The Faculty of Medicine of the University of Mostar is one of the key medical higher education institutions in Bosnia and Herzegovina, with a particularly strong academic, clinical and regional role in Herzegovina. Within the F2 SEO cluster, this article has dual value because it covers both classical medicine and the Dental Medicine Study Programme in Mostar. This allows the article to target two related but SEO-distinct search areas: statistical data analysis for medical papers and statistical data analysis for dental medicine research.
Unlike articles that focus only on medicine or only on dentistry, this article should simultaneously cover search terms such as Faculty of Medicine University of Mostar, Faculty of Medicine Mostar, Dental Medicine Study Programme Mostar, dental medicine Mostar, statistics medicine Mostar, SPSS medicine Mostar, statistical data analysis dental medicine, clinical research Mostar, medical data analysis Bosnia and statistical support for bachelor’s, master’s, doctoral and scientific papers. For this reason, f2-102 is one of the strategically important Bosnia and Herzegovina articles in the F2 cluster.
Statistical data analysis for the Faculty of Medicine, University of Mostar
Research at the Faculty of Medicine of the University of Mostar may cover a broad spectrum of medical, clinical, public health, laboratory, biomedical and dental topics. In the medical segment, data often come from hospital documentation, outpatient findings, laboratory tests, diagnostic procedures, clinical assessments, epidemiological databases or questionnaire-based research. In the dental segment, data may include oral health, dental anxiety, periodontal indicators, orthodontic parameters, prosthetic outcomes, implant results, radiological findings and oral health-related quality of life.
Because of this broad scope, statistical data analysis must be precisely adapted to the type of research. It is not the same to analyze laboratory values, treatment outcomes, complication rates, dental indices, patient satisfaction, Likert scales or quality of life. Each type of data requires a different database structure, a different selection of statistical tests and a different approach to interpreting results.
In clinical research, patient groups, therapeutic procedures, diagnostic findings, preoperative and postoperative parameters, treatment outcomes or risk factors are often compared. Such papers commonly use group comparison tests, correlation analysis, regression models, ROC analysis and survival analysis. It is especially important to distinguish statistical significance from clinical relevance.
In dental medicine, research often includes data at the level of the patient, tooth, tooth surface, implant, procedure or radiological finding. This is methodologically important because multiple measurements within the same patient are not always fully independent observations. Therefore, the unit of analysis must be clarified already during study planning: patient, tooth, implant, finding, procedure or total score.
Statistical support includes structuring the dataset, selecting appropriate statistical methods, performing SPSS analyses, preparing tables and suggesting interpretation of results. I prepare statistical results and suggest how to present them in the paper. The author remains responsible for the final text and interpretation of the results.
Internal linking
Internal links include general statistical help, Bosnia and Herzegovina hub, SPSS guides, method guides, pricing and FAQ pages:
Statistical help, SPSS data analysis, theses, dissertations and research
Statistical help for Bosnia and Herzegovina – SPSS, bachelor’s, master’s and PhD research
SPSS analysis – complete guide
Statistical methods – which analysis to use and when
Is it allowed to get statistical help?
Statistical data analysis price
Mostar as medicine + dental medicine
Unlike Sarajevo, which has the status of the central medical authority in Bosnia and Herzegovina, and Tuzla, which has a strong regional position for northeastern Bosnia, Mostar has a particularly important bilingual, regional and structural SEO value. The Faculty of Medicine of the University of Mostar should be positioned as the core article for Herzegovina, while the Dental Medicine Study Programme Mostar should additionally strengthen long-tail searches related to dental medicine.
This article should cover the following keyword groups: Faculty of Medicine Mostar statistics, Faculty of Medicine University of Mostar SPSS, statistical data analysis medicine Mostar, Dental Medicine Study Programme Mostar statistics, dental medicine Mostar SPSS, clinical research Mostar, dental data analysis, epidemiological analysis Bosnia, statistical support medicine Bosnia and SPSS for medical papers.
It is especially important that the article clearly distinguishes the medical and dental segments. The medical segment includes clinical research, diagnostics, treatment outcomes, laboratory findings, public health and epidemiology. The dental segment includes oral health, caries, periodontal indicators, orthodontics, implantology, prosthodontics, dental anxiety, radiological findings and oral health-related quality of life.
