To examine the prevalence of CL, this cross sectional study was carried out during 2009–2016 in the city of Kashan. The data of 2,676 CL cases referred to health centers of Kashan University of Medical Sciences were collected. The patients were introduced to the laboratory by physician after an early-detection examination. For the diagnosis of CL, the direct smear technique has been used. The samples were taken from patient’s lesions by clinical staff and then examined under a light microscope for signs of Leishman parasite. All data obtained were recorded in researcher-constructed checklists and the SPSS version 24 (Chicago, Illinois, USA) for Windows was used to perform a chi-square test for relationships between variables. The checklist used included patients’ demographic characteristics such as age, gender, occupation, place or number of acute lesion(s), date of occurrence of lesion(s), history of travel in the past to other endemic foci of the disease, location of residence, results of clinical examination and laboratory tests, the type of treatment regimen, and nationality of patients. Data were analyzed using SPSS version 24 for windows.
Spss version 24
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Sourced in United States, Japan, United Kingdom, Germany, Belgium, Austria, China, Spain, Ireland
SPSS version 24 is a statistical software package developed by IBM. It provides a comprehensive set of tools for data analysis, including techniques for descriptive statistics, regression analysis, and predictive modeling. The software is designed to help users interpret data, identify trends, and make informed decisions based on statistical insights.
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Lab products found in correlation
Sas version 9, by SAS Institute (59 mentions)
Prism 8, by GraphPad (28 mentions)
Prism 7, by GraphPad (27 mentions)
Epidata version 3, by IBM (26 mentions)
R version 3, by R Project for Statistical Computing (25 mentions)
Prism 5, by GraphPad (17 mentions)
Spss statistics for windows version 24, by IBM (17 mentions)
Stata version 14, by StataCorp (12 mentions)
Prism 6, by GraphPad (12 mentions)
Sas 9, by SAS Institute (12 mentions)
Epidata version 4, by IBM (10 mentions)
Spss statistics for window, by IBM (8 mentions)
Prism 9, by GraphPad (7 mentions)
Spss statistic, by IBM (7 mentions)
R version 4, by R Project for Statistical Computing (6 mentions)
Stata version 13, by StataCorp (6 mentions)
Prism version 7, by GraphPad (6 mentions)
Sas version 9, by IBM (5 mentions)
Prism version 6, by GraphPad (5 mentions)
Stata 13, by StataCorp (5 mentions)
4 983 protocols using spss version 24
1
Epidemiological Study of Cutaneous Leishmaniasis
2
Evaluating Treatment Response in OCD
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Statistical analyses were performed with SPSS version 24.0. Repeated measures ANOVA for Y-BOCS was conducted using pre-treatment, post-treatment, and follow-up scores. Effect sizes were calculated with Cohen’s d, defined as (Mpre – Mpost)/SDpre.
Treatment response was calculated based on the international consensus criteria [26 (link)] which requires a ≥ 35% reduction of the individual patient’s pre-treatment Y-BOCS score in order to be classified as a clinically relevant response. A patient is classified as remitted if the post-treatment Y-BOCS score is ≤12 points. For Y-BOCS scores there were no missing data at pre-treatment, whereas data were missing for four patients at post-treatment and 12 at follow-up. For PHQ-9 one case was missing at pre-treatment and 10 at post-treatment. For GAD-7 there were 2 patients with missing data at pre-treatment and 10 at post-treatment. Missing data were replaced using the expectation-maximization method of SPSS, version 24. The method was chosen to allow for repeated measures ANOVA. All data presented are an integrated part of the 4-day standard quality control procedure.
Treatment response was calculated based on the international consensus criteria [26 (link)] which requires a ≥ 35% reduction of the individual patient’s pre-treatment Y-BOCS score in order to be classified as a clinically relevant response. A patient is classified as remitted if the post-treatment Y-BOCS score is ≤12 points. For Y-BOCS scores there were no missing data at pre-treatment, whereas data were missing for four patients at post-treatment and 12 at follow-up. For PHQ-9 one case was missing at pre-treatment and 10 at post-treatment. For GAD-7 there were 2 patients with missing data at pre-treatment and 10 at post-treatment. Missing data were replaced using the expectation-maximization method of SPSS, version 24. The method was chosen to allow for repeated measures ANOVA. All data presented are an integrated part of the 4-day standard quality control procedure.
