Introduction:

The Quantitative Data Management and Analysis with SPSS course is designed to equip participants with the skills and knowledge necessary to effectively manage and analyze quantitative data using the SPSS software. Participants will learn various techniques for data cleaning, manipulation, and analysis, enabling them to derive meaningful insights and make data-driven decisions. The course combines theoretical concepts with hands-on exercises to ensure participants gain practical experience in using SPSS for data management and statistical analysis.

Course Objectives:

• Understand the principles of quantitative data management and analysis.
• Gain proficiency in using the SPSS software for data entry, cleaning, and manipulation.
• Learn techniques for data screening, transformation, and recoding.
• Explore various statistical analysis methods, including descriptive statistics, inferential statistics, and multivariate analysis.
• Understand how to interpret and report statistical findings accurately and effectively.
• Develop skills in conducting hypothesis testing and interpreting the results.
• Learn advanced techniques such as regression analysis, factor analysis, and cluster analysis using SPSS.
• Understand the basics of data visualization and graphical representation of quantitative data.
• Gain insights into best practices for data management and analysis to ensure accuracy and reproducibility.
• Apply SPSS to real-world datasets and research scenarios through hands-on exercises.

Organizational Benefits:

• Enhanced data-driven decision-making: By equipping employees with SPSS skills, organizations can make informed decisions based on robust data analysis, leading to improved efficiency and effectiveness.
• Improved data management: Proper data management techniques taught in the course ensure data accuracy, consistency, and reliability, minimizing errors and improving data quality.
• Increased research and analytical capabilities: Organizations can conduct rigorous data analysis, enabling them to generate valuable insights and identify trends and patterns for research, planning, and decision-making.
• Streamlined data processing: SPSS automates many data management and analysis tasks, saving time and effort in data processing and allowing organizations to focus on interpreting results and making informed decisions.
• Cost savings: By training employees in-house on SPSS, organizations can save costs associated with outsourcing data analysis tasks or hiring external consultants.
• Improved collaboration and knowledge sharing: SPSS provides a standardized platform for data analysis, facilitating collaboration and knowledge sharing among team members and across departments.
• Compliance with research standards: Training in SPSS ensures that organizations adhere to best practices and standards in quantitative data analysis, increasing the credibility and validity of research findings.
• Enhanced reporting and visualization: SPSS enables organizations to generate clear and visually appealing reports and charts, facilitating effective communication of research findings and data-driven insights.

Who Should Attend:

• Researchers and analysts involved in quantitative data analysis
• Data managers and data scientists
• Statisticians and research methodologists
• Social scientists and policy analysts
• Market researchers and business analysts
• Academic researchers and students
• Professionals in fields such as healthcare, finance, marketing, and social sciences
• Individuals interested in building skills in quantitative data management and analysis

Duration:

5 days

Please note that the course outline provided above is a sample outline and can be customized or adjusted based on specific needs and requirements. The actual duration and number of modules can be tailored to meet the training objectives and time constraints.

 

Course Outline:

Module 1: Introduction to Quantitative Data Management and Analysis

• Overview of quantitative data analysis process
• Importance of data management in quantitative research
• Introduction to SPSS software and its features

Module 2: Introduction to SPSS: Interface and Data Entry

• SPSS interface and menu options
• Creating and importing data files
• Data types and variable properties in SPSS

Module 3: Data Cleaning and Preparation in SPSS

• Identifying and handling missing data
• Dealing with outliers and extreme values
• Data validation and consistency checks

Module 4: Data Screening and Quality Control

• Checking data distribution and normality
• Assessing reliability and validity of measures
• Identifying and addressing data quality issues

Module 5: Data Transformation and Recoding in SPSS

• Transforming variables (e.g., log transformations)
• Recoding variables (e.g., creating categorical variables)
• Computing new variables based on existing ones

Module 6: Descriptive Statistics in SPSS

• Measures of central tendency (mean, median, mode)
• Measures of variability (standard deviation, range)
• Frequency distributions and graphical representations

Module 7: Inferential Statistics: Hypothesis Testing and Confidence Intervals

• Null and alternative hypotheses
• Parametric and nonparametric tests
• Confidence intervals and p-values

Module 8: Bivariate Analysis: T-tests, Chi-square tests, and Correlation in SPSS

• Independent samples t-test
• Paired samples t-test
• Chi-square test of independence
• Pearson correlation analysis

Module 9: Analysis of Variance (ANOVA) in SPSS

• One-way ANOVA
• Factorial ANOVA
• Post-hoc tests and effect size measures

Module 10: Multivariate Analysis: Regression Analysis in SPSS

• Simple linear regression
• Multiple linear regression
• Assessing model fit and interpreting regression coefficients

Module 11: Factor Analysis and Principal Component Analysis in SPSS

• Exploratory factor analysis
• Confirmatory factor analysis
• Interpreting factor loadings and component scores

Module 12: Cluster Analysis in SPSS

• Hierarchical clustering
• K-means clustering
• Interpreting cluster solutions

Module 13: Nonparametric Statistics in SPSS

• Mann-Whitney U test
• Kruskal-Wallis test
• Chi-square test for goodness of fit

Module 14: Survival Analysis in SPSS

• Kaplan-Meier survival analysis
• Cox proportional hazards regression
• Interpreting survival curves and hazard ratios

Module 15: Data Visualization and Graphical Representation in SPSS

• Creating bar charts, line graphs, and scatterplots
• Customizing graph appearance and layout
• Exporting graphs for presentations and reports

Module 16: Reporting and Interpretation of Statistical Results

• Presenting descriptive statistics
• Interpreting inferential statistics
• Communicating research findings effectively

Module 17: Advanced SPSS Techniques and Features

• Logistic regression
• Discriminant analysis
• MANOVA
• Advanced data manipulation techniques

Module 18: Handling Missing Data in SPSS

• Identifying missing data patterns
• Imputation techniques
• Best practices for handling missing data

Module 19: Data-Driven Decision-Making with SPSS

• Using SPSS outputs to inform decision-making
• Evaluating statistical significance and practical significance
• Making recommendations based on data analysis results

Module 20: Best Practices in Data Management and Analysis with SPSS

• Data documentation and organization
• Data security and confidentiality
• Ensuring reproducibility and transparency

 

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