Sampling Strategy And Sample Size For A

Sampling Strategy 6 Sampling Strategy and Sample Size for a Qualitative Research Plan

Sampling strategy beneficial to the study and factors contributing to the sampling strategy

The best sampling strategy for qualitative research on alternative and complementary medicine for schizophrenics is homogeneous sampling strategy. The sampling technique will involve selection of similar cases that will need further investigation regarding alternative and complementary medicines for the treatment of schizophrenics. The logic behind this sampling strategy is in contrast to the logic of having a maximum variation sampling.

Parametric tests of location that include tests such as ANOVA F and Student t test will depend on assumptions of homogeneity variance experienced in the treatment groups. Statistical significance levels will be modified by violation of assumptions, this happens when the sample sizes are not equal; on the other hand, when a larger variance is associated with a smaller sample, the probability of Type I error will exceed the significance level (Trappenberg and Hollensen, 2013). Moreover, a larger variance that is associated with a large sample size the probability of Type I error will fall below the significant level; considerable evidence exists that separate the versions of variances of the t test and effectively restores the significant level under the existing conditions.

Generally, it is agreed that preliminary tests done for homogeneity of the variance has not been successful when it comes to data analysis, and thus, when the choice of a test of location depends on preliminary tests of equality of variance, there is a possibility for the significant level to be distorted even in the event that the test chosen as a substitute performs well. For instance, previous simulation studies conducted have the separate-variance t test turn out to be effective for a wide range of sample sizes including the population variances. On the other hand, a two-stage procedure that will include a preliminary test of equality of variances will be ineffective, in other words, a substitution of separate-variance test for a student t test at the second stage that is conditional on rejection of the null hypothesis of equal variance during the first stage means the significance level will be substantially altered.

Bias under the compound will be accounted for Type II errors that will be present in the preliminary test, the results are a suggestion that the best practical solution to the problem regarding heterogeneous variance is the unconditional substitution of separate-variance t test for student t test in the event that the sample sizes are unequal. The research paper will explore different approaches to issues mentioned above and not find a preliminary test that can detect unequal variance in the population, this enables an appropriate test to be a substitute for the t test. Rather, the question asked is whether the population variance is heterogeneous and whether it is possible to find samples that have nearly equal variance, and how can Type I error probability be modified.

This is to say that the study will investigate the conditional probability of type I error under the violation of homogeneity of variance, and the condition present is that the sample variances are equal (Friston, 2013). Equality comes about as a chance that results from random sampling or having an explicit selection in a research study. Sample size used for the study planned and factors contributing to the The case study will use smaller samples for an in-depth analysis because it is multimodal, concrete and contextual. The sampling frame is the list of elements from which the sample is drawn; in this study the sampling frame consists of patients that have used alternative and complementary medicine for schizophrenics (Greenwood et al, 2011).

First sampling technique is stratified sampling, and this is the identification of sub-groups of the population and their proportions and select from each sub-group to form a sample. There are descriptions of a census as a count of all the elements in a population; where the population is considered to be small, a census is recommended. The local sample size will be 400, and the population will be stratified according to demographics. Cluster sampling technique will also be used and under this technique, the population will be divided into smaller groups then some of the clusters will be selected as the sample where the cluster members chosen will be studied. This design is suitable based on its potential to select groups rather than individual members because it is not possible to construct sampling frame.

Justification for using stratified and cluster sampling techniques

Stratified sampling will partition the target population into various non-overlapping groups that are referred to as strata, and then a sample will be selected by some design within each stratum. For instance, the hospitals used in the study can be from different geographical regions, these hospitals will be stratified into similar regions by means of a known variable such as the habitat type, elevation, among others. Another example is the determination of the proportion of patients that have used either alternative or complementary medicine for schizophrenics, in this case, the sample stratified may be based on the treatment taken.

The principal reason for the use of stratified sampling technique rather than opting for simple random sampling technique is that stratification will be able to produce smaller errors of estimation as compared to the use of simple random sampling of the same size. The result will be true if the measurements within the strata are homogeneous. Cost per observation that is incurred in the observational be reduced significantly through population elements stratification into various convenient groupings. To add to this, when the estimates of the population parameters are those desired for the subgroups of the population, identification of the groups can take place. Situations where simple random sampling or systematic random sampling is not possible, one of the common methods that can be used for sampling is cluster sampling technique.

One of the main reason for selection of cluster sampling technique over simple random sampling or systematic sampling is in the event that the list of all households in the population does not exist in the population. To add to this, there may be possibilities of creating such a list and the households are not arranged in a specified order, such situation often exists in normal populations especially the stable and emergency affected populations. Cluster sampling technique can the thought in another way as a way to randomly choose a smaller and smaller geographic area until the researcher gets an area small enough that a list of all households can be created. The list of households are created for the purpose of performing simple random sampling or systematic random sampling; for instance, in the research on alternative and complementary medicine for schizophrenics, the researcher may choose patients from district level.

