Methodological studies on research design, conduct, analysis and reporting

Introduction

A significant number of researches have looked into the effectiveness of meditation as a treatment intervention for cancer patients/victims experiencing negative psychological challenges because of this disease (Smith et al., 2005). These researchers have used different study designs, methods and procedures, analysis and reporting techniques. In this paper, a critical review of the specific research designs, methods and procedures, as well as the techniques of analysis and result reporting are provided. For those who are seeking psychology dissertation help, delving into these research methodologies can offer the most valuable insights. The review will cover both methodological and analytical considerations and the controversies with regard the use of certain methods and procedures used by the studies. The limitations with existing methods used in the different studies will also be addressed.

Critical Review

The key studies and interpretation of their results- do the data support their conclusions.

1) Chang et al. (2018)

Chang et al (2018) conducted an exploratory study/research about the impact of mindfulness meditation (MM) on the life quality (QoL) of cancer outpatients. The researchers conducted a pilot study, including all cancer type patients into the exploratory study. The researchers created a meditation intervention comprising of 3 sessions conducted monthly. The researchers measured intervention with the help of the World Health Organisation QoL instrument. The research found post-intervention scores to be higher compared to pre-intervention. The usual care (UC) patients showed no significant different pre and post-intervention. These researchers conclude that MM is beneficial in improving the QoL of cancer patients, benefits that persist more than three months after treatment. On average, the gain score for physical health was at 11.2 points, while that of the psychological health aspects was 7.3 points. These authors conclude that MM was effective in a population of diverse cancer patients and is, therefore, suitable for general use. Indeed, the findings of this study support the authors’ conclusion.

2) Anand and Das (2018)

These authors also looked at the impact of a meditation technique known as Vipassana on lowering psychological stress in cancer patients. They followed a pre- and post-test research design. The authors used a sample of 30 participants grouped into a control and experimental group. Both groups consisted some cancer patient. The experimental group was passed through Vipassana meditation training for a month, while the control group was given no intervention. ICMR Psychosocial Stressor Questionnaire was used to measure the levels of psychosocial stress in participant. The researchers compared pre and post data using Wilcoxon Signed Rank Test. They then calculated Z values for the psychosocial stress in the groups. The results showed a substantial difference between post and pre scores for the psychosocial stress in the experimental group with values of p< .01 or Z =3.41. The authors found a significant reduction in psychosocial stress among the experimental group participants with no reduction seen in the control. These results support their findings that, indeed, Vipassana meditation is a viable approach in reducing psychosocial stress in cancer patients and that practicing this technique is an appropriate method of living while sick or even when a person is in good health.

3) Carlson et al. (2013)

Carlson et al. (2013) conducted a randomised controlled trial (RCT) to compare the effectiveness of two group interventions that are supported, including Mindfulness-Based recovery of cancer and Supportive Expressive Group Therapy for Breast cancer survivors who are distressed. Through a multisite RCT of 271 participants and an eighteen-hour contact with a professional, these authors found a positive reduction of stress symptoms in women who participated in the mindfulness based approach, with values for the experimental group at P= .009, while the supportive group was p= .012. Meanwhile, for the control group, the value for Mindfulness based intervention was p= .024. These values support their conclusion that mindfulness based cancer recovery was much better at reducing stress levels, improving QoL and enhancing social support for breast cancer survivors who are distressed compared to the other intervention

4) Ott, Norris and Bauer-Wu, (2006)

In this study, the authors conducted a critical review of pieces of literature about the clinical applicability of mindfulness meditation in cancer patients and oncology. The researchers conducted a literature search in Ovid, PsycInfo and MEDLINE and reviewed other literature from APA, Health Care and the Centre for Mindfulness in Medicine. These authors critically and systematically evaluated every abstract and article to understand their research questions, purpose statement, sample size, study design, mindfulness intervention, results and outcomes. Most of the studies examined looked at prostate and breast cancer and the use of mindfulness intervention in clinical settings. They found that this intervention enhanced psychological functioning, enhanced patients’ coping, reduced stress and improved their general wellbeing. The authors found that most of the literature they studied used rigorous methods, controlled design and randomised approach to study the impact of MM on cancer patients in different treatment settings. The findings of the systematic review support their findings that behavioural interventions such as MM is viable for oncology and can be used by clinicians in this setting to help treat cancer patients’ psychosocial challenges.

