Statistics Essentials Using SPSS Training Course

Module 1: Foundations of Statistics and Data Types

• Definition of statistics, populations, and samples

• Types of data: Qualitative vs. Quantitative data

• Measurement scales: Nominal, Ordinal, Interval, and Ratio

• Introduction to study design: Experimental vs. Observational studies

• Data collection principles and common sources of data

• Practical session: Identifying data types and measurement scales for a real-world dataset using SPSS

Module 2: Descriptive Statistics and Data Visualization

• Measures of central tendency: Mean, Median, and Mode

• Measures of dispersion: Variance, Standard Deviation, and Range

• Measures of position: Quartiles, Percentiles, and Box Plots

• Visualizing data: Histograms, Bar Charts, and Scatter Plots

• Skewness and Kurtosis: Understanding data shape

• Practical session: Calculating key descriptive statistics and generating appropriate visualizations using SPSS

Module 3: Probability and Theoretical Distributions

• Basic rules of probability: Addition and Multiplication rules

• Conditional probability and independence

• Discrete probability distributions: Binomial and Poisson

• Continuous probability distributions: The Normal Distribution and Z-scores

• The Central Limit Theorem and its importance in inference

• Practical session: Calculating probabilities and finding values under the Normal curve using SPSS output

Module 4: Sampling Methods and Statistical Estimation

• Common sampling techniques: Simple Random, Stratified, and Cluster Sampling

• Defining the sampling distribution and its properties

• Point estimates versus interval estimates

• Constructing and interpreting Confidence Intervals for the mean and proportion

• Determining required sample size for specific precision levels

• Practical session: Using simulated data to demonstrate the Central Limit Theorem and calculate a Confidence Interval in SPSS

Module 5: Principles of Hypothesis Testing

• The logic of hypothesis testing: Null and Alternative hypotheses (H₀ and Hₐ)

• Type I (α) and Type II (β) errors and the concept of statistical power

• Steps of a formal hypothesis test (test statistic, p-value, decision rule)

• Understanding and interpreting the p-value

• Z-tests for single population means and proportions

• Practical session: Performing a one-sample Z-test for a mean and interpreting the results in a full write-up using SPSS

Module 6: Comparative Analysis: t-Tests and ANOVA

• One-sample, Independent Samples, and Paired Samples t-tests

• Assumptions required for using t-tests

• Introduction to Analysis of Variance (ANOVA) for comparing multiple means

• Interpreting the ANOVA F-statistic and omnibus test result

• Post-hoc analysis for identifying specific group differences

• Practical session: Conducting an Independent Samples t-test and a One-Way ANOVA in SPSS

Module 7: Introduction to Correlation and Simple Linear Regression

• Measures of association: Covariance and Pearson's Correlation Coefficient (r)

• The concept of linear association and the scatter plot

• Simple linear regression: Least Squares method and fitting the line

• Interpreting the slope (b₁) and intercept (b₀)

• Assessing model fit: R² and hypothesis test for the slope

• Practical session: Calculating the correlation, fitting a simple linear regression model, and interpreting coefficients in SPSS

Module 8: Non-Parametric Statistical Methods

• When to use non-parametric tests (non-normal data, ordinal scale data)

• Wilcoxon Rank-Sum test (non-parametric alternative to independent t-test)

• Kruskal-Wallis test (non-parametric alternative to One-Way ANOVA)

• Sign Test and Wilcoxon Signed-Rank test (non-parametric alternatives to paired t-test)

• Chi-Square test for independence and goodness-of-fit

• Practical session: Performing a Chi-Square test of independence and a Kruskal-Wallis test on categorical/non-normal data using SPSS

Module 9: Fundamentals of Time Series and Index Numbers

• Introduction to time series data and its components (trend, seasonality, cycle, irregularity)

• Methods for time series decomposition (Classical Decomposition)

• Introduction to forecasting concepts and basic techniques (moving averages)

• Defining and constructing index numbers (e.g., Laspeyres and Paasche)

• Practical application of index numbers in economic reporting

• Practical session: Calculating and interpreting a three-period moving average and constructing a simple price index in SPSS

Module 10: Statistical Software Application and Reporting

• SPSS Interface and Data Management: Using Data View, Variable View, and Syntax Editor

• Data Manipulation in SPSS: Recoding variables, computing new variables, and splitting files

• Executing Analysis via SPSS Dialog Boxes and Syntax: Comparing point-and-click versus command syntax

• Interpreting and Exporting SPSS Output: Understanding the Viewer window and tables

• Data Visualization and Chart Customization in SPSS: Creating high-quality graphs for reports

• Practical session: Running an end-to-end analysis on a new dataset and producing a concise, professional statistical report using SPSS

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