The Faculty of Dental Medicine and Health Osijek is a unique higher education institution in Croatia because it combines dental medicine with a broad spectrum of health sciences within a single academic framework. This dual positioning makes it especially valuable from an SEO perspective, as it simultaneously covers dental medicine, nursing, physiotherapy, public health, laboratory diagnostics and other applied health disciplines. Within the F5 cluster, this article represents a central node connecting statistical data analysis for both dental and health-related research.
Students, researchers and doctoral candidates at the faculty work with diverse datasets: clinical dental parameters, oral health indicators, periodontal measurements, radiological findings, implant outcomes, as well as questionnaires, Likert scales, patient satisfaction, quality of life, pain, functional status, knowledge and attitudes of healthcare professionals and public health indicators. Such diversity requires a flexible and methodologically precise statistical approach.
Statistical data analysis for the Faculty of Dental Medicine and Health Osijek
The Faculty of Dental Medicine and Health Osijek should not be interpreted solely as a traditional dental school, nor purely as a health studies institution. Its strength lies in the integration of both domains. As a result, academic work may include strictly clinical dental data, applied healthcare research or a combination of both.
Dental research often includes clinical parameters such as periodontal indices, caries measurements, orthodontic metrics, radiographic findings, implant success rates and dental material testing. In contrast, health studies research typically includes surveys, Likert scales, patient-reported outcomes, rehabilitation indicators, functional tests and public health variables.
Statistical analysis must therefore be tailored to the specific type of data and research question. Analyzing periodontal depth measurements is fundamentally different from analyzing survey responses or quality of life scores. Incorrect methodological choices can significantly reduce the scientific validity of a study.
Statistical support includes dataset structuring, selection of appropriate methods, SPSS analysis, table preparation and interpretation guidance. I will prepare statistical results and suggest how they should be presented in the paper. The author remains responsible for the final text and interpretation.
SEO specificity: dental medicine and health sciences
This faculty occupies a distinct SEO position because it can rank for both dental medicine and health studies queries. Key search terms include dental medicine Osijek statistics, health studies Osijek SPSS, data analysis dental research, nursing statistics Osijek, physiotherapy statistics Osijek, public health Osijek analysis and questionnaire analysis in healthcare.
Within the SEO structure, this article should be clearly differentiated from pure dental faculty content such as Zagreb, which represents core dentistry. Osijek should be positioned as a hybrid node covering both dentistry and applied health sciences, capturing long-tail queries related to statistical analysis, SPSS support and research interpretation.
The article should also cover local SEO for Osijek, Slavonia and eastern Croatia, because many searches are highly practical: SPSS help, survey analysis, Likert scale analysis, hypothesis testing, interpretation of results and methodology support.
Data types in dental and health research
Dental research may include oral health indicators, caries prevalence, periodontal parameters, orthodontic measurements, radiological diagnostics, implant outcomes, prosthetic results, dental anxiety and oral health-related quality of life. Health sciences research includes demographic data, clinical indicators, pain measurements, functional status, rehabilitation outcomes, nursing care variables, health behavior, knowledge and attitudes, stress, burnout and public health indicators.
Many studies are based on questionnaires and scales. These include Likert scales, satisfaction scales, quality of life instruments, dental anxiety scales, workload assessments and knowledge evaluations. For such instruments, reliability and structural validity must be assessed.
In dental research, the level of measurement is especially important. Outcomes may be measured at the level of the patient, tooth, implant, tooth surface or treatment procedure. Multiple measurements within the same patient are not automatically independent observations.
Research design
Cross-sectional studies
Cross-sectional studies are very common in undergraduate and graduate research, analyzing data at a single point in time, such as knowledge, attitudes, satisfaction, quality of life, pain, dental anxiety, oral health or health behavior.
Group comparisons
Group comparisons may be based on gender, age, work experience, education, diagnosis, therapy, oral status, type of dental procedure, type of intervention, workplace or functional limitation. Depending on variable type, t-test, Mann-Whitney U test, ANOVA, Kruskal-Wallis test, chi-square test and Fisher’s exact test can be used.
Correlational studies
Correlational studies explore relationships between variables, such as dental anxiety and frequency of dental visits, pain and quality of life, stress and burnout, knowledge and health behavior, patient satisfaction and communication, or oral health and sociodemographic factors.
Intervention studies
Some papers examine effects of education, prevention, rehabilitation programs, therapeutic procedures, dental interventions or protocol changes. Pre-post measurement, control group, primary outcome and suitable tests are important.
