The Faculty of Medicine at the University of Banja Luka is one of the key medical higher education institutions in Bosnia and Herzegovina, with an important role in clinical training, health-related study programmes and medical research. Alongside general medicine, the faculty includes dental medicine and health-oriented programmes, which creates a natural connection between medical, dental and applied health research.
Students and researchers work with many different types of data – clinical findings, laboratory values, diagnostic tests, questionnaires, epidemiological datasets, dental measurements and data from health-related study programmes. For that reason, statistical data analysis is a central part of bachelor’s, master’s, doctoral and scientific papers. It is not enough to run a test in SPSS; the research design, data structure, unit of analysis and clinical interpretation must be clear from the beginning.
Statistical data analysis for the Faculty of Medicine, University of Banja Luka
The Faculty of Medicine at the University of Banja Luka is relevant for research in clinical medicine, dental medicine, health studies, public health, laboratory diagnostics, nursing, physiotherapy and other applied health fields. These papers often combine clinical data, questionnaire results, laboratory findings, functional assessments and quality of life data. Because of that, statistical analysis should be planned as part of the research process rather than added at the end as a technical step.
Medical research in Banja Luka often analyzes treatment outcomes, diagnostic accuracy, complications, epidemiological indicators, laboratory values, hospitalization, comorbidities, clinical status, functional status and patient quality of life. Dental research may focus on oral health, caries, periodontal parameters, orthodontic measurements, implantology, prosthodontics, radiological findings, dental anxiety and patient satisfaction with dental care. Health-related programmes frequently examine knowledge, attitudes, satisfaction, quality of care, communication in healthcare, burnout, patient safety, rehabilitation outcomes and public health behavior.
Analyzing such data requires a careful methodological approach. Continuous variables such as laboratory values, age, blood pressure, body mass index or duration of hospitalization are analyzed differently from categorical variables such as diagnosis, treatment group, complications or treatment outcome. Ordinal variables, including Likert scales, pain scales, functional classifications and satisfaction levels, require additional attention because individual items and total scores are not interpreted in the same way.
SPSS is commonly used in this type of work, but software does not replace statistical reasoning. The research question, hypotheses and analysis strategy must be defined clearly. A practical starting point is the page on medical statistics and data analysis. For broader work with SPSS, theses, dissertations and research projects, the overview of statistical help and SPSS data analysis is also useful.
For students and researchers from Bosnia and Herzegovina, the analysis must fit the specific academic and research context. Papers from Banja Luka may be clinical, dental, epidemiological or health-science oriented. The country-specific page on statistical help for Bosnia and Herzegovina is therefore relevant when working with SPSS and academic research data from BiH.
Medical, dental and health-related data
Medical data may be structured at several levels. Sometimes the unit of analysis is the patient, sometimes a finding, hospitalization, treatment procedure or, in dental medicine, a tooth, implant, tooth surface or radiological finding. A wrongly defined unit of analysis can lead to incorrect conclusions. If one patient contributes several teeth or repeated measurements, observations are not always fully independent.
In health-related programmes, questionnaire data are very common. Students analyze attitudes, knowledge, satisfaction, workload, stress, burnout, patient safety, quality of life, health behavior or perception of quality of care. Such studies often use Likert scales, total scores and subscales. A high-quality analysis should describe the instrument, response range, scoring direction, reliability and interpretation of higher values.
Dental research adds topics such as dental anxiety, oral health-related quality of life, periodontal parameters, caries indices, orthodontic measurements, prosthetic outcomes and implant success. Objective clinical indicators and subjective patient-reported outcomes should be distinguished because they often require different analytical approaches.
Choosing statistical methods
Descriptive statistics summarize the sample and variables. For approximately normally distributed continuous variables, mean and standard deviation are often reported; for skewed distributions, median and interquartile range are more appropriate. Categorical variables are described with frequencies and percentages.
