Objectives:

– Understand the fundamental principles of SPSS and its use for data analysis.

– Familiarize with the SPSS interface and basic features.

1.1. Introduction to SPSS

– History and evolution of SPSS.

– Uses and application areas of SPSS in research and data analysis.

1.2. Installation and Configuration

– Step-by-step guide for installing SPSS on different operating systems (Windows, macOS, Linux).

– Configuring SPSS for optimal use.

1.3. SPSS Interface

– Overview of the SPSS user interface (windows, toolbars, menus).

– Customizing the user interface to meet specific needs.

1.4. Creating a New Project

– Creating a new data sheet.

– Importing data from different file formats (CSV, Excel, etc.).

– Exporting data to other file formats.

1.5. Data Structure in SPSS

– Types of variables in SPSS: numeric, string, date, etc.

– Defining measurement levels of variables (nominal, ordinal, interval, ratio).

– Handling missing values and erroneous data.

1.6. Data Manipulation

– Modifying and transforming variables.

– Filtering data: selecting specific cases.

– Sorting data for analysis.

1.7. Introduction to Syntax Commands

– Using syntax commands to automate recurring tasks.

– Advantages of using SPSS syntax over the graphical interface.

1.8. Practice: Importing and Exploring Data

– Practical exercise in importing data from a CSV file.

– Initial data exploration: generating simple descriptive statistics.

1.9. Examples and Use Case

– Case studies illustrating the use of SPSS in various fields (clinical research, marketing, social sciences).

– Discussion on challenges and solutions when using SPSS for real-world data analysis.

Objectives:

– Master advanced data management techniques in SPSS.

– Understand the various steps of data preparation for analysis.

2.1. Variables in SPSS

– Types of variables: numeric, string, date, etc.

– Defining measurement levels of variables: nominal, ordinal, interval, ratio.

– Handling missing values and erroneous data.

2.2. Recoding and Transforming Variables

– Recoding variables: creating new variables based on specific criteria.

– Transforming variables: normalization, standardization, creating derived variables.

2.3. Case Selection and Filtering

– Selecting cases based on specific criteria: simple and complex filters.

– Using conditions to filter data: practical examples.

2.4. Data Sorting

– Sorting data: ascending and descending order.

– Using sorting for exploratory data analysis.

2.5. Managing Missing Data

– Identifying missing data in SPSS.

– Techniques for managing missing data: deletion, imputation, advanced methods.

2.6. Data Import and Export

– Importing data from various file formats: CSV, Excel, databases.

– Exporting data to other file formats: Excel, PDF, formats compatible with other software.

2.7. Advanced Use of Syntax Commands

– Creating scripts to automate data management tasks.

– Advantages of using SPSS syntax for data management.

2.8. Practice: Advanced Data Manipulation

– Practical exercises on recoding and transforming variables.

– Case studies on filtering and sorting data for specific analyses.

2.9. Examples and Use Cases

– Case studies illustrating the importance of effective data management in statistical analysis.

– Discussion on common errors and best practices in data management in SPSS.

2.10. Data Validation and Quality Control

– Techniques for verifying data integrity after management.

– Quality control strategies to minimize errors in subsequent analyses.

Objectives:

– Understand the fundamental concepts of descriptive statistics.

– Master the use of SPSS to generate and interpret descriptive statistics.

3.1. Introduction to Descriptive Statistics

– Definition of descriptive statistics and their role in data analysis.

– Types of descriptive measures

3.2. Calculating Descriptive Statistics

– Using SPSS to calculate descriptive statistics for one or more variables.

– Interpreting results: reading and understanding the descriptive statistics tables generated by SPSS.

3.3. Visualizing Descriptive Statistics

– Creating descriptive graphs: histograms, box plots, whisker plots.

– Customizing graphs for effective presentation of descriptive data.

3.4. Comparative Analysis

– Comparing descriptive statistics between different data groups.

– Using statistical tests to check for significant differences.

3.5. Frequency Analysis

– Calculating frequency distributions for categorical variables.

– Interpreting the results of frequency analysis to identify trends and patterns.

3.6. Practical Case Studies

– Applying descriptive statistics to real-world case studies in various fields.