Data types in medical and dental research
In medical papers, continuous variables are often analyzed, such as age, blood pressure, laboratory values, biochemical parameters, body mass index, duration of disease, length of hospitalization, number of complications or quality of life scores. These variables require checking the distribution, selecting appropriate measures of central tendency and choosing parametric or nonparametric tests.
Categorical variables include sex, diagnosis, treatment group, disease status, presence of complications, positive or negative test result, type of procedure, hospitalization, treatment outcome or presence of comorbidities. For such data, frequencies, percentages, chi-square test, Fisher’s exact test and logistic regression are used.
Ordinal variables often appear in disease severity classifications, pain scales, Likert scales, satisfaction levels, functional status and quality of life assessments. With ordinal data, it is important to distinguish individual items from total scores. Individual Likert items are usually analyzed as ordinal variables, while validated total scores may also be analyzed as continuous variables depending on scale structure and reliability.
In dental medicine, additional specific units of measurement appear. Data may be related to the patient, tooth, quadrant, tooth surface, implant, orthodontic parameter, radiological finding or individual dental procedure. If the same patient contributes several teeth or implants to the analysis, the data may be clustered and methodologically dependent. This structure must be considered when selecting the statistical approach.
Research design
Clinical medical studies
Clinical studies include treatment comparisons, treatment outcomes, complications, diagnostic procedures, laboratory parameters, functional status and patient quality of life. In such papers, the primary outcome, secondary outcomes, inclusion and exclusion criteria, follow-up period and statistical analysis plan should be clearly defined.
Dental clinical studies
Dental clinical studies may include periodontal parameters, caries indices, orthodontic measurements, prosthetic outcomes, implant results, radiological findings, oral health-related quality of life and patient satisfaction with dental procedures. In these papers, it is especially important to clearly define the unit of analysis and the structure of clustered data.
Epidemiological studies
Epidemiological studies analyze prevalence, incidence, risk factors, public health indicators, population behavior and disease distribution. In Mostar and Herzegovina, such topics may have particular regional significance because they can be linked to specific features of healthcare delivery, access to services and local population characteristics.
Retrospective studies
Retrospective studies use existing medical records, dental charts, laboratory findings, surgical protocols or hospital databases. Their advantage is data availability, while challenges include missing values, inconsistent documentation and limited control over original measurements.
Prospective studies
Prospective studies allow more precise planning of measurements and follow-up. Variables, measurement time points, primary outcome and statistical plan should be defined before data collection begins.
Cross-sectional studies
Cross-sectional studies analyze a situation at one point in time. They are common in bachelor’s, master’s and graduate theses because they allow relatively quick analysis of knowledge, attitudes, satisfaction, quality of life, pain, dental anxiety or health behavior.
Questionnaire-based studies
Questionnaire studies are used in medicine and dental medicine to measure quality of life, pain, dental anxiety, patient satisfaction, knowledge, attitudes, health behavior, adherence and perception of healthcare. Instrument description, scoring, reliability and possible factor structure are important.
Important statistical methods
Descriptive statistics
Descriptive statistics are used to present the sample and basic characteristics of variables. In medical and dental papers, it is important to decide carefully whether to present mean and standard deviation or median and interquartile range. Frequencies and percentages are used for categorical variables.
Group comparison tests
To compare two or more groups, t-test, Mann-Whitney U test, ANOVA, Kruskal-Wallis test, chi-square test and Fisher’s exact test are used. The choice depends on variable type, number of groups, sample size and distribution.
Correlation analysis
Correlation analysis is used to assess relationships between variables. In medical and dental research, common examples include the relationship between laboratory findings and clinical outcomes, pain and quality of life, dental anxiety and frequency of dental visits, oral health and sociodemographic factors, or functional status and rehabilitation outcomes.
Regression analysis
Regression analysis is used to estimate predictors of medical or dental outcomes. Linear regression is used for continuous outcomes, logistic regression for binary outcomes and ordinal regression for ordinal outcomes. In medical papers, regression can identify predictors of complications, therapy response or quality of life. In dental papers, it can identify predictors of dental anxiety, oral health or patient satisfaction.
