Introduction:

The Advanced Quantitative Data Management and Analysis with SPSS course is designed to provide participants with an in-depth understanding of advanced techniques and best practices for managing and analyzing quantitative data using SPSS software. SPSS is a widely-used tool in the field of data analysis and statistics. This course aims to enhance participants' skills and knowledge in data management, manipulation, and advanced statistical analysis using SPSS.

Objective:

 The main objective of this training program is to enable participants to proficiently manage and analyze quantitative data using advanced techniques in SPSS. By the end of the course, participants will be able to:

1. Understand advanced principles of quantitative data management and analysis.
2. Perform complex data transformations and recoding in SPSS.
3. Conduct advanced statistical analyses, including multivariate techniques.
4. Perform data mining and predictive modeling using SPSS.
5. Interpret and communicate advanced statistical findings effectively.
6. Apply advanced techniques for data visualization and reporting using SPSS.
7. Enhance data management and analysis efficiency through automation and scripting in SPSS.

Organizational Benefits:

By attending this training program, organizations can expect the following benefits:

1. Enhanced data analysis capabilities: Participants will acquire advanced skills in statistical analysis, enabling organizations to extract more comprehensive and accurate insights from their quantitative data.
2. Improved decision-making: Through advanced data analysis techniques, organizations will have access to more robust and reliable information for making data-driven decisions.
3. Efficient data management: Participants will gain expertise in managing large datasets and performing complex data transformations, improving data quality and integrity.
4. Predictive modeling and data mining: Organizations will learn techniques for building predictive models and extracting valuable patterns and trends from their data.
5. Enhanced reporting and visualization: Participants will acquire skills to create impactful visualizations and reports, enabling clear communication of analysis findings to stakeholders.
6. Automation and efficiency: The course covers automation and scripting techniques in SPSS, allowing organizations to streamline repetitive tasks and improve overall efficiency in data management and analysis.

Who Should Attend:

This training program is suitable for individuals and organizations involved in advanced quantitative data analysis, research, and decision-making. The target audience includes:

1. Data analysts and researchers familiar with SPSS.
2. Statisticians and data scientists looking to expand their skills in SPSS.
3. Advanced-level SPSS users seeking to deepen their knowledge in data analysis techniques.
4. Professionals involved in complex data analysis and modeling.
5. Graduates and postgraduates in quantitative fields seeking to enhance their analytical skills.

Duration:

The Advanced Quantitative Data Management and Analysis with SPSS course has a duration of 10 days. This comprehensive training allows participants to gain a thorough understanding of advanced techniques in SPSS for quantitative data analysis.

Course Outline:

 The training program consists of more than 20 modules, covering various advanced topics in quantitative data management and analysis using SPSS.

Course Outline:

Module 1: Introduction to Advanced Quantitative Data Management and Analysis

• Overview of advanced techniques and approaches in quantitative data management and analysis
• Introduction to SPSS and its advanced features
• Understanding the importance of data quality and integrity

Module 2: Complex Data Transformations and Manipulation

• Advanced data cleaning and manipulation techniques in SPSS
• Recoding variables and creating derived variables
• Handling missing data and outliers in advanced analysis

Module 3: Multivariate Data Analysis Techniques

• Introduction to multivariate analysis
• Exploratory factor analysis and principal component analysis in SPSS
• Interpreting factor loadings and component scores

Module 4: Advanced Regression Analysis

• Multiple regression analysis and its applications in SPSS
• Logistic regression analysis for binary outcomes
• Hierarchical regression analysis for modeling complex relationships

Module 5: Analysis of Variance (ANOVA) and Experimental Designs

• Advanced ANOVA techniques, including factorial ANOVA and repeated measures ANOVA
• Post-hoc tests and pairwise comparisons in SPSS
• Designing and analyzing experiments with multiple factors

Module 6: Advanced Techniques for Data Mining and Predictive Modeling

• Introduction to data mining concepts and techniques
• Building predictive models using decision trees, random forests, and other algorithms in SPSS
• Evaluating model performance and making predictions

Module 7: Advanced Statistical Techniques: Chi-square tests, Nonparametric tests, and Survival Analysis