– Discussing the conclusions drawn from descriptive analyses and their impact on decision-making.

3.7. Advanced Use of SPSS Functions

– Using SPSS syntax to automate the generation of descriptive statistics.

– Creating macros to simplify recurring descriptive analysis tasks.

3.8. Interpreting Results

– Techniques for effectively interpreting and communicating the results of descriptive statistics.

– Identifying outliers and unusual points in the data.

Objectives:

– Understand parametric tests and their application in statistical analysis.

– Master the use of SPSS to perform and interpret parametric tests.

4.1. Introduction to Parametric Tests

– Definition of parametric tests and their underlying assumptions.

– Distinction between parametric and non-parametric tests.

4.2. Student’s t-Test

– t-Test for independent samples: comparing means between two groups.

– t-Test for paired samples: comparing means before and after treatment in the same group.

4.3. Analysis of Variance (ANOVA)

– One-way ANOVA

– Two-way ANOVA

4.4. Assumptions and Checks

– Checking the assumptions of parametric tests: normality, homogeneity of variances.

– Techniques to address violations of assumptions.

4.5. Interpreting Results

– Reading and interpreting the results of parametric tests generated by SPSS.

– Using SPSS to calculate p-values, confidence intervals, and test statistics.

4.6. Post-Hoc Comparisons

– Post-hoc comparison techniques: Tukey, Bonferroni, LSD, etc.

– Applying post-hoc comparisons to identify significant differences between groups.

4.7. Analysis of Covariance (ANCOVA)

– Introduction to ANCOVA: controlling for continuous confounding variables.

– Interpreting ANCOVA results in SPSS.

4.8. Practical Case Studies

– Applying parametric tests to real-world case studies in various fields.

– Discussing the conclusions drawn from parametric tests and their impact on decision-making.

4.9. Advanced Use of SPSS

– Using SPSS syntax to automate the execution of parametric tests.

– Creating scripts to generate automated reports from test results.

Objectives:

– Understand non-parametric tests and their applications in statistical analysis.

– Master the use of SPSS to perform and interpret non-parametric tests.

5.1. Introduction to Non-Parametric Tests

– Definition of non-parametric tests and when to use them compared to parametric tests.

– Key non-parametric tests available in SPSS.

5.2. Wilcoxon Test

– Wilcoxon test for paired samples.

– Interpreting Wilcoxon test results in SPSS.

5.3. Mann-Whitney Test

– Mann-Whitney test.

– Using SPSS to perform the Mann-Whitney test and interpret the results.

5.4. Kruskal-Wallis Test

– Kruskal-Wallis test.

– Interpreting Kruskal-Wallis test results in SPSS.

5.5. Spearman Test

– Spearman correlation test.

– Using SPSS to calculate the Spearman correlation coefficient and interpret the results.

5.6. Contingency Table Analysis

– Contingency table analysis.

– Applying the chi-square test in SPSS and interpreting the results.

5.7. Multiple Comparisons

– Multiple comparisons in non-parametric tests.

– Using SPSS to perform non-parametric tests with post-hoc adjustments.

5.8. Practical Case Studies

– Applying non-parametric tests to real-world case studies in various fields.

– Discussing the conclusions drawn from non-parametric tests and their impact on decision-making.

5.9. Advanced Use of SPSS

– Using SPSS syntax to automate the execution of non-parametric tests.

– Creating scripts to generate automated reports from non-parametric test results.

Objectives:

– Understand the fundamental principles of linear regression and its applications.

– Master the use of SPSS to perform and interpret linear regression analysis.

6.1. Introduction to Linear Regression

– Definition of linear regression and its underlying assumptions.

– Types of regression: simple  and multiple.

6.2. Simple Regression Analysis

– Simple regression model.

– Interpretation of regression coefficients.

6.3 Multiple Regression Analysis

– Multiple regression model.

– Using SPSS to estimate multiple regression coefficients and assess the significance of predictor variables.

6.4. Assumptions of Linear Regression

– Checking the assumptions of linear regression.

– Techniques to address violations of assumptions.

6.5. Variable Selection

– Variable selection techniques in regression.

– Criteria for choosing the best regression model in SPSS.

6.6. Model Diagnostics

– Diagnosing the regression model: residuals, influence of observations, multicollinearity.