3
Efficacy of Psychological Intervention for OCD and Anxiety
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Statistical analyses were performed with SPSS version 24.0. Repeated measures ANOVA for Y-BOCS, GAD-7, and PHQ-9 were conducted with Greenhouse-Geisser corrections. Effect sizes were calculated with Cohen's d, defined as (Mpre-Mpost)/SDpre as recommended by Morris and DeShon (2002 (link)). There were no missing data for Y-BOCS or GAD-7 pre-treatment. A total of 63 patients were available for Y-BOCS evaluation post-treatment, 54 at 3-month and 59 at 12-month follow up. Four patients had a missing GAD-7 score post-treatment, and 19 at 3-month follow-up. One patient had a missing PHQ-9 score pre-treatment and 19 at 3-month follow-up. A total of 10 patients had missing values on CSQ-8 at post-treatment. Missing data were replaced using the expectation-maximization method of SPSS, version 24. The method was chosen to allow for repeated measures ANOVA. Effect estimates were also obtained by estimating both a random intercept model and a random slope model, taking into account potential clustering at treatment group level as well as at the individual patient level. Likelihood ratio test was applied to compare these two models, and the random intercept model was chosen since there was no gain in allowing for random slopes. ICC was calculated from this random intercept model.
4
Heavy Metals Analysis in Soil and Honey
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Data collection and descriptive statistics, including the calculations of averages, medians, relative standard deviations, Spearman’s correlation, and bioaccumulation factors, were conducted using Microsoft Excel 365 (Microsoft, New York, NY, USA) and Addinsoft version 15.5.03.3707 (Microsoft, New York, NY, USA). The data precision was assessed and presented as the standard deviation (SD). All data were reported as means ± standard deviations and underwent statistical analysis using SPSS Version 24 (SPSS Inc., Chicago, IL, USA). The statistical outcomes were presented as means (averaged over three replications) and standard deviations. The recorded data were subjected to a two-way analysis of variance (ANOVA) to explore the impact of various variables on the concentrations of heavy metals in both soil and honey samples. Further analysis of variance (ANOVA) and average separation were performed using the Duncan test at a significance level of p ≤ 0.005 with SPSS Version 24 (SPSS Inc., Chicago, IL, USA).
5
Temporal Dynamics of Reproductive Physiology
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We investigated the effect of time (day, week or month) on the scrotal circumference, testicular pixel intensity and hormonal concentrations by conducting an
analysis of ANOVA using the Generalized Linear Models (GLMs) procedure of SPSS version 24 software (IBM, Somers, NY, USA). Post-hoc pairwise comparisons were
made by the Bonferroni correction for differences between two time-points. The weekly data of scrotal circumference and testicular pixel intensity for 4 weeks
were averaged, converted to monthly data and subjected to the statistical analyses. We investigated the effect of treatment on the scrotal circumference,
testicular pixel intensity, testicular weight, epididymal weight, cross sectional area of the seminiferous tubule, luminal area of the seminiferous tubule and
sperm count per epididymis by conducting an analysis of ANOVA using the Generalized Linear Models (GLMs) procedure of SPSS version 24 software. Post-hoc
pairwise comparisons were made by the Bonferroni correction for differences between two treatments (GnRH antagonist treatment vs control) and between two
time-points. The data are expressed as mean ± standard error of the mean (SEM). Differences were considered significant at P < 0.05.
analysis of ANOVA using the Generalized Linear Models (GLMs) procedure of SPSS version 24 software (IBM, Somers, NY, USA). Post-hoc pairwise comparisons were
made by the Bonferroni correction for differences between two time-points. The weekly data of scrotal circumference and testicular pixel intensity for 4 weeks
were averaged, converted to monthly data and subjected to the statistical analyses. We investigated the effect of treatment on the scrotal circumference,
testicular pixel intensity, testicular weight, epididymal weight, cross sectional area of the seminiferous tubule, luminal area of the seminiferous tubule and
sperm count per epididymis by conducting an analysis of ANOVA using the Generalized Linear Models (GLMs) procedure of SPSS version 24 software. Post-hoc
pairwise comparisons were made by the Bonferroni correction for differences between two treatments (GnRH antagonist treatment vs control) and between two
time-points. The data are expressed as mean ± standard error of the mean (SEM). Differences were considered significant at P < 0.05.
6
Assessing PubCompare Awareness and Usage
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Returned questionnaires were coded for easy referencing. The responses from the study were entered into Microsoft Excel Software for easy sorting, and they were double checked for correctness by 2 of the authors. The reliability of this questionnaire was performed using Cronbach's Alpha method of SPSS version 24. Cronbach's alpha of at least 0.7 is considered reliable.