5) Schellekens et al. (2014)

In this study, Schellekens et al. (2014) aimed at examining the efficacy of Mindfulness-Based Stress Reduction on reducing the psychological stress in lung cancer patients compared to treatment as usual. These researchers conducted a RCT to compare treatment as usual to Mindfulness-Based Stress reduction. In the pilot-stud with 16 participants in a mindfulness-based stress reduction program, the authors found this intervention to be acceptable and feasible in lung cancer population. Their findings support the conclusion that this information is valuable because it shows that mindfulness stress reduction is among the few available programs for psychosocial care that can help alleviate psychological distress among cancer patients.

Controversies in the literature concerning their current use of their methods and procedures

Exploratory Study Design

The use of an exploratory research design by Chang et al. (2018) to study and compare the effectiveness of two group interventions that are supported, including Mindfulness-Based recovery of cancer and Supportive Expressive Group Therapy for Breast cancer survivors attracts several controversies because of the method’s weaknesses. First, this design is often used to assess issues that are yet to be defined clearly. It helps to helps in identifying the most appropriate research design, method of data collection and subject section. The results, however, are not very useful in making important clinical decisions but offer some clue concerning a particular situation (Singh, 2021). According to Singh (2021), exploratory research are very information based on it being that secondary data that is accessible is used or the qualitative data acquired through informal discussions.

Researchers argue that exploratory research is not quantitative research and that using their findings as the final can result in wrong decisions (Singh, 2021). Most exploratory research designs supply researchers with qualitative information and judgemental interpretation of findings. Additionally, Chang et al. (2018) also indicate the likeliness of bias in their study, confirming the fact that qualitative researches are often susceptible to a lot of interpreter bias. For instance, conclusions obtained from focus groups may not be clear. A researcher may not know if the interviewed participants or subjects understood the concept or idea behind the research questions, or if they overstated their answers. These issues make exploratory findings to be considered as preliminary evidence (Singh, 2021).

Moreover, most exploratory methods use small/modest samples, which are not quite a sufficient representation of the whole population because they are not selected based on probability. This may fail to provide an average situation. According to Singh (2021), it is important to conduct quantitative research before any important decisions are taken to ensure there is enough sample for precise measurements. Regardless of these limitations, exploratory research provides important insight concerning the issue being researched (Singh, 2021). This article, from the findings, has provided important insight about the effectiveness of two group interventions, including Mindfulness-Based recovery of cancer and Supportive Expressive Group Therapy for Breast cancer survivors dealing with disease related stress.

A pre-test and post-test research design

Anand and Das (2018) used a pre-test and post-test research design to examine the impact of a meditation technique (Vipissana) on cancer patients. A pre-test and post-test research design entails an experiment where measurements are obtained on participants prior to and after treatment is done (Alessandri, Zuffianò and Perinelli, 2017). This design is often used in both quasi-experimental and experimental research and does not always require control groups. Anand and Das (2018) have indicated several issues with this method, especially related to internal validity. Internal validity is the level that a research establishes a viable cause-and-effect association between a treatment intervention and the results. One of the things or factors that influence internal validity in a pre-test and post-test research design is selection bias. Anand and Das (2018) have noted that they experienced this challenge because it was challenging to find a comparable control group and a treatment group.

Additionally, it is argued that most often, the history of participants taking part in the study experience issues outside the research, which impact the study measurements prior and post treatment. Furthermore, biological changes, especially related to the disease in participants also influences the measurements. Attrition is the other issue that influences measurements in study design when a participant leaves the research before post-measurement data are taken (Alessandri, Zuffianò and Perinelli, 2017). Nonetheless, threats to the issue of internal validity are often reduced through random assignment or random selection of participants to experimental or control groups (Alessandri, Zuffianò and Perinelli, 2017).