– Using SPSS to perform diagnostic tests and interpret the results.

6.7. Using SPSS Syntax

– Using SPSS syntax to automate the execution of linear regression models.

– Creating scripts to generate automated reports from regression results.

6.8. Advanced Applications

– Linear regression with categorical variables.

– Weighted linear regression and other advanced variants in SPSS.

6.9. Practical Case Studies

– Applying linear regression to real-world case studies in various fields.

– Discussing the conclusions drawn from linear regression models and their impact on decision-making.

Objectives:

– Understand the fundamental principles of logistic regression and its applications.

– Master the use of SPSS to perform and interpret logistic regression analysis.

7.1. Introduction to Logistic Regression

– Definition of logistic regression and its applications in modeling binary and ordinal categorical variables.

– Comparison with linear regression.

7.2. Binary Logistic Regression Model

– Binary logistic regression model.

– Interpretation of logistic regression coefficients and odds ratios.

7.3. Multinomial Logistic Regression Model

– Multinomial logistic regression model.

– Multinomial regression coefficients and assess the significance of predictor variables.

7.4. Assumptions of Logistic Regression

– Checking the assumptions of logistic regression

– Techniques to address violations of assumptions.

7.5. Variable Selection

– Variable selection techniques in logistic regression.

– Criteria for choosing the best logistic regression model in SPSS.

7.6. Model Diagnostics

– Diagnosing the logistic regression model.

– Using SPSS to perform diagnostic tests and interpret the results.

7.7. Using SPSS Syntax

– Using SPSS syntax to automate the execution of logistic regression models.

– Creating scripts to generate automated reports from logistic regression results.

7.8. Advanced Applications

– Logistic regression with categorical variables.

– Logistic regression with interactions and quadratic terms in SPSS.

7.9. Practical Case Studies

– Applying logistic regression to real-world case studies in various fields.

– Discussing the conclusions drawn from logistic regression models and their impact on decision-making.

Objectives:

– Understand the fundamental principles of Analysis of Variance (ANOVA) and its applications.

– Master the use of SPSS to perform and interpret different types of ANOVA.

8.1. Introduction to Analysis of Variance (ANOVA)

– Definition of ANOVA and its applications in comparing the means of several groups.

– Differences between one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

8.2. One-Way ANOVA

– One-way ANOVA: comparison of means among three or more groups.

– Interpretation of one-way ANOVA results in SPSS.

8.3. Two-Way ANOVA

– Two-way ANOVA: study of the effect of two independent variables on a dependent variable.

– Using SPSS to estimate main effects and the interaction between factors in two-way ANOVA.

8.4. Repeated Measures ANOVA

– Repeated measures ANOVA.

– Interpretation of repeated measures ANOVA results in SPSS.

 8.5. Assumptions of ANOVA

– Checking the assumptions of ANOVA.

– Techniques to check and remedy violations of assumptions in SPSS.

8.6. Post-Hoc Comparisons

– Using post-hoc comparisons after ANOVA.

– Applying post-hoc tests in SPSS to identify significant differences between groups.

8.7. Mixed Repeated Measures ANOVA

– Mixed repeated measures ANOVA.

– Using SPSS to analyze and interpret mixed repeated measures ANOVA results.

8.8 Using SPSS Syntax

– Using SPSS syntax to automate the execution of ANOVA.

– Creating scripts to generate automated reports from ANOVA results.

8.9. Advanced Applications

– ANOVA with covariates: controlling for continuous variables in the ANOVA analysis.

– Conducting robust ANOVA in situations where assumptions are not fully met.

8.10. Practical Case Studies

– Applying ANOVA to real-world case studies in various fields (psychology, biology, marketing).

– Discussing the conclusions drawn from ANOVA and their impact on decision-making.

Objectives:

– Understand the fundamental principles of correlation analysis and its applications.

– Master the use of SPSS to perform and interpret different types of correlation analyses.

9.1. Introduction to Correlation Analysis

– Definition of correlation and its uses in exploring relationships between variables.

– Differences between positive, negative, and zero correlation.

9.2. Pearson Correlation Coefficient

– Pearson correlation coefficient.