Validation studies
Dental and health papers often use questionnaires and scales. If an instrument is translated, adapted or used in a new population, reliability, internal consistency, factor structure and association with relevant measures should be checked.
Statistical methods
Descriptive statistics
Descriptive statistics present sample characteristics and basic results. They include frequencies, percentages, means, standard deviations, medians, interquartile ranges, minimum and maximum values. Presentation should match the type and distribution of variables.
Group comparison tests
Parametric and nonparametric tests are used. t-test and ANOVA are used when assumptions are met, while Mann-Whitney U test and Kruskal-Wallis test are used for ordinal data, asymmetric distributions or smaller samples. Chi-square and Fisher’s exact test are used for categorical variables.
Correlation analysis
Correlations examine relationships between continuous or ordinal variables. Pearson correlation is used for approximately normally distributed continuous variables, while Spearman correlation is used for ordinal variables or when normality assumptions are not met. Spearman correlation is often suitable for Likert scales.
Regression analysis
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 dental and health research, regression can identify predictors of quality of life, dental anxiety, pain, functional status, satisfaction, burnout, risk behavior or oral health.
Reliability analysis
Cronbach’s alpha is used to assess internal consistency of scales. In multi-item questionnaires, reliability shows whether items measure the same construct. Item correlations, alpha if item deleted and scale structure may also be reported.
Factor analysis
Exploratory factor analysis is used to examine the structure of questionnaires or scales. It is useful for instruments measuring attitudes, satisfaction, quality of life, dental anxiety, stress, professional burden, perception of care or self-assessed competencies.
ROC analysis
ROC analysis can be useful when evaluating diagnostic or classification value of a test, scale, dental indicator, biomarker or functional parameter. AUC, sensitivity, specificity and possible cut-off values are presented.
SPSS analysis and interpretation
SPSS is the primary statistical software used in many research projects at this faculty. It enables efficient data processing, hypothesis testing, regression modeling, reliability analysis, factor analysis and graphical output.
However, statistical output must always be interpreted in the context of dental or health sciences. Statistical significance alone is not sufficient; practical relevance, effect sizes, confidence intervals and the direction of associations must also be considered.
Likert scales
Likert scales are widely used. Individual items are typically treated as ordinal variables, while total scores may be treated as continuous variables if reliability is sufficient. The number of items, response range, scoring method and meaning of higher scores should be clearly described.
Sample size
Sample size often depends on available patients or respondents but must be sufficient to ensure statistical validity. In dental research, it is particularly important to distinguish between number of patients and number of teeth, implants, surfaces or measurement units.
Typical topics
Typical topics include dental medicine, oral health, dental anxiety, patient satisfaction, oral health-related quality of life, periodontology, orthodontics, prosthodontics, implantology, nursing, physiotherapy, public health, palliative care, rehabilitation, pain, functional status, health behavior, patient education, burnout, communication in healthcare, patient safety and questionnaire validation.
SEO position (F5 Osijek)
This article serves as a core node for keywords related to dental statistics Osijek, health data analysis Osijek, SPSS health research Osijek, nursing statistics Osijek, physiotherapy statistics Osijek and public health Osijek.
Workflow
Support includes reviewing the topic and hypotheses, dataset preparation, method selection, SPSS analysis, table preparation and interpretation. I will prepare statistical results and suggest how to present them. The author remains responsible for the final text and interpretation.
FAQ
Can dental data be analyzed?
Yes. Oral health, dental anxiety, periodontal parameters, orthodontic metrics, implant outcomes, radiological findings and patient satisfaction can be analyzed.
Can healthcare surveys be analyzed?
Yes. Knowledge, attitudes, satisfaction, burnout, quality of care, communication, patient safety and other health indicators can be analyzed.
Can Likert scales be analyzed?
Yes. Individual items, total scores, subscales, reliability and factor structure can be analyzed.
Is SPSS sufficient?
For many final, graduate and professional papers, SPSS is sufficient. More complex hierarchical dental data or longitudinal models may require additional tools or advanced models.
Keywords
dental medicine Osijek statistics, health studies Osijek SPSS, FDMZ Osijek statistics, dental data analysis, nursing statistics Osijek, physiotherapy statistics Osijek, public health Osijek statistics, health survey analysis, Likert scale health
Metatext
Statistical data analysis for the Faculty of Dental Medicine and Health Osijek: SPSS, dental medicine, health studies, nursing, physiotherapy, public health, questionnaires and rehabilitation outcomes.
