The mean, standard deviation, and range for the tested variables (taste threshold, taste perception, BMI, DMFS) were analyzed using IMB® SPSS® (PASW version 23.0 IBM®, Chicago, IL, USA). The difference between countries in terms of the studied variables was tested using ANOVA. A p-value of <0.05 was recorded as statistically significant. Spearman’s rank correlation was used for correlation analysis between variables. Differences between BMI groups (underweight, normal, overweight, and obese) were tested using ANOVA.
Imb spss
Manufactured by IBM
Sourced in United States
IBM SPSS is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, statistical modeling, and visualization. The core function of IBM SPSS is to enable users to analyze and interpret data, identify patterns, and make informed decisions.
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Lab products found in correlation
7 protocols using imb spss
1
Taste Thresholds and Oral Health
2
Visual Scoring and Patient-Reported Outcomes in Breast Reconstruction
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Total visual scores (TVS) were made by adding all six categories of the Ten-Point Scale. Distribution of data was quantified using Kolmogorov–Smirnov tests. In normality test, not all variables were shown to follow a normal distribution. Average of BREAST-Q and TVS with ranges (min–max) were found for each type of surgery (immediate and delayed, unilateral and bilateral, autologous and alloplastic) and compared by Mann–Whitney
Utests. Interobserver agreements for panels were evaluated by calculating the relatedness based on the interclass correlation coefficients (ICCs) with 95% confidence interval (CI). About ICC, < 0.40 was considered as a “poor” agreement, 0.40 to 0.59 as “fair,” 0.60 to 0.74 as “good,” and 0.75 to 1.00 as “excellent.”
12 (link)
Correlation between TVS and each of the three BREAST-Q scales was evaluated using Spearman's correlation. Statistical significance was defined as
p < 0.05. We performed all statistical analysis using IMB SPSS, version 26 (IBM Corp., Armonk, NY).
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Total visual scores (TVS) were made by adding all six categories of the Ten-Point Scale. Distribution of data was quantified using Kolmogorov–Smirnov tests. In normality test, not all variables were shown to follow a normal distribution. Average of BREAST-Q and TVS with ranges (min–max) were found for each type of surgery (immediate and delayed, unilateral and bilateral, autologous and alloplastic) and compared by Mann–Whitney
Utests. Interobserver agreements for panels were evaluated by calculating the relatedness based on the interclass correlation coefficients (ICCs) with 95% confidence interval (CI). About ICC, < 0.40 was considered as a “poor” agreement, 0.40 to 0.59 as “fair,” 0.60 to 0.74 as “good,” and 0.75 to 1.00 as “excellent.”
12 (link)
Correlation between TVS and each of the three BREAST-Q scales was evaluated using Spearman's correlation. Statistical significance was defined as
p < 0.05. We performed all statistical analysis using IMB SPSS, version 26 (IBM Corp., Armonk, NY).
3
Sweet Taste Perception and Dietary Habits
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The mean, standard deviation, and range for the variables were analysed using IMB® SPSS® (PASW version 23.0 IBM® Chicago, Ill, USA). The independent t-test was used to determine the differences between boys and girls. Differences between the sweet taste perception groups (low, medium, and high) were tested by one-way ANOVA. Regarding beverage and snack questionnaire, the BS and SS scores were considered as a dependent variable and used for multiple linear regression analysis. The relationship between variables was tested using Spearman's rank correlation. A p value of <0.05 was considered statistically significant.
4
Sperm Characteristics Statistical Analysis
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Statistical analysis was performed using IMB SPSS version 22.0 for Windows by IBM Corp. (Chicago, IL, USA). Data concerning normal, abnormal, and immature spermatozoa were analysed using the Chi-squared test. Differences were considered significant if p < 0.05.
5
Oral Microbiome Changes During Pregnancy
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The mean, standard deviation and range for variables were calculated using the IMB® SPSS® (PASW version 21.0 IBM® Chicago, IL, USA). The independent t test was used to determine the differences between non-pregnant and pregnant groups. The pregnant group was divided into two subgroups depending on how far their pregnancy had progressed-early pregnant and late pregnant. The difference between the three groups (non-pregnant, early pregnant and late pregnant) was tested by one-way ANOVA. Difference in proportion of microbiological data and groups were tested by Chi square test. For plaque pH, the area under the curve below pH 5.7 (AUC5.7) and pH 6.2 (AUC6.2) was calculated using a special computer program. The relationship between variables was tested using Pearson’s correlation. A p value of <0.05 was considered statistically significant. Multinomial regression analysis was run to evaluate the impact of the different variables recorded on the two different groups (non-pregnant and pregnant) and the three different groups (non-pregnant, early pregnant, late pregnant).
6
Analyzing Sourdough Bread Characteristics
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In order to assess significant differences among sourdough bread samples, the effects of the different sourdough starters on bread physicochemical characteristics, aroma volatile compounds, appearance of mold and rope spoilage, and sensorial analysis were analyzed for their mean differences with the Analysis of Variance (ANOVA) procedure followed by Duncan’s post hoc multiple range test to extract the specific differences between the various treatments. Analysis was performed by using IMB SPSS (version 20, IBM Corp., Armonk, NY, USA) at an alpha level of 5%.
7
Statistical Analysis of Dental Interventions
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The statistical analysis was carried out using SPSS (IMB SPSS® Version 24.0 IBM Corp., Armonk, USA). A P value of less than 5% (P< 0.05%) was considered statistically significant. Means between groups were compared using ANOVA with Tukey´s post hoc test in Paper II-IV. In Paper II, a general linear model in combination with Tukey´s test was performed when corrections were made for background/confounding variables: gender, caries, enamel defects, headache and trauma.
In Paper II-IV, chi-square analysis and Fisher´s exact test were used to determine differences between groups regarding categorical data, e.g. to calculate the success rate of crossbite correction.
In Paper II-IV, chi-square analysis and Fisher´s exact test were used to determine differences between groups regarding categorical data, e.g. to calculate the success rate of crossbite correction.
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