Statistical techniques are essential in research studies and projects as they equip the researcher to properly collect and analyze data. The analyzed data is able to assist the researcher to draw meaningful interpretation of research findings. The selection of appropriate statistical techniques depends on three components of research (Mishra et al., 2019). One component is the aim and objective of the study, the purpose of the study being conducted (Mishra et al., 2019).
The second component identified by the authors was the type of data and distribution of the data used (Mishra et al., 2019). The third and last component that has influence on the type of statistical technique chosen by a researcher is the nature of the observations, whether comparing the means of the same group or comparing two independent groups (Mishra et al., 2019). In addition, for the use of nominal, original or discrete data, nonparametric methods are used, in comparison, continuous data requires the use of parametric and nonparametric methods (Mishra et al., 2019). Selection of statistical techniques such as descriptive statistics and inferential statistics also has direct influence on a researchers conclusion as findings from the interpretation of data are affected by methods used.
In a study conducted by Shiraly et al. (2021) the aim of the study was to evaluate the knowledge and practice of effective patient-provider communication. The data collection tool utilized was a two part questionnaire with the first part collecting demographic and professional data such as age, gender, practice setting, years of experience and if any formal training on patient-provider communication (Shiraly et al., 2021). The second part was a 5-point Likert scale focused on knowledge and practice questions related to effective patient-provider communication. The use of Blooms taxonomy was used to assess the knowledge base of providers.
Researchers reported that the use of SPSS can assist in verifying and analyzing data (Mishra et al., 2019). Shiraly et al (2021) used SPSS to conduct descriptive and inferential data as the means and standard deviation were calculated for demographic and professional data (Shiraly et al., 2021). Inferential data was used to test the correlation between knowledge and practice by utilizing the Pearson rank correlation coefficient (Shiraly et al., 2021). In addition, the student t-test was utilized to measure associations between knowledge and practice in addition to categorical variables identified by the authors such as age, gender, and practice setting (Shiraly et al., 2021).
The statistical methods used concluded that almost 80% of physicians possessed knowledge of effective communication however about 55% scored moderately when applied to practice (Shiraly et al., 2021). The knowledge and practice mean were 41.5 with standard deviation of 2.8 and 38.7 with standard deviation of 3.4 respectively (Shiraly et al., 2021). The mean knowledge score was higher in female providers and those working in private practice (Shiraly et al., 2021). Moreover, there was a positive correlation between mean knowledge and practice scores with p < 0.001, showing statistical significance. I anticipate utilizing similar statistical methods such as the Pearsons correlation test from existing studies as well as my own to show the relationship between patient-provider communication and treatment adherence. Considering the need for mean, median, standard deviation and other descriptive statistics will help describe data in a meaningful and useful way (Mishra et al., 2019). Although not a statistical technique I am also considering the use of Blooms taxonomy to assess the knowledge of effective communication with providers as well.