A randomised control trial/RCT

Carlson et al. (2013) conducted a randomised controlled trial (RCT) to compare the effectiveness of two group interventions that are supported, including Mindfulness-Based recovery of cancer and Supportive Expressive Group Therapy for Breast cancer survivors. Despite its popular use in health related research, it is associated with several issues, particularly those related to its design, participation barriers, structure and conduct, costs, reporting and analysis. RCT design follows systemic reviews of already existing evidence, leading to well-informed questions and specifying the interventions, participants and results obtained. Additionally, a wide participant eligibility criterion leads to high representativeness of the general population and excellent rates of recruitment to the study. It is usually important to ensure that outcome measures are socially and clinically relevant, are valid, defined, reliable and sensitive to change (Hariton and Locascio, 2018).

It is currently an empirical problem whether this research design can be used to inform policy around mental health. Without proper research design innovations, it is likely that the outcomes produced by RCT will have little practical use, particularly if the applied model cannot control the impact of social interaction and complexity between dynamic systemic change and social complexity. There are other limitations associated with patient participation in the study. Usually, patients that take part in RCT studies are carefully selected reducing representativeness. Additionally, the numbers of patients that take part in RCT studies like that of Carlson et al. (2013) are small and they take part in short treatment periods compared to routine management or treatment of similar chronic illnesses. Insufficient compliance with the protocols of the study also results in false-positive or false-negative results. It is also usually challenging to monitor or measure compliance to the protocol of the study to be followed by participants (Hariton and Locascio, 2018).

Even though RCT is efficient in clinical research, it is being criticized as an approach that follows imperfect experimental models on less representative populations (Schellekens et al., 2014). Some of the criticisms are also based on the notion that RCT studies are done on controlled environments. The design and implementation, as well as the reporting of RCT have also been associated with biased interpretations that either oppose or favour treatment (Hariton and Locascio, 2018).

Critical Review

Ott, Norris and Bauer-Wu, (2006) conducted a critical review about the clinical applicability of mindfulness meditation in cancer patients and oncology. The researchers conducted a literature search in Ovid, PsycInfo and MEDLINE and reviewed other literature from APA, Health Care and the Centre for Mindfulness in Medicine. They, however, site a few limitations related to this research design. One obvious limitation is the fact that the review appraises published articles and is not a primary research. This method is faster and simpler when researching about a research question because it does not involve the collection or analysis of primary data. In addition, the presentation of the reviewed data is flexible allowing a word count of approximately 8000 words. However, the authors note that this research design does not include hypothesis testing and lacks originality unlike empirical studies (Cooper et al., 2018). Cooper et al. (2018) agrees with this claim stating that critical reviews, including systematic and critical reviews cannot be used to convince publishers about the novelty of the study is the data is presented weakly.

Alternative study designs

Evidence has found that these conventional research designs concentrates on measuring, as well as reporting on the effectiveness of programs or treatment interventions, which often find conflicting or mixed evidences (Booth Briscoe and Wright, 2020). This offers little clue concerning why a treatment intervention worked or failed when it is used in different circumstances or contexts (Booth Briscoe and Wright, 2020). An alternative to the critical review design is the realist review. This method provides complex answers to complex issues and provides the practice and policy communities with detailed, rich and practical understanding about sophisticated social intervention (Booth Briscoe and Wright, 2020). This is important when planning or implementing programs or interventions at local, regional or national levels (Booth Briscoe and Wright, 2020).

The alternative to pre-test and post-test, RCT and exploratory research designs is carrying out observational studies. Evidence suggests that compared to the other methods, observational studies are less costly and have great timeliness. Additionally, they can be used for a broader variety or range of participants or patients thereby increasing representativeness (Mueller et al., 2018). However, there are also concerns that this method has inherent bias, which has limited its use in the comparison of treatment interventions. As a result, observational studies are used mostly to identify prognostic indicators and risk factors in circumstances where controlled and randomised trials raise ethical issues or are impossible to do (Mueller et al., 2018). However, evidence suggests that observational studies usually inflate the effects of positive treatment, compared with controlled and randomised trials. Nonetheless, methodological improvements are usually done using more sophisticated data sets or better statistical techniques (Mueller et al., 2018).

The methodological and/or analytical reasons that may explain differences in findings between studies

Different methods of data analysis chosen in the different articles had a significant effect on the results reported. The different methods of data analyses had different variability, a situation that affects the analytic results obtained by different researchers. The different choices made by the authors or researchers of the articles in their statistical analyse of data have also resulted in different findings. The differences in findings emerge from the different covariates examined and the statistical analysis choices made in every article. It is evident from the various researches that different statistical methods were chosen.

