{"id":13038,"date":"2025-02-04T11:09:02","date_gmt":"2025-02-04T11:09:02","guid":{"rendered":"https:\/\/selartuniversity.com\/?page_id=13038"},"modified":"2025-03-26T12:48:13","modified_gmt":"2025-03-26T12:48:13","slug":"data-analysis-with-spss-for-research","status":"publish","type":"page","link":"https:\/\/selartunispain.com\/en\/data-analysis-with-spss-for-research\/","title":{"rendered":"Data Analysis with SPSS For Research"},"content":{"rendered":"
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  • \ud83c\udfafTarget Audience:<\/b> Intermediate<\/li>\n
  • \ud83d\udcbbDelivery Mode:<\/b> Online \/ On-Campus<\/li>\n
  • \ud83d\udde3\ufe0fLanguages:<\/b> Spanish<\/li>\n
  • \u231bTotal Duration:<\/b> 30 hours<\/li>\n
  • \ud83d\uddd3\ufe0fSchedule: <\/strong>Monday – Friday : 3h\/day<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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    Course Content<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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    Data Analysis with SPSS for Research<\/strong><\/h2>

    1. T-Tests<\/strong><\/h3>

    T-tests are statistical tools used to compare means between two groups or conditions. This module will cover:<\/p>

    • One-sample T-test<\/strong>: Comparing the mean of a sample to a reference value.<\/li>
    • Independent samples T-test<\/strong>: Comparing the means of two different groups.<\/li>
    • Paired samples T-test<\/strong>: Comparing means in situations where the same subjects are measured at two different times.<\/li>
    • Assumptions and conditions<\/strong>: Normality, homogeneity of variances, and sample size.<\/li>
    • Interpreting SPSS output<\/strong>: Analysis of output tables and statistical significance.<\/li><\/ul>

      2. One-Way ANOVA: Between-Subjects and Repeated Measures<\/strong><\/h3>

      ANOVA (Analysis of Variance) is a technique used to compare more than two groups or conditions. This section will cover:<\/p>

      • One-way between-subjects ANOVA<\/strong>:
        • Comparing independent groups with a single categorical variable.<\/li>
        • Assumptions (normality, homogeneity of variance, independence).<\/li>
        • Interpretation of results and post hoc tests (Tukey, Bonferroni).<\/li><\/ul><\/li>
        • Repeated measures ANOVA<\/strong>:
          • Comparing data obtained from the same subjects under different conditions or time points.<\/li>
          • Sphericity assumption and Mauchly\u2019s test.<\/li>
          • Corrections (Greenhouse-Geisser, Huynh-Feldt).<\/li>
          • Post hoc analysis and trend plots.<\/li><\/ul><\/li><\/ul>

            3. Factorial ANOVA: Between-Subjects, Within-Subjects, and Mixed Designs<\/strong><\/h3>

            Factorial ANOVA allows the simultaneous analysis of multiple factors. This section will cover:<\/p>

            • Between-subjects factorial ANOVA<\/strong>:
              • Comparing multiple factors across different groups.<\/li>
              • Analysis of main effects and interactions.<\/li><\/ul><\/li>
              • Within-subjects factorial ANOVA<\/strong>:
                • Comparing multiple factors within the same group under different conditions.<\/li>
                • Interpretation of main effects and interactions.<\/li><\/ul><\/li>
                • Mixed-design ANOVA<\/strong>:
                  • Combining between-subjects and within-subjects factors.<\/li>
                  • Applications in longitudinal and experimental studies.<\/li>
                  • Interpretation of SPSS output and adjustments for assumption violations.<\/li><\/ul><\/li><\/ul>

                    4. Multivariate Analysis: MANOVA<\/strong><\/h3>

                    MANOVA (Multivariate Analysis of Variance) is an extension of ANOVA that allows analyzing multiple dependent variables simultaneously. This section will cover:<\/p>

                    • Basic concepts and differences from ANOVA<\/strong>.<\/li>
                    • MANOVA assumptions<\/strong>: Multivariate normality, homogeneity of variance-covariance matrices.<\/li>
                    • Interpretation of key statistics<\/strong>: Wilks\u2019 Lambda, Pillai\u2019s Trace, Hotelling\u2019s Trace, and Roy\u2019s Largest Root.<\/li>
                    • Applications in research<\/strong>: Use in studies with multiple outcome measures.<\/li>
                    • Practical example in SPSS<\/strong>: Data setup, execution, and interpretation of output tables.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                      This course is designed for students preparing their Final Degree Project (TFG) or Master\u2019s Thesis (TFM)<\/strong> who need data analysis tools to support their research. It is not a highly specialized statistics course but rather a practical introduction to using SPSS<\/strong> for analyses such as ANOVA, MANOVA, and T-tests<\/strong>, helping students interpret results and apply them in academic studies.<\/p>

                      Through real-world examples and hands-on exercises, students will learn to obtain key statistics for their projects, enabling them to present solid data in their research and increase their chances of success in their final project defense.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                      Adventages<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                      • Practical Use of SPSS<\/strong><\/li>
                      • Understanding Basic and Advanced Statistical Tests<\/strong><\/li>
                      • Interpretation of Statistical Results<\/strong><\/li>
                      • Application in Their Final Degree\/Master\u2019s Project<\/strong><\/li>
                      • Better Argumentation in Project Defense<\/strong><\/li>
                      • Data-Driven Decision Making<\/strong><\/li>
                      • Increased Confidence in Using Statistical Tools<\/strong><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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