– Interpretation of r values and assessment of the strength and direction of the correlation.

9.3. Spearman Correlation Coefficient

– Spearman correlation coefficient.

– Using SPSS to calculate the Spearman correlation coefficient and interpret the results.

9.4. Kendall Correlation Coefficient

– Kendall correlation coefficient.

– Interpretation of Kendall correlation coefficient results in SPSS.

9.5. Correlation Matrices

– Creating correlation matrices to evaluate relationships between multiple pairs of variables.

– Using SPSS to generate and interpret correlation matrices.

9.6. Assumptions of Correlation Analysis

– Checking the assumptions of correlation analysis.

– Techniques to remedy violations of assumptions in SPSS.

9.7. Hypothesis Testing

– Hypothesis testing on correlation.

– Using SPSS to perform hypothesis tests on correlation coefficients.

9.8. Using SPSS Syntax

– Using SPSS syntax to automate the execution of correlation analyses.

– Creating scripts to generate automated reports from correlation analysis results.

9.9. Advanced Applications

– Partial correlation.

– Using SPSS to perform and interpret partial correlation analyses.

9.10. Practical Case Studies

– Applying correlation analyses to real-world case studies in various fields.

– Correlation analyses and their impact on decision-making.

Objectives:

– Understand the fundamental principles of factor analysis and its applications.

– Master the use of SPSS to perform and interpret different types of factor analyses.

10.1. Introduction to Factor Analysis

– Definition of factor analysis and its uses in data dimensionality reduction.

– Types of factor analysis: exploratory and confirmatory.

10.2. Exploratory Factor Analysis (EFA)

– Process of exploratory factor analysis: factor extraction, factor rotation.

– Interpretation of EFA results in SPSS to identify relationships between variables.

10.3. Factor Extraction Methods

– Factor extraction techniques: principal component analysis (PCA), common factor analysis.

– Using SPSS to choose the appropriate factor extraction method and interpret the results.

10.4. Factor Rotation

– Factor rotation: rotation techniques to simplify factor interpretation.

– Application of factor rotation in SPSS and interpretation of the results.

10.5. Selecting the Number of Factors

– Methods for determining the optimal number of factors;

– Using SPSS to apply these methods and decide the number of factors to retain.

10.6. Interpretation of Factor Loadings

– Interpretation of factor loadings.

– Using SPSS to examine factor loading matrices and interpret the results.

10.7. Confirmatory Factor Analysis (CFA)

– Introduction to confirmatory factor analysis: specifying a priori factor models.

– Using SPSS to fit confirmatory factor analysis models and assess model fit.

10.8. Assumptions of Factor Analysis

– Checking the assumptions of factor analysis.

– Techniques to remedy assumption violations in SPSS.

10.9. Using SPSS Syntax

– Using SPSS syntax to automate the execution of factor analyses.

– Creating scripts to generate automated reports from factor analysis results.

10.10 Advanced Applications

– Factor analysis with categorical variables.

– Using factor analyses for market segmentation and dimensionality reduction in complex data sets.

10.11. Practical Case Studies

– Applying factor analysis to real-world case studies in various fields.

– Discussing the conclusions drawn from factor analyses and their impact on decision-making.

Objectives:

– Understand the fundamental principles of cluster analysis and its applications.

– Master the use of SPSS to perform and interpret different types of cluster analyses.

11.1. Introduction to Cluster Analysis

– Definition of cluster analysis and its uses in segmenting data into homogeneous groups.

– Difference between hierarchical cluster analysis and non-hierarchical cluster analysis.

11.2. Cluster Analysis Methods

– Cluster analysis techniques.

– Using SPSS to apply cluster analysis methods and interpret the results.

11.3. Similarity Criteria

– Similarity measures in cluster analysis.

– Choosing the appropriate similarity measure in SPSS to optimize data classification.

11.4. Dendrograms

– Interpreting dendrograms.

– Using SPSS to generate and interpret dendrograms in cluster analysis.

11.5. Non-Hierarchical Classification Methods

– Non-hierarchical classification techniques: k-means, k-medoids.

– Application of non-hierarchical classification methods in SPSS

11.6. Selecting the Number of Clusters

– Methods for determining the optimal number of clusters.