For instance

Carlson et al. (2013) followed a descriptive statistical analysis approach with specific assumptions of interclass coefficient, an inflation factor, and a two-tailed α value, a dropout rate value and a power value to carry out a RCT of mindfulness-based intervention against a supportive group intervention. This is unlike Chang et al. (2018) who conducted an IBM SPSS statistical data analysis using windows software and independent t-tests and paired t-tests to compare pre and post-intervention QoL values. P values and α level values were also determined in this article. These are totally unlike Anand and Das (2018) who used the Wilcoxcon Sign Rank Test to examine the difference between scores pre and post treatment. On the other hand, Ott, Norris and Bauer-Wu, (2006) conducted a critical review of evidences published in the past. Analysis of the evidences were critiqued by different reviewers, a research coordinator, a researcher and a clinician who determined their characteristics, sample size, methods used, interventions undertaken, study results an study measures. Results were then summarised based on common themes. The last research conducted by Schellekens et al. (2014) also conducted a descriptive statistical analysis, involving a t-test, power value determination, alpha values intra-cluster correlations. Evidently, with these diverging data analysis techniques, the results obtained are different.

A critique of the study sampling procedures or protocols

In the exploratory study conducted by Chang et al. (2018), a nonrandomized sampling approach was followed with no specific sampling approach. Patients willingly joined either the mindfulness meditation group or the usual care group and the two groups were assessed to compare the effectiveness of two group interventions. Meanwhile, Schellekens et al. (2014) followed a randomised sampling with participants being randomised between treatment as usual (TAU) and Mindfulness-Based stress reduction. In their sampling, patients were invited to go through an interview to assess their eligibility or inclusion criteria. Those who met the inclusion criteria were then invited to answer some questions to determine their interest to participate, after which an invitation is sent to take part in the research interview.

Anand and Das (2018) also used random sampling and categorization of patients into control and experimental groups with both group comprising some cancer patients. Carlson et al. (2013) also randomly assigned participants into different intervention training groups. In comparison to the others and being a critical review, these authors used an inclusion criterion where only original researches that examined the impact of mindfulness-based treatment on cancer populations were reviewed.

Conclusions about the reliability/validity/sensitivity/specificity of measures used

Unlike non-randomised study, controlled and randomised trials offer great sensitivity, validity and reliability and provide comprehensive results on the benefits and risks of interventions. Controlled and random trials offer higher degree of probability and the statistical tests are easily interpretable by researchers. Therefore, the acquired outcomes from these interventions have high reliability levels compared to non-randomised research techniques. Randomised trials also avoid errors when sufficiently powered, helping to stop problems where null hypotheses are either accepted or incorrectly rejected.

The data analysis techniques used in the literature and related controversies

Quantitative statistics: Descriptive statistics

Some of the articles have used descriptive statistics to describe various features. For instance, Carlson et al. (2013) have used descriptive statistics to determine and present secondary outcomes, pre-protocol and intent-to-treat analyses of those who attended and completed post and pre-intervention assessments. One of the controversies associated with descriptive statistical analysis is that it often does not go past making conclusions. Additionally, the conclusions made in descriptive statistics depend on the hypotheses already formulated by other researchers. A measure of central tendency, mean is conspicuous in Carlson et al. (2013) where the authors have shown a significant reduction of stress signs among research participants that went through mindfulness-base cancer recovery, a mean change of about -19.3 compared with the supportive expressive groups that had a mean change of -9.46.

The other data analysis technique that has been used is inferential statistics. This approach has been used to examine the impact of Mindfulness-based interventions on sample participants who represent the general population. Inferential statistics of the sample collected is then used to make conclusions or reasons concerning eighty to ninety percent of the entire population (Amrhein, Trafimow and Greenland, 2019). The inferential techniques that have evidently been used in some of the articles are hypothesis testing and estimating parameters. Estimating parameters have been used on sample populations to show that mindfulness-based interventions are effective in reducing stress among cancer patients/populations. On the other hand, hypotheses tests have been used by some of the articles to answer some research question. These analysis techniques have helped showcase the relationship or association between various variables. These techniques are visible in the articles by Anand and Das (2018), Chang et al. (2018) and Schellekens et al. (2014). The use of t-tests, and correlations, inferential statistical analysis methods are also common in the article by Schellekens et al. (2014), comparing treatment as usual and mindfulness-based stress reduction.