ROC analysis
ROC analysis is used when the aim is to assess diagnostic accuracy of a test, biomarker, laboratory finding, clinical score or dental indicator. AUC, sensitivity, specificity and possible cut-off values are reported. ROC analysis is especially important in clinical and diagnostic studies.
Survival analysis
Survival analysis is used when the outcome is time to event, such as time to complication, relapse, hospitalization, death, implant failure or repeated procedure. Kaplan-Meier analysis and Cox regression are most commonly used.
Reliability and questionnaire validation
For questionnaires and scales, Cronbach’s alpha, item analysis, factor analysis and correlations with other measures are used. This is important for scales measuring quality of life, pain, dental anxiety, satisfaction, knowledge, attitudes and health behavior.
SPSS analysis and interpretation of results
SPSS is a common tool for statistical data analysis in medical and dental papers because it supports descriptive statistics, group comparisons, correlations, regression models, ROC analysis, reliability analysis, factor analysis and graphical presentation. However, SPSS alone does not guarantee a methodologically correct paper.
The key issue is correct interpretation of results. A p-value indicates statistical significance, but it does not automatically indicate clinical importance. In medical and dental papers, it is necessary to report the direction of the difference, effect size, confidence intervals, practical meaning of the findings and possible study limitations.
It is especially important not to interpret results in isolation. A statistical finding should be connected with the research question, previous literature, clinical logic and sample limitations. This improves the quality of the paper and reduces the risk of superficial interpretation.
Sample size and statistical power
Sample size in medical and dental research must be connected with the primary outcome, expected effect size, number of groups, number of predictors and planned tests. Too small a sample may fail to confirm a real difference, while very large samples may show statistically significant but clinically small differences.
In dental research, special attention should be given to the difference between the number of patients and the number of teeth, implants or measurement units. If an analysis is presented as if all teeth were independent, while several teeth actually come from the same patient, a methodological problem may arise. Therefore, the data structure must be clarified before analysis.
Typical topics for the Faculty of Medicine and Dental Medicine Study Programme in Mostar
Typical medical topics include clinical research, diagnostic accuracy, treatment outcomes, laboratory biomarkers, epidemiology, public health, quality of life, pain, functional status, chronic diseases, comorbidities, complications, treatment response, hospitalization, regression models, ROC analysis and survival analysis.
Typical dental topics include dental anxiety, oral health, caries, periodontal parameters, orthodontic indicators, implantology, prosthodontics, endodontics, radiological findings, patient satisfaction with dental care, oral health-related quality of life and validation of dental questionnaires.
Workflow
The workflow includes reviewing the topic and hypotheses, checking dataset structure, defining variables, selecting statistical methods, SPSS analysis, table preparation and interpretation of results. In medical and dental papers, it is especially important to clarify the study design, unit of analysis, primary outcome and result presentation at the beginning.
I prepare statistical results and suggest how to present them in the paper. The author remains responsible for the final text and interpretation of the results.
FAQ
Can clinical medical 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 ROC analysis be performed?
Yes. ROC analysis is appropriate when diagnostic accuracy of a laboratory test, biomarker, clinical score or dental indicator is assessed.
Can questionnaires from medicine or dental medicine be analyzed?
Yes. Likert scales, quality of life, pain, dental anxiety, satisfaction, knowledge, attitudes and health behavior can be analyzed.
Is SPSS sufficient?
For many bachelor’s, master’s, graduate, doctoral and scientific papers, SPSS is sufficient. For more complex hierarchical dental data, longitudinal models or advanced analyses, additional tools may be needed.
Keywords
statistics Faculty of Medicine Mostar, Faculty of Medicine University of Mostar SPSS, Dental Medicine Study Programme Mostar statistics, dental medicine Mostar SPSS, statistical data analysis medicine Mostar, statistical data analysis dental medicine, clinical research Mostar, dental data analysis, ROC analysis medicine, regression medicine Mostar, statistical support Bosnia medicine
Metatext
Statistical data analysis for the Faculty of Medicine, University of Mostar and Dental Medicine Study Programme Mostar: SPSS, clinical research, dental medicine, epidemiology, regression, ROC analysis and medical data analysis.