• Advanced statistical tests, including chi-square tests and nonparametric tests, in SPSS
• Survival analysis techniques for time-to-event data
• Interpreting output and drawing conclusions from advanced statistical tests

Module 8: Advanced Techniques for Data Visualization in SPSS

• Creating advanced data visualizations, including customized charts and graphs, in SPSS
• Interactive and dynamic data visualization techniques
• Effectively communicating complex analysis findings through visualizations

Module 9: Automation and Scripting in SPSS

• Introduction to SPSS syntax and scripting
• Automating repetitive tasks and analyses using SPSS syntax
• Customizing and extending SPSS functionalities through scripting

Module 10: Reporting and Presenting Advanced Analysis Results

• Creating comprehensive reports and summaries of advanced analysis using SPSS
• Designing effective visual presentations of advanced analysis findings
• Communicating complex statistical results to stakeholders

Module 11: Longitudinal Data Analysis

• Analyzing longitudinal data using mixed-effects models in SPSS
• Handling missing data and modeling growth trajectories

Module 12: Structural Equation Modeling (SEM)

• Introduction to SEM and its applications in SPSS
• Specifying and estimating SEM models using SPSS
• Interpreting path coefficients and model fit indices

Module 13: Advanced Data Management Techniques

• Advanced data management techniques, including reshaping and restructuring data in SPSS
• Combining and merging datasets with different structures
• Working with large datasets efficiently

Module 14: Advanced Statistical Techniques: Advanced Analysis of Variance (ANOVA)

• Advanced ANOVA techniques, such as MANOVA and ANCOVA
• Interpreting output and conducting post-hoc analyses in SPSS

Module 15: Item Response Theory (IRT) Analysis

• Introduction to IRT and its applications in testing and measurement
• Estimating item parameters and assessing model fit using SPSS

Module 16: Advanced Techniques for Survey Data Analysis

• Analyzing complex survey data using specialized techniques in SPSS
• Applying survey weights and accounting for complex sampling designs
• Generating accurate estimates and standard errors

Module 17: Advanced Techniques for Categorical Data Analysis

• Advanced techniques for analyzing categorical data, including multinomial logistic regression and log-linear models in SPSS
• Interpreting odds ratios and conducting model diagnostics

Module 18: Advanced Techniques for Big Data Analytics

• Handling and analyzing big data using SPSS
• Sampling techniques and data reduction strategies for big data analysis
• Applying advanced analytics to extract insights from large datasets

Module 19: Advanced Techniques for Time Series Analysis

• Time series analysis techniques, including ARIMA and exponential smoothing, in SPSS
• Forecasting future values and evaluating forecast accuracy

Module 20: Case Studies and Practical Applications

• Applying advanced SPSS techniques to real-world case studies and datasets
• Addressing data analysis challenges and solving complex problems
• Hands-on exercises and projects to reinforce learning

General Notes

·         All our courses can be Tailor-made to participants' needs

·         The participant must be conversant in English

·         Presentations are well-guided, practical exercises, web-based tutorials, and group work. Our facilitators are experts with more than 10 years of experience.

·         Upon completion of training the participant will be issued with a Foscore development center certificate (FDC-K)

·         Training will be done at the Foscore development center (FDC-K) centers. We also offer inhouse and online training on the client schedule

·         Course duration is flexible and the contents can be modified to fit any number of days.

·         The course fee for onsite training includes facilitation training materials, 2 coffee breaks, a buffet lunch, and a Certificate of successful completion of Training. Participants will be responsible for their own travel expenses and arrangements, airport transfers, visa application dinners, health/accident insurance, and other personal expenses.

·         Accommodation, pickup, freight booking, and Visa processing arrangement, are done on request, at discounted prices.

·         Tablet and Laptops are provided to participants on request as an add-on cost to the training fee.

·         One-year free Consultation and Coaching provided after the course.

·         Register as a group of more than two and enjoy a discount of (10% to 50%)

·         Payment should be done before commence of the training or as agreed by the parties, to the FOSCORE DEVELOPMENT CENTER account, so as to enable us to prepare better for you.

·         For any inquiries reach us at training@fdc-k.org or +254712260031

 

 

·         Website:www.fdc-k.org