The statistical analyses in these articles have allowed researchers to derive data from their sample populations and research data. Using these statistical data analyses methods, data is collected automatically through surveys and other static methods like examining preferences. Quantitative data analysis research methods are strong in providing descriptive data, which allows researchers to have a preview of the user population. However, this method encounters challenges with regard to interpreting the data. Without important that that can help in the interpretation of the acquired numbers, it become quite challenging to confidently say whey the intervention is disapproved or approved. Data deficiency can also result in critical errors in developing an intervention or treatment for a population (Sheard, 2018).

Another limitation of the quantitative approach of data analysis is that it over-relies on the sample size and p-value. This P-value represents a statistical value, which shows the likelihood that the findings were the outcome of a probability. For a p-value that is below .05, it is argue that the results of a study are statistically significant. This means that there is a 5% less chance of the findings being the results of probability (Sheard, 2018). The other advantage is that the p-value can be manipulated by size of the sample. However, a sufficient size of sample is required to obtain substantial statistical power, which shows that the findings are accurate. Smaller sample sizes leads to underpowered studies that lack statistical significance, even with accurate findings. Therefore, the challenge of quantitative data analysis and studies is that small samples make it challenging to determine or find a relationship. However, it is also risky to use an extremely large sample size because the statistical power becomes meaningless (Sheard, 2018).

How the study design and research questions determined the subsequent analysis

Quantitative research design in the various examined articles followed a specific process that determined the subsequent analysis. First, the researchers familiarised with the relevant data management tools, including data analysis software and screening tools. This then led to understanding the different variables, dependent and independent variables and the measuring scales that would be used such as ration, interval, ordinal and nominal measures. This then leads the researcher to choose appropriate descriptive statistics that would be used. Running the descriptive statistics helped the researchers to summarise the data sets via central tendency measures (median, mode and mean), dispersion measures or measures of distribution. The researchers then ran relevant inferential statistics to allow them to assess their capacity to make conclusions about their findings, if there are relationships between the variables being studies in a sample population and whether an intervention approach can be used in the general population. Afterwards the researchers selected appropriate statistical tests based on the variables being studied, the measurement scales, and shape of distribution, as well as the research questions. The researchers then looked for the statistical significance through the p-value that examines the chance of the research outcomes being less of a coincidence. For instance, a lower p-value shows that a research outcome is genuine.

The most dominant analytical technique and alternative analytical approaches

From these articles, it is evident that quantitative research and descriptive data analysis methods are common in finding the effectiveness of mindfulness-based interventions on reducing stress among cancer victims. The outcomes have been presented in numbers and scales that extend themselves for further interpretation and analysis. However, Statistical data analysis, which involves the collection of data, validation and interpretation, as well as performing some statistical operations have been, used in different health studies. However, other new methods like diagnostic analysis can be applied to support statistical analysis. Evidence suggests that this method comprises data discovery, data drill down or mining. Diagnostic analysis involves identifying anomalies in research, determining causal association or the hidden relationship between events or variables and uncovering hidden data through regression analysis, probability theory and filtering (Smucny et al., 2019).

Conclusion

Different articles examined in this piece of writing have found a positive influence of mindfulness-based interventions on the reduction of stress among cancer patients of victims. This writing has found that different articles looking at this relationship use varying research designs. These include exploratory study designs that compare variables though it is not as good as quantitative methods, a pre-test and post-test research design that takes measures prior to and post treatment, a randomised control trail that assigns participants randomly into a control and experimental group and measures are compared in the groups after the intervention. Other research designs that have been found here include conducting critical reviews of literature, realist reviews and conducting observational studies. The research has found that although quantitative research is common in the area of health and in finding the cause and effect relationship, other techniques of analysis such as diagnostic analysis can be used as well.

References

Anand, H. and Das, I., 2018. EFFECT OF VIPASSANA MEDITATION IN REDUCING PSYCHOSOCIAL STRESS AMONG CANCER PATIENTS.

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Singh, A., 2021. An Introduction to Experimental and Exploratory Research. Available at SSRN 3789360.

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