Module 1: Introduction to Survey Data and SPSS

• Understanding Survey Data: Types of questions, response scales
• Introduction to SPSS Interface: Data View, Variable View, Output Viewer
• Defining Variables in SPSS: Name, Type, Width, Decimals, Label, Values
• Entering and Importing Data into SPSS: Manual entry, importing Excel files
• Saving and Opening SPSS Data Files (.sav)
• Practical session: Navigating SPSS, defining variables, and manually entering a small dataset.

Module 2: Data Preparation and Management in SPSS

• Data Cleaning Techniques: Identifying and handling missing values
• Recoding Variables: Transforming continuous to categorical, collapsing categories
• Computing New Variables: Creating derived variables from existing ones
• Merging and Appending Datasets: Combining different survey files
• Splitting Files and Selecting Cases: Analyzing subsets of data
• Practical session: Importing a larger dataset, identifying missing values, recoding a variable, and computing a new score.

Module 3: Descriptive Statistics for Survey Data

• Measures of Central Tendency: Mean, Median, Mode
• Measures of Dispersion: Standard Deviation, Variance, Range
• Frequency Distributions: Tables and percentages for categorical data
• Describing Ordinal and Scale Data
• Interpreting Skewness and Kurtosis: Understanding data distribution
• Practical session: Generating frequency tables and descriptive statistics for various variables in a survey dataset.

Module 4: Visualizing Survey Data

• Creating Bar Charts and Pie Charts for Categorical Data
• Generating Histograms for Continuous Data
• Box Plots: Visualizing distributions and outliers
• Scatter Plots: Exploring relationships between two continuous variables
• Customizing Charts: Titles, labels, colors, and themes
• Practical session: Creating various types of charts (bar, histogram, box plot) from the survey data and customizing their appearance.

Module 5: Hypothesis Testing Fundamentals

• Introduction to Statistical Inference: Population vs. sample
• Null and Alternative Hypotheses: Formulating research questions statistically
• Significance Level (α) and P-value: Understanding statistical significance
• Type I and Type II Errors: Risks in hypothesis testing
• Steps in Hypothesis Testing: A systematic approach
• Practical session: Formulating hypotheses for a given research scenario and identifying the appropriate statistical test.

Module 6: Comparing Means (T-tests and ANOVA)

• Independent Samples t-test: Comparing two independent group means
• Paired Samples t-test: Comparing means from related samples
• One-Way ANOVA: Comparing means of three or more independent groups
• Post-Hoc Tests: Identifying specific group differences after ANOVA
• Interpreting Output: Significance values, effect sizes
• Practical session: Performing an independent samples t-test and a one-way ANOVA on the survey data, interpreting the results.

Module 7: Analyzing Relationships Between Categorical Variables (Chi-Square)

• Cross-tabulation (Contingency Tables): Displaying relationships between categorical variables
• Chi-Square Test of Independence: Assessing association between two categorical variables
• Measures of Association for Nominal and Ordinal Data: Phi, Cramer's V
• Interpreting Chi-Square Output: Expected counts, p-value
• Limitations of Chi-Square Test
• Practical session: Conducting a Chi-Square test to examine the relationship between two categorical variables in the survey data.

Module 8: Correlation and Regression Analysis

• Pearson Correlation Coefficient: Measuring linear relationships between continuous variables
• Spearman's Rho: For monotonic relationships and ordinal data
• Simple Linear Regression: Predicting one variable from another
• Interpreting Regression Output: R-squared, coefficients, p-values
• Assumptions of Linear Regression: Checking for violations
• Practical session: Performing a correlation analysis and a simple linear regression in SPSS, interpreting the results.

Module 9: Reliability and Factor Analysis

• Reliability Analysis: Assessing the consistency and stability of a scale
• Cronbach's Alpha: A common measure of internal consistency
• Introduction to Factor Analysis: Reducing dimensionality, identifying underlying constructs
• Exploratory Factor Analysis (EFA) concepts: Eigenvalues, factor loadings
• Interpreting Factor Analysis Output (conceptual overview)
• Practical session: Conducting a reliability analysis (Cronbach's Alpha) for a multi-item scale in the survey dataset.

Module 10: Advanced Topics and Reporting Survey Findings

• Introduction to Multiple Regression (conceptual overview)
• Best Practices for Presenting Survey Findings: Clear and concise reporting
• Ethical Considerations in Survey Data Analysis
• Interpreting and Discussing Implications of Results
• Using SPSS Syntax for Reproducibility
• Practical session: Reviewing and interpreting comprehensive SPSS output for various analyses, and discussing how to structure a findings report.

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588