– Using SPSS to apply these methods and choose the appropriate number of clusters.

11.7. Evaluating Clustering Quality

– Evaluating clustering quality: interpreting validity indices.

– Using SPSS to assess the consistency and separation of the obtained clusters.

11.8. Using SPSS Syntax

– Using SPSS syntax to automate the execution of cluster analyses.

– Creating scripts to generate automated reports from cluster analysis results.

11.9. Advanced Applications

– Cluster analysis with mixed variables.

– Using advanced techniques in SPSS to handle complex and heterogeneous data sets.

11.10. Practical Case Studies

– Applying cluster analysis to real-world case studies in various.

– Discussion on the conclusions drawn from cluster analyses and their impact on decision-making.

Objectives:

– Understand the fundamental principles of mixed models and their applications.

– Master the use of SPSS to perform and interpret different types of mixed models.

12.1. Introduction to Mixed Models

– Definition of mixed models.

– Applications of mixed models in modeling longitudinal data and random effects.

12.2. Generalized Linear Mixed Models (GLMM)

– Generalized linear mixed models.

– Using SPSS to specify and fit GLMMs and interpret the results.

12.3. Linear Mixed Models (LMM)

– Linear mixed models: modeling random effects in longitudinal data and experimental studies.

– Application of LMMs in SPSS to analyze relationships between continuous and categorical variables.

12.4. Nonlinear Mixed Models

– Nonlinear mixed models: adapting mixed models to nonlinear and complex data.

– Using SPSS to fit nonlinear mixed models and interpret the results.

12.5. Covariance Structure

– Specification of covariance structure in mixed models.

– Using SPSS to choose and specify the best covariance structure for mixed models.

12.6. Assumptions of Mixed Models

– Verification of mixed model assumptions.

– Techniques to address violations of assumptions in SPSS.

12.7. Variable Selection

– Variable selection techniques in mixed models.

– Using SPSS to perform variable selection in mixed models and improve model accuracy.

12.8. Using SPSS Syntax

– Using SPSS syntax to automate the execution of mixed model analyses.

– Creating scripts to generate automated reports from mixed model analysis results.

12.9. Advanced Applications

– Mixed models with growth effects.

– Using advanced techniques in SPSS to handle mixed models with complex data structures.

12.10. Practical Case Studies

– Application of mixed models to real-world case studies in various fields.

– Discussion on the conclusions drawn from mixed models and their impact on decision-making.

Objectives:

– Understand the fundamental principles of time series analysis and its applications.

– Master the use of SPSS to perform and interpret different types of time series analyses.

13.1. Introduction to Time Series Analysis

– Definition of time series.

– Applications of time series analysis in forecasting, trend detection, and modeling fluctuations.

13.2. Characteristics of Time Series

– Characteristics of time series: trend, seasonality, cycles, and random components.

– Identification of different time series components in SPSS.

13.3. Forecasting Methods

– Forecasting techniques in time series analysis.

– Using SPSS to apply these techniques and generate accurate forecasts.

13.4. ARIMA Models

– ARIMA models: autoregressive integrated moving average model for stationary time series.

– Steps for specifying, estimating, and diagnosing ARIMA models in SPSS.

13.5. Trend Detection

– Methods to detect trends in time series: centered moving average, linear regression.

– Using SPSS to identify and model trends in time series.

13.6 Seasonality Models

– Seasonality models: fitting seasonal components using decomposition and seasonal modeling methods.

– Application of seasonal techniques in SPSS to capture and interpret seasonal effects.

13.7. Spectral Analysis

– Spectral analysis of time series.

– Using SPSS to perform spectral analysis and interpret the results.

13.8. Time Series Regression Models

– Time series regression models

– Using SPSS to specify and estimate time series regression models with covariates.

13.9. Model Validation and Evaluation

– Methods to validate and evaluate time series models: fit criteria, cross-validation.

– Using SPSS to assess forecast accuracy and the robustness of time series models.

13.10. Using SPSS Syntax

– Using SPSS syntax to automate the execution of time series analyses.

– Creating scripts to generate forecasts and automated reports from time series analysis results.

13.11. Advanced Applications

– Time series analysis with missing data

– Using advanced techniques in SPSS to model complex and heterogeneous time series.