All data were analyzed using SPSS version 24.0 for descriptive statistical analysis. Frequencies and percentage were determined. Relationship between demographic profile and awareness of PC was explored using cross tabs and Fisher's exact test (F). Association between socio-demographic characteristics and correct definition of PC was also assessed using Fisher's exact test (F) and probability value. Differences in practice of PC activities by hospital and community PTs were also analyzed using cross tabs and Fisher's exact test (F). P – Value <0.05 were considered significant.
All data were analyzed using SPSS version 24.0 for descriptive statistical analysis. Frequencies and percentage were determined. Relationship between demographic profile and awareness of PC was explored using cross tabs and Fisher's exact test (F). Association between socio-demographic characteristics and correct definition of PC was also assessed using Fisher's exact test (F) and probability value. Differences in practice of PC activities by hospital and community PTs were also analyzed using cross tabs and Fisher's exact test (F). P – Value <0.05 were considered significant.
7
Statistical Analysis of Diabetes Risk Factors
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SPSS version 24 (IBM Corp., Armonk, NY, USA) was used to analyze the secondary data set. All variables used in this study were categorical. For the analysis of the categorical variables, which are: diabetes (no/yes), BMI > 33, BMI category, age category, hypertension, nationality, and smoking (no/yes), frequencies and percentages were used. Additionally, chi square test was used to assess the relationship between the outcome (diabetes) and other covariates. Finally, logistic regression was used to assess the relationship between diabetes mellitus and the other independent factors where odds ratios with confidence intervals were delineated. Univariate regression was specifically used to assess the relationship between diabetes and each other covariates at a time while multivariate logistic regression was applied to illustrate the relationship between diabetes and the other tested covariates and to adjust for all possible confounders that might affect the studied outcome. The 5% significant level was used for the p-value. All statistical analyses were performed using SPSS version 24. The codes used to perform the analysis in SPSS for the tested variables are shown in Appendix A .
8
Maternal BPD and Infant Disinhibited Behavior
9
Comparative Analysis of Sample Groups
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A Friedman test was used to compare quantitative variables, and the Cochran test was used for qualitative variables, and statistical analysis was carried out with SPSS version 24 with a significance level of 5%.
Two levels of analysis were conducted, one descriptive and one inferential. A descriptive level was used to describe the status of the samples in each group using frequency distribution tables and summary statistics, such as mean and standard deviation. At the inferential level, a Shapiro‐Wilk test was conducted to test the normality assumption for the data distribution. Analyses based on parametric tests are appropriate if the data meet the normality assumption. If the assumption of normality is not met, non‐parametric tests are used.
To compare multiple groups, an analysis of variance (ANOVA) was conducted following confirmation of normality. Inferences were drawn about the differences between the three groups based on these tests. A significance level of 5% was applied to all analyses, and SPSS version 24 was used for the analysis. On the basis of these statistical tests, comparisons between groups were made, and conclusions were reached.
Two levels of analysis were conducted, one descriptive and one inferential. A descriptive level was used to describe the status of the samples in each group using frequency distribution tables and summary statistics, such as mean and standard deviation. At the inferential level, a Shapiro‐Wilk test was conducted to test the normality assumption for the data distribution. Analyses based on parametric tests are appropriate if the data meet the normality assumption. If the assumption of normality is not met, non‐parametric tests are used.
To compare multiple groups, an analysis of variance (ANOVA) was conducted following confirmation of normality. Inferences were drawn about the differences between the three groups based on these tests. A significance level of 5% was applied to all analyses, and SPSS version 24 was used for the analysis. On the basis of these statistical tests, comparisons between groups were made, and conclusions were reached.
10
Analyzing Black Locust Provenance Traits
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Microsoft Excel 2016 was used to examine the variation in leaf phenotypic and physiological traits, including the mean value, standard error, amplitude, and coefficient of variation (CV). SPSS version 24 was used to perform analyses of variance (ANOVAs) in conjunction with Duncan’s multiple range tests for multiple comparisons. Principal component analysis was applied to the sixteen traits of the black locust provenances. A p-value for the ANOVA F tests ≤0.05 was considered significant. The formula, Vst (%) = δ2t/s/(δ2t/s+δ2s), was used to calculate the percentage of variance among and within the provenances, where Vst is the differentiation coefficient of the trait, δ2t/s is the variance component between provenances, and δ2s is the variance component within provenances [44 ]. A covariance correlation matrix was then used to analyze the correlations between clonal populations and geographical populations. The euclidean distance of each quantitative trait was calculated with the open-source statistical package, R; graphical visualization of the results was carried out using MEGA ver. 6.0 [45 (link)] after all the tested data had been processed in SPSS version 24. In addition, Mantel’s correlation tests were conducted on the euclidean and geographical distances of all the traits of the black locust trees.
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