Group comparisons may use t-test, Mann-Whitney U test, ANOVA, Kruskal-Wallis test, chi-square test or Fisher’s exact test depending on the research question, variable type, number of groups, sample size and distribution. In medicine and dental medicine, the test should be selected based on data properties rather than habit.
Correlation analysis is used when the goal is to examine associations between variables. Pearson correlation is appropriate for approximately normally distributed continuous variables, while Spearman correlation is appropriate for ordinal variables, Likert scales and non-normal distributions. In medical papers, correlations are often used for relationships between laboratory findings and clinical outcomes, pain and quality of life, dental anxiety and frequency of dental visits, or functional status and rehabilitation outcomes.
Regression analysis evaluates predictors of outcomes. Linear regression is used for continuous outcomes, logistic regression for binary outcomes and ordinal regression for ordinal outcomes. In medicine, regression may identify factors associated with complications, treatment response, quality of life or hospitalization. In dental medicine, it may be used to predict dental anxiety, oral health, patient satisfaction or treatment success.
ROC analysis is useful when assessing diagnostic accuracy of a test, biomarker, laboratory value, clinical score or dental indicator. Survival analysis, including Kaplan-Meier and Cox regression, is used when the outcome is time to event, such as time to complication, relapse, death, implant failure or repeated procedure.
SPSS analysis and interpretation
For many student and scientific papers, SPSS is sufficient. More important than the software itself is the workflow: dataset preparation, checking variables, describing the sample, selecting tests, running analyses, preparing tables and interpreting results. The SPSS analysis guide gives a structured introduction, while the page on which statistical method to use and when helps with method selection.
Results should not be interpreted only through p-values. Statistical significance does not automatically imply clinical relevance. The direction of the effect, effect size, confidence intervals, sample limitations and practical meaning should also be described.
Sample size and study planning
Sample size should match the research question, primary outcome, expected effect size, number of groups and planned analysis. Too small a sample may fail to detect real effects, while very large samples may make clinically small differences statistically significant. For planning, the guide on how many participants are needed can be useful.
Statistical support is allowed when the author remains responsible for the final text, scientific interpretation and conclusions. The distinction between statistical support and inappropriate authorship is discussed in the text is it allowed to get statistical help.
Workflow
The workflow includes reviewing the topic and hypotheses, checking dataset structure, defining variables, selecting methods, performing SPSS analysis, preparing tables and interpreting results. In medical and dental papers, study design, unit of analysis, primary outcome and presentation of results should be clarified early. A practical overview is available in the guide how working with a statistician looks step by step.
I prepare statistical results and suggest how to present them in the paper. The author remains responsible for the final text and interpretation.
FAQ
Can clinical research be analyzed?
Yes. Treatment outcomes, complications, diagnostic tests, laboratory findings, functional status and patient follow-up can be analyzed.
Can dental research be analyzed?
Yes. Oral health, dental anxiety, periodontal indicators, orthodontic parameters, implant outcomes, radiological findings and patient satisfaction can be analyzed.
Can surveys from health-related programmes be analyzed?
Yes. Likert scales, knowledge, attitudes, satisfaction, quality of life, burnout, patient safety and health behavior can be analyzed.
Is SPSS sufficient?
For many bachelor’s, master’s, doctoral and scientific papers, SPSS is sufficient. More complex hierarchical dental data, longitudinal models or advanced analyses may require additional tools.
Keywords
Faculty of Medicine Banja Luka statistics, Faculty of Medicine University of Banja Luka SPSS, dental medicine Banja Luka data analysis, SPSS medicine Bosnia, statistical data analysis medicine BiH, clinical research Banja Luka, dental data analysis, epidemiology Bosnia statistics, health studies Banja Luka statistics, healthcare survey analysis
Metatext
Statistical data analysis for the Faculty of Medicine, University of Banja Luka, dental medicine and health studies: SPSS, clinical research, epidemiology, dental medicine and interpretation of results.