13.12. Practical Case Studies

– Application of time series analysis to real-world case studies in various fields

– Discussion on the conclusions drawn from time series analyses and their impact on decision-making.

14.1. Introduction to Content Analysis

– Definition of content analysis: qualitative methodology for analyzing textual data.

– Applications of content analysis in qualitative research, information management, and social media analysis.

14.2. Preparation of Textual Data

– Techniques for preprocessing textual data: text cleaning, tokenization, lemmatization.

– Using SPSS to prepare textual data for content analysis.

14.3. Categorization and Coding of Data

– Methods for categorizing textual data.

– Application of coding techniques in SPSS to assign categories to textual data.

14.4 Frequency and Distribution Analysis

– Frequency analysis of terms.

– Using SPSS to generate frequency tables and interpret term distributions.

14.5 Co-occurrence Analysis

– Co-occurrence analysis of terms.

– Application of co-occurrence analysis in SPSS to visualize and interpret word networks.

 14.6 Sentiment Analysis

– Sentiment analysis in textual content.

– Using SPSS to analyze sentiment across the text and interpret the results.

14.7. Thematic Analysis

– Thematic analysis of textual data.

– Application of thematic analysis in SPSS

14.8. Using SPSS Syntax

– Using SPSS syntax to automate the execution of textual content analyses.

– Creating scripts to generate automated reports from content analysis results.

14.9. Advanced Applications

– Content analysis with multimedia data.

– Using advanced techniques in SPSS to analyze large volumes of textual and multimedia data.

14.10. Practical Case Studies

– Application of content analysis to real-world case studies in various fields

– Discussion on the conclusions drawn from content analyses and their impact on decision-making.

Objectives

– Understand advanced data visualization techniques.

– Master the use of SPSS to create complex and informative visualizations.

15.1. Introduction to Advanced Visualization Techniques

– Importance of data visualization in exploratory analysis and communication of results.

– Applications of advanced visualization techniques in decision-making and predictive analysis.

15.2. Interactive Visualization

– Using SPSS to create interactive visualizations: dynamic charts, interactive dashboards.

– Integrating interactive features into visualization reports with SPSS.

15.3. 3D Charts and Spatial Visualizations

– Creating 3D charts in SPSS: representing three-dimensional data for in-depth analysis.

– Using spatial visualizations to map geographic and spatial data.

15.4. Temporal Data Visualization

– Techniques for visualizing time series in SPSS: bar charts, line charts with confidence intervals.

– Using temporal visualizations to identify trends and variations in data over time.

15.5. Network Visualization

– Visualizing networks and graphs in SPSS.

– Using network techniques to analyze complex data structures.

15.6. Radar Charts and Multivariate Visualizations

– Creating radar charts to visualize multivariate data profiles.

– Using multivariate visualizations in SPSS.

15.7. Advanced Visualization Techniques

– Advanced visualization techniques in SPSS: dynamic scatter plots, data animations.

– Applying advanced visualizations to present complex insights intuitively.

15.8. Customization and Interactivity

– Customizing charts in SPSS: adjusting colors, legends, and axes to improve readability.

– Adding interactivity to visualizations to allow users to explore data interactively.

15.9. Using SPSS Syntax

– Using SPSS syntax to automate the creation of complex visualizations.

– Creating scripts to generate automated reports with advanced visualizations from analysis results.

15.10. Advanced Applications

– Using advanced visualization techniques to present complex results to diverse audiences.

– Exploring specific use cases where advanced visualizations play a crucial role in understanding data.

15.11. Best Practices in Visualization

– Best practices for designing effective and informative visualizations in SPSS.

– Ethical considerations in data visualization to ensure clarity and accuracy of the presented information.

Objectives

– Understand the importance of automating analyses in SPSS.

– Master automation techniques to improve the efficiency and reproducibility of analyses.

16.1. Introduction to Automating Analyses

– Definition of automating analyses.

– Importance of automation in reducing errors, managing time, and standardizing analyses.

16.2. Using SPSS Syntax

– Introduction to SPSS syntax: a programming language to automate commands and procedures.

– Creating SPSS scripts to perform complex analyses in an automated manner.