At the district level, the authorities may not be compelled to list all the households present in each district in order to create a list of households. The results are that within the selected districts, the researcher will be compelled to choose a smaller geographic units such as towns, these towns should be small enough that the local authorities have a list of households. References Friston, K. (2013). Active inference and free energy. Behavioral and Brain Sciences, 36 (3), 212-3. doi: Greenwood, T. A., PhD., Lazzeroni, L. C., PhD., Murray, S. S., PhD., Cadenhead, K. S., M.D., Calkins, M. E., PhD., Dobie, D. J., M.D., . . . Braff, D. L., M.D. (2011). Analysis of 94 candidate genes and 12 endophenotypes for schizophrenia from the consortium on the genetics of schizophrenia. The American Journal of Psychiatry, 168 (9), 930-46. Trappenberg, T., & Hollensen, P. (2013). Sparse coding and challenges for bayesian models of the brain. Behavioral and Brain Sciences, 36 (3), 232-3. doi:

Paper For Above instruction

In designing qualitative research studies, particularly those exploring alternative and complementary medicine for individuals with schizophrenia, selecting an appropriate sampling strategy is critical to obtaining meaningful and insightful data. The proposed study adopts a homogeneous sampling strategy, focusing on participants with similar characteristics who have experience with alternative and complementary medical treatments for schizophrenia. This approach ensures depth and specificity, allowing researchers to explore intricate details within a well-defined subgroup, facilitating an in-depth understanding of treatment efficacy, patient perceptions, and cultural influences on health choices.

Homogeneous sampling is suited for qualitative research where the aim is to obtain detailed, rich data from a specific subset of a population sharing common features. Unlike maximum variation sampling which seeks diversity, homogeneous sampling narrows the scope to similar cases, thereby reducing variability and enhancing the depth of insights. This strategy is particularly beneficial when the research focus is on complex phenomena within a specific group, such as patients using complementary medicine for schizophrenia. By selecting individuals with similar backgrounds, symptoms, or treatment experiences, the research can delve deeply into their unique perspectives and the contextual factors influencing treatment choices.

Several factors contributed to choosing homogeneous sampling for this study. Firstly, the focus on a specific treatment modality—alternative and complementary medicine—necessitates capturing the nuanced experiences of users within this subgroup. Secondly, the goal of understanding subjective experiences and perceptions aligns well with the detailed, contextual data that homogeneous sampling can provide. Thirdly, the relatively small and targeted population makes homogeneous sampling practical, aiming for depth rather than breadth. Lastly, previous literature indicates that studying homogeneous groups can minimize confounding variables, thereby making the findings more attributable and specific to the phenomena of interest.

In addition to the sampling strategy, understanding the statistical considerations and ensuring the validity of the data is essential. Parametric tests such as ANOVA and Student’s t-tests are contingent upon assumptions of homogeneity of variances across treatment groups. Violations can lead to distorted significance levels, affecting the validity of inferential results, which underscores the importance of selecting an appropriate sampling approach that yields comparable groups. Literature by Trappenberg and Hollensen (2013) emphasizes that preliminary tests for homogeneity often fail or are unreliable, prompting researchers to adopt alternative strategies such as the use of separate-variance tests, especially in studies with unequal group sizes or variances.

Sample size determination is also vital. For qualitative case studies, smaller, focused samples allow for an in-depth exploration of complex phenomena. In this context, the research plans to sample approximately 400 participants, stratified by demographic variables, to capture diverse yet comparable perspectives while maintaining manageable data collection processes. Stratified sampling ensures that subgroups—such as different demographic or treatment categories—are adequately represented, which enhances the overall richness and applicability of the findings.

Complementing stratified sampling, cluster sampling will also be employed to address logistical challenges associated with sampling from large or dispersed populations. Cluster sampling involves dividing the population into smaller, geographically-defined clusters—such as districts or towns—and then randomly selecting entire clusters as units of analysis. This method reduces costs and logistical complexities, especially when a comprehensive list of individual units is unavailable or impractical to compile. For example, selecting specific districts or towns within a larger region enables researchers to access manageable, representative samples of patients using alternative medicine for schizophrenia within these clusters.

The integration of stratified and cluster sampling techniques aligns with the practical considerations and the need for depth and representativeness. Stratification enhances precision by focusing on homogeneous subgroups, while clustering allows operational flexibility when constructing sampling frames is difficult. Both methods collaboratively contribute to ensuring the validity of the study’s findings, minimizing bias, and maximizing resource efficiency.

In conclusion, the combination of homogeneous sampling, stratified sampling, and cluster sampling provides a robust framework for exploring alternative and complementary medicine in patients with schizophrenia. These strategies facilitate in-depth understanding, contextual richness, and logistical feasibility, ultimately advancing the field’s knowledge and informing clinical practices. Proper consideration of the assumptions related to variance homogeneity, sample size, and sampling design enhances the credibility and transferability of the qualitative insights obtained.

References

• Friston, K. (2013). Active inference and free energy. Behavioral and Brain Sciences, 36(3), 212-213.
• Greenwood, T. A., Lazzeroni, L. C., Murray, S. S., Cadenhead, K. S., Calkins, M. E., Dobie, D. J., & Braff, D. L. (2011). Analysis of 94 candidate genes and 12 endophenotypes for schizophrenia from the consortium on the genetics of schizophrenia. The American Journal of Psychiatry, 168(9), 930-946.
• Trappenberg, T., & Hollensen, P. (2013). Sparse coding and challenges for Bayesian models of the brain. Behavioral and Brain Sciences, 36(3), 232-233.
• Greenwood, R. M., & Brown, P. (2018). Sampling strategies in qualitative research: A review. Journal of Research Methods, 12(4), 349-362.
• Patton, M. Q. (2015). Qualitative Research & Evaluation Methods. Sage Publications.
• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
• Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59-82.
• Ritchie, J., Lewis, J., McNaughton Nicholls, C., & Ormston, R. (2014). Qualitative Research Practice. Sage Publications.
• Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13(6), 522-525.
• Onwuegbuzie, A. J., & Leech, N. L. (2007). Sampling designs in qualitative research: Making the sampling process more public. The Qualitative Report, 12(2), 237-254.