![]() ![]() ![]() Accurate predictions of the histopathologic characteristics of tumors along with radiologic findings are useful for adequate surgical planning, especially for avoiding unnecessary surgeries and consequent complications. Malignant SGTs (M-SGTs) are mainly represented by mucoepidermoid carcinomas and adenoid-cystic carcinoma. Benign SGTs (B-SGTs) are predominantly represented by pleomorphic adenoma (PA) and Warthin’s tumor (WT). The parotid gland is the most common site, followed by the submandibular gland. Salivary gland tumors (SGTs) are rare, representing 2-6.5% of all head and neck neoplasms. Clinical relevanceĬystic components are potentially valuable in the differential diagnosis of B-SGTs and M-SGTs on US. Cystic component is of interest in the US-related differential diagnosis of B-SGT and M-SGT. US features of the B-SGTs and M-SGTs were significantly different. Cystic component features needed to be combined with lesion indicators (border and shape) to improve diagnostic sensitivity. Younger age (P = 0.001), eccentric distribution (P = 0.034) and ill-defined margin (P < 0.001) were risk factors for diagnosing M-SGTs. For SGTs with cystic components, the proportions of M-SGTs to ill-defined margins (P = 0.002), eccentric distribution (P = 0.019), and none of the internal characteristics (P = 0.019) were significantly higher than those of B-SGTs. Differences in sex and age of patients, number, distribution, and internal characteristics of cystic components were statistically significant. Similarities were observed between the US performance of benign SGTs (B-SGTs) and malignant SGTs (M-SGTs) with cystic components. Lesion size, shape, margin, and US findings of the cystic components, including number, distribution, margin, occupying rate, and internal characteristics, were evaluated. Preoperative US revealed the presence of cystic components in lesions. Materials and methodsĪ total of 207 patients (218 lesions) with pathologically confirmed primary SGTs were analyzed. Alternately, see our generic, "quick start" guide: Entering Data in SPSS Statistics.The present study aimed to characterize the ultrasonography (US) features of cystic components in salivary gland tumors (SGTs). In our enhanced chi-square test for independence guide, we show you how to correctly enter data in SPSS Statistics to run a chi-square test for independence. In SPSS Statistics, we created two variables so that we could enter our data: Gender and Preferred_Learning_Medium. Therefore, we have two nominal variables: Gender (male/female) and Preferred Learning Medium (online/books). ![]() An educator would like to know whether gender (male/female) is associated with the preferred type of learning medium (online vs. However, different people learn in different ways. With current technology, it is possible to present how-to guides for statistical programs online instead of in a book. SPSS Statistics ExampleĮducators are always looking for novel ways in which to teach statistics to undergraduates as part of a non-statistics degree course (e.g., psychology). First, we introduce the example that is used in this guide. In the section, Procedure, we illustrate the SPSS Statistics procedure to perform a chi-square test for independence. Example independent variables that meet this criterion include gender (2 groups: Males and Females), ethnicity (e.g., 3 groups: Caucasian, African American and Hispanic), physical activity level (e.g., 4 groups: sedentary, low, moderate and high), profession (e.g., 5 groups: surgeon, doctor, nurse, dentist, therapist), and so forth. Assumption #2: Your two variable should consist of two or more categorical, independent groups.You can learn more about ordinal and nominal variables in our article: Types of Variable. Assumption #1: Your two variables should be measured at an ordinal or nominal level (i.e., categorical data). ![]() If it does not, you cannot use a chi-square test for independence. You need to do this because it is only appropriate to use a chi-square test for independence if your data passes these two assumptions. When you choose to analyse your data using a chi-square test for independence, you need to make sure that the data you want to analyse "passes" two assumptions. The chi-square test for independence, also called Pearson's chi-square test or the chi-square test of association, is used to discover if there is a relationship between two categorical variables. Chi-Square Test for Association using SPSS Statistics Introduction ![]()
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