16.3. Creating Macros

– Definition of macros in SPSS: recording sequences of actions for reuse.

– Using macros to automate recurring processes and simplify analysis workflows.

16.4. Programming in Python with SPSS

– Integrating Python into SPSS.

– Executing Python scripts to automate advanced tasks and analyze data more flexibly.

16.5. Task Scheduling

– Scheduling tasks in SPSS: using task schedulers to run analyses at regular intervals.

– Automating the generation of reports and sending results via email.

16.6. Using the Report Production Wizard

– Using the Report Production Wizard in SPSS.

– Customizing report templates to include specific charts, tables, and analyses.

16.7. Integration with Other Tools

– Integrating SPSS with other automation and data management tools.

– Exchanging data and results between SPSS and other platforms to optimize analysis processes.

16.8. Security and Access Management

– Best practices for security in automating analyses in SPSS.

– Ethical considerations in automating analyses to ensure data confidentiality and integrity.

16.9. Deployment and Maintenance

– Deploying automation workflows in a production environment.

– Maintaining scripts and macros to ensure the stability and reliability of automated analyses.

16.10. Practical Case Studies

– Applying automation of analyses to real-world case studies in various fields

– Discussing the benefits and challenges encountered when automating analysis processes in SPSS.

Objectives:

– Understand the challenges associated with missing data in statistical analysis.

– Master techniques for managing missing data in SPSS to ensure accuracy of results.

17.1 Introduction to Managing Missing Data

– Definition of missing data: types and common causes of missing data.

– Importance of managing missing data to avoid bias and maximize the effectiveness of analyses.

17.2. Identifying Missing Data

– Techniques to identify missing data in SPSS: visual examination, descriptive statistics.

– Using SPSS to generate reports on missing values and their distribution across variables.

17.3. Methods for Managing Missing Data

– Techniques for imputing missing data: mean, median, mode imputation.

– Using SPSS to apply different imputation methods and compare the results.

17.4 Multiple Imputation

– Concept of multiple imputation.

– Using SPSS to perform multiple imputation analyses and assess the robustness of results.

17.5. Techniques for Missing Data-Sensitive Analysis

– Robust analysis techniques for missing data.

– Applying missing data-sensitive techniques in SPSS to minimize biases in analyses.

17.6. Evaluating the Impact of Missing Data

– Assessing the impact of missing data on analysis results.

– Using SPSS to simulate different missing data scenarios and evaluate their influence on conclusions.

17.7. Practical Strategies for Managing Missing Data

– Developing missing data management strategies tailored to the specific context of the study.

– Discussing best practices to minimize information loss and maximize the validity of analyses in SPSS.

17.8. Using SPSS Syntax

– Using SPSS syntax to automate the management of missing data.

– Creating scripts to systematically apply imputation techniques and missing data-sensitive analyses.

17.9. Advanced Applications

– Managing missing data in large and complex data environments.

– Using advanced techniques in SPSS to handle missing data in longitudinal and multivariate analyses.

17.10. Practical Case Studies

– Applying missing data management techniques to real-world case studies in various fields.

– Discussing specific challenges encountered and solutions adopted to optimize data analysis.

Objectives:

– Understand the importance and applications of scripts in SPSS.

– Master the use of scripts to automate complex tasks and customize analyses.

18.1. Introduction to Scripts in SPSS

– Definition of scripts in SPSS: written programs or instructions to automate repetitive tasks.

– Importance of scripts for enhancing efficiency, accuracy, and reproducibility of analyses.

18.2. Supported Scripting Languages

– Overview of scripting languages supported by SPSS: SPSS syntax, Python, R.

– Comparison of the advantages and limitations of each language for automating analyses.

18.3. Creating and Running SPSS Scripts

– Using SPSS syntax to create scripts: basic commands, control structures, built-in functions.

– Practical examples of SPSS scripts for performing statistical analyses, data transformations, and generating reports.

18.4. Integrating Python and R into SPSS

– Integration of Python into SPSS: using Python scripts to extend analytical capabilities.

– Setting up the Python environment in SPSS and examples of using Python scripts for specific tasks.

18.5. Advanced Script Usage

– Advanced scripting techniques in SPSS: handling complex data manipulations, creating custom models, automating workflows.

