducting quantitative sampling will benefit the research because it will create a sampling frame. It is a list of the entire population that the researcher is interested. The sampling frame, for example, would be a list of parents of disabled children. A sample is a subset of parents from the sampling frame. These parents chosen have characteristics that is considered representatives of the population (parents of disabled children). For example, the characteristics are physically disabilities, diagnosis, ages, gender, treatment plans, etc. The research will then need to be generalized. This is when the data gathered from the research will be imputed as statistic information. Statistic is a description of a certain variable regarding the sample. If there is a sampling error, it is when the difference between a statistic (variable of the sample which is the parents of disabled children) and a parameter (certain variable involving the population which is the disability of the child). The sampling error will happen if another researcher decides to conduct the same study but with a much larger population. For example, let’s say I conducted my study of the parental stress that parents of disabled children experience with a small group of those parents selected in my sampling frame. I would provide a result of how those impacts negatively affect those parents. However, if another researcher wanted to complete the same study, with the same sample but with a larger population of parents, and provided a different result, maybe hypothetically speaking, that not all parents of disabled children experience paternal stress, a sampling error occurred. Not enough samples were collected to determine a possible, close to accurate conclusion. This example of sampling error explains how greatly important it is to create a proper sampling group to representative the research.
.  To determine a representative sample, a probability sampling needs to be conducted. There are four types of probability sampling where one of these types needs to be chosen to benefit this research. The first type is called a simple random sampling where, as its title is described, it is completely random. A number will be randomly assigned to each participant. In other words, a random number assigned to a parent with a disabled child. Then those numbers assigned will be randomly picked and imputed into the sample frame. The random selection can be completed by a simple handpicking hidden numbers from a hat to a computer generalizing numbers. The second type is called systematic sampling where to determine the sample frame, the researcher needs to determine exactly how large they want their sample to be. Once an amount is determined, the researcher will need to divide the population with the amount of the sample. The results will provide a number spaced between the selected individuals in the sampling frame. This is known as a sampling interval (Chrisflip, 2014). For example, there was a population of 500 parents of disabled children. But the desired amount for the sample is 50 parents. So, dividing 500 of those parents with the chosen amount 50, will equal to 10. This will mean by choosing in the sample, the researcher will select every 10^th parent from the list will be included in the sample. The third type is called stratified sampling. This type of sampling (strata) is where the population is separated into different groups. So, for instance, for this research of studying the paternal stress from parents of disabled children, stratified sampling will separate the parents by different characteristics. The ideal characteristic to separate into groups would be the child’s diagnosis, physical or mental impairments. This could be children diagnosed with autism can be separated into one group while those with physical impairments into another group, and so on. After the parents are separated into different groups, the researcher can either utilize simple random sampling or systematic sampling to create the sampling frame. The advantage of using the stratified sampling is the assurance of having small subgroups (strata). But the disadvantage is the difficulty to get a sampling frame if there is not enough information from those subset groups. The final type is called cluster sampling where it is impossible to create a large sampling frame. For example, all possible diagnosis, mental and physical impairments that children have in California. What needs to be completed is to first create a sampling frame based on clusters. The clusters will be put into another sampling frame and sampled by either the simple random/systematic/stratified sampling. For example, it would be very difficult and impossible to sample all parents of disabled children if the researcher wanted to test parents of the state of California. Finding parents of disabled children in California is a challenge. To make the search easier, the researcher will need to produce a list of major medical providers (for example Kaiser Permanente) located in California, or sum it to only Kaiser Permanente as the researcher’s clusters. This is where the research can gather parents of disabled children who are being treated at Kaiser, to be placed in a sampling frame and can be sampled by either the simple random/systematic/stratified sampling to get a representative sample of those parents. The advantage of cluster sampling is the time saved in creating a sample.
.  For my research proposal, I am establishing a quantitative method to examine the parental stress experienced by the parents of disabled children, whether mental or physical disabilities. The quantitative method I would use is called the quasi-experiment design, where this design will search to establish a relationship between the characteristics of the disabled child and paternal stress. For example, is the levels of paternal stress varied by the different disabilities of children? Another key factor that the quasi-experiment design is a perfect quantitative method for this research proposal is because the variables (characteristics of the participants) are not randomly selected. If the participants were randomly selected, it is most likely the outcome in my research will not deliver a positive result. A parent of a nondisabled child cannot truly grasp the experience or understanding of a parent of a disabled child. The parent of a nondisabled children doesn’t attend unlimited attendance of sessions or accommodate a lifestyle that benefits their child with a disability. In addition, those accommodations may possibility lead to any stressful challenges such as financial responsibilities, physical burdens, maladaptive behaviors in children, judgement (some religious beliefs view disability as punishment), feelings of social isolation (cultures’ pity and stigma), and concerns about lifelong care of the child (Lindo, Kliemann, Combes, & Frank, 2016). Again, a parent of a nondisabled children that is randomly selected into the research wouldn’t be a good candidate and may lead to misleading results. To determine the rightful participants for my research, based from my research, the probability sampling I would use would be cluster sampling. I feel that I would be able to conduct my research on a justifiable number of parents. Since there are varies types of disabilities the children may have, I will be able to separate, in that sample frame, into clusters characterized based on their disabilities. Once in clusters, I will utilize stratified sampling as the next step to gather parents as the representatives of the population. I believe the quasi-experiment design is a perfect quantitative method to gather valuable information for my research and cluster sampling as the probability sampling would benefit my research to determine and gather the appropriate participants for my research to receive a positive outcome.

References

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