The Faculty of Pharmacy at the University of Sarajevo is one of the leading academic institutions in Bosnia and Herzegovina for education and research in pharmacy, pharmaceutical biochemistry, laboratory diagnostics, pharmaceutical technology, toxicology, pharmacology, clinical pharmacy and public health. Due to the strong connection between pharmacy, medicine and laboratory sciences, research conducted at this faculty often requires precise statistical data analysis and careful interpretation.
For students and researchers, statistical analysis is an essential component of bachelor’s, master’s, doctoral and scientific research work. Data may originate from laboratory measurements, analytical methods, pharmacological studies, biochemical analyses, questionnaires, stability testing, quality control, clinical pharmacy research or pharmacoepidemiological databases. Therefore, each analysis must be tailored to the specific research question.
Statistical data analysis for the Faculty of Pharmacy Sarajevo
Pharmaceutical research covers a wide range of methodologies. Some studies focus on laboratory measurements and method validation, others on formulation comparison, stability analysis or pharmacoepidemiological data, while survey-based research may analyze adherence, rational drug use or patient satisfaction. This means that statistical analysis must be selected according to the type of data, study design and intended interpretation.
Laboratory and biochemical studies often involve continuous variables such as concentrations, biomarker values, absorbance, pH levels, stability parameters and release profiles. These require descriptive statistics, distribution analysis, group comparisons, correlations and regression models. In validation studies, additional indicators such as precision, accuracy, linearity, detection limits and quantification limits may also be important.
Clinical pharmacy and pharmacoepidemiology studies often focus on adverse effects, adherence, drug interactions and prescribing patterns. These studies frequently involve categorical and ordinal variables, questionnaires and multivariate models. The analysis must connect statistical findings with pharmaceutical and clinical relevance.
SPSS is commonly used for these analyses, but methodological decisions must always align with the research design. A useful starting point is the section on statistical help for SPSS and regional context is available at statistical support for Bosnia and Herzegovina.
Characteristics of pharmaceutical data
Pharmaceutical data include continuous, categorical and ordinal variables. Continuous variables include concentrations, laboratory values, stability parameters and analytical results, while categorical variables include therapy types, adverse events, pharmaceutical forms and outcomes. Ordinal variables are often derived from Likert scales.
It is essential to distinguish between experimental laboratory data and human-based data. Laboratory data are more controlled, whereas clinical and survey data involve greater variability because they depend on patients, healthcare professionals, therapy regimes and real-world practice conditions.
Survey-based research requires careful handling of scales, scoring and internal consistency. Total scores, subscales and single items should not be treated as identical because their measurement levels and interpretation may differ.
Research design
Laboratory studies focus on validation, stability and physicochemical properties. Such studies should clearly describe samples, repetitions, batches, measurement conditions and quality criteria. Statistical presentation should demonstrate variability, reproducibility and stability of results.
Cross-sectional studies analyze knowledge, attitudes, behavior, self-medication, adherence or perception of pharmaceutical care. They capture a situation at one point in time and are especially common in questionnaire-based research.
Retrospective studies use existing datasets, such as pharmacy records, medication lists, adverse event reports, laboratory findings or clinical documentation. Prospective studies allow controlled data collection, for example in pharmaceutical care interventions or adherence monitoring over time.
Guidance on method selection is available in the article which statistical method to use and when.
Statistical methods
Descriptive statistics summarize data, while inferential methods test hypotheses and identify relationships. Continuous laboratory values are commonly described using means, standard deviations, medians and measures of dispersion. Categorical variables are presented using frequencies and percentages.
Group comparisons may use t-tests, ANOVA, Mann-Whitney U tests, Kruskal-Wallis tests, chi-square tests or Fisher’s exact tests. The choice depends on distribution, measurement level, sample size and research question.
Correlation analysis examines relationships between variables, such as concentration and time, knowledge and adherence, or satisfaction and perceived pharmaceutical care. Regression models identify predictors and can be applied to continuous, binary or ordinal outcomes.
ROC analysis and validation metrics are important in analytical and diagnostic research, especially when cut-off values, sensitivity, specificity or diagnostic accuracy need to be evaluated.
SPSS and interpretation
Statistical results must be interpreted in context. A significant p-value is not enough if the pharmaceutical meaning of the result remains unclear. Effect size, confidence intervals and practical relevance should also be considered.
The SPSS guide provides a structured approach. If a project is still in the planning phase, methodological clarification before data collection is especially useful.
Sample size
Sample size depends on study design. In laboratory studies, samples, repetitions and batches must be distinguished. In survey studies, the number of respondents is central. In clinical pharmacy research, the required sample size depends on the primary outcome and expected effect size.
Guidance is available in how many participants are needed.
Ethics and responsibility
Statistical support includes analysis, method selection and presentation, while the author remains responsible for interpretation, as explained in is it allowed to get statistical help.
I prepare statistical results and suggest how they should be presented. The author remains responsible for the final text and interpretation.
FAQ
Can pharmaceutical laboratory studies be analyzed?
Yes. Concentrations, stability, method validation, formulation comparisons, release profiles and quality control data can be analyzed.
Can clinical pharmacy research be analyzed?
Yes. Therapy adherence, adverse effects, drug interactions, treatment outcomes, patient satisfaction and pharmaceutical care can be statistically analyzed.
Can pharmacy questionnaires be analyzed?
Yes. Likert scales, knowledge, attitudes, self-medication, adherence, satisfaction and perception of pharmaceutical care can be analyzed.
Is SPSS sufficient?
For many bachelor’s, master’s, doctoral and scientific papers, SPSS is sufficient. More complex experimental, longitudinal or highly advanced models may require additional tools.
Keywords
Faculty of Pharmacy Sarajevo statistics, pharmaceutical data analysis SPSS, clinical pharmacy research Bosnia, laboratory analysis statistics, pharmaceutical biochemistry statistics, method validation statistics, therapy adherence analysis
Metatext
Statistical data analysis for the Faculty of Pharmacy Sarajevo: SPSS, pharmacy, laboratory research, clinical pharmacy, pharmaceutical biochemistry and interpretation.