– Developing scripts to manage longitudinal, multivariate, and domain-specific analyses.

18.6. Developing Macros and Script Libraries

– Creating macros in SPSS: recording sequences of actions for reuse.

– Managing and using script libraries to share scripts among users and projects.

18.7. Debugging and Optimizing Scripts

– Techniques for debugging scripts in SPSS.

– Optimizing script performance to improve the efficiency and speed of analysis execution.

18.8. Using the Production Report Assistant

– Using the Production Report Assistant in SPSS: creating report templates from scripts.

– Customizing report templates to include specific analyses and generating automated reports.

18.9. Security and Access Management

– Best practices for script security in SPSS.

– Ethical considerations for automating analyses to ensure data confidentiality and integrity.

18.10. Advanced Applications

– Applying scripts in large and complex data environments.

– Using scripts to solve specific problems and perform advanced analyses in various contexts.

18.11. Practical Case Studies

– Applying scripts to real-world case studies in various fields.

– Discussing specific challenges encountered and solutions adopted to optimize the use of scripts in SPSS.

Objectives:

– Understand the methods for exporting results in SPSS.

– Master techniques for presenting results to effectively communicate analysis conclusions.

19.1. Introduction to Exporting Results

– Importance of exporting results in the analysis process: sharing, archiving, and presenting data.

– Supported export formats in SPSS for different types of data and reports.

19.2. Exporting Data

– Techniques for exporting data in SPSS: exporting to common file formats (Excel, CSV, TXT).

– Customizing export options to include metadata and annotations.

19.3. Exporting Graphs

– Exporting graphs in SPSS: generating high-resolution graphics for insertion in presentations and reports.

– Using common graphic formats (PNG, JPEG, PDF) for exporting visual results.

19.4. Creating Reports

– Using the Production Report Assistant in SPSS: creating customized report templates.

– Customizing reports to include specific analyses, data tables, and graphic visualizations.

19.5. Presenting Results

– Techniques for presenting results in SPSS: structuring reports for effective communication.

– Using graphs, tables, and text to interpret and illustrate analysis conclusions.

19.6. Advanced Data Visualization

– Using advanced visualization techniques in SPSS to create dynamic and interactive presentations.

– Integrating complex visualizations into reports to represent relationships and trends in the data.

19.7. Customization and Formatting

– Customizing reports in SPSS: adjusting colors, fonts, and layouts to improve readability.

– Applying formatting standards to ensure consistency and professionalism in reports.

19.8. Using SPSS Syntax to Automate Presentation

– Using SPSS syntax to automate the creation and generation of reports.

– Creating scripts to generate standardized reports from analysis results.

19.9. Exporting to Other Presentation Tools

– Integrating SPSS with other presentation tools (e.g., Microsoft PowerPoint) to include data and graphs.

– Exporting SPSS results to formats compatible with other presentation software.

Objectives:

– Apply the skills acquired throughout the program to a practical project.

– Use SPSS to conduct a comprehensive data analysis and present the results effectively.

20.1. Introduction to the Practical Project

– Overview of the practical project: objectives, methodology, and data used.

– Importance of practical application of SPSS skills to reinforce understanding and expertise.

20.2. Developing the Analysis Plan

– Defining analysis objectives for the project: identifying research questions and hypotheses.

– Planning analysis steps and selecting appropriate techniques in SPSS.

20.3. Data Preparation

– Preprocessing data for the project: cleaning, transforming, and managing missing data.

– Using SPSS to prepare the data for analysis.

20.4. Data Analysis

– Applying statistical analysis techniques in SPSS: parametric and non-parametric tests, regressions, etc.

– Interpreting analysis results to answer research questions.

20.5. Visualization and Interpretation of Results

– Creating visualizations in SPSS to represent analysis results: graphs, tables, and charts.

– Interpreting visualizations to effectively communicate the project’s conclusions.

20.6. Writing the Project Report

– Structuring the project report: introduction, methods, results, discussion, and conclusion.

– Using the SPSS Production Report Assistant to create a well-documented and professional report.

20.7. Presenting the Results

– Preparing an oral or written presentation of the project results.

– Using PowerPoint slides or other tools to present key findings and recommendations.