SPSS for Education Data Analysis Training Course

Participants can import education data and create documentation that supports accurate analysis.

• Importing Excel, CSV and text files with controlled variable formats
• Codebooks documenting source systems, field definitions and permissible values
• Long and wide data structures for learner-level repeated observations
• Unique identifiers checked across enrolment, assessment and attendance extracts
• Date variables converted for term, cohort and intervention-period analysis
• Data provenance records identifying collection dates, owners and transformations

Participants can identify, correct and document common data quality problems before analysis.

• Frequencies and descriptive checks revealing invalid codes and impossible values
• Range checks for scores, ages, dates and attendance percentages
• Duplicate case investigation using identifiers, dates and matching fields
• String recoding for inconsistent school names, programme labels and categories
• Outlier screening with boxplots, z scores and contextual checks
• Cleaning logs recording decisions, corrections, exclusions and unresolved issues

Participants can create documented transformations and rerun analyses through SPSS syntax.

• COMPUTE commands creating totals, averages, gains and indicator variables
• RECODE procedures grouping scores, response categories and demographic fields
• IF conditions applying transparent inclusion and eligibility rules
• DO IF structures handling conditional scoring and derived classifications
• Value labels and variable labels maintained after transformations
• Syntax comments documenting assumptions, versions and analytical intent

Participants can summarise learner and programme data using appropriate descriptive measures.

• Frequency tables for enrolment, completion, attendance and response patterns
• Means, medians and standard deviations for assessment-score distributions
• Cross-tabulations examining attainment by grade, gender or programme group
• Percentiles and proficiency bands describing score-position distributions
• Missing-data profiles separated by school, cohort and demographic category
• Descriptive narratives distinguishing observed patterns from causal claims

Participants can create and assess charts that communicate education data patterns accurately.

• Histograms assessing score distributions, skewness and potential ceiling effects
• Boxplots comparing attainment distributions across schools or intervention groups
• Bar charts displaying categorical survey responses with meaningful denominators
• Line charts tracking attendance, retention or progress across reporting periods
• Scatterplots examining associations between study time, attendance and outcomes
• Chart annotations stating sample size, units, timeframe and data source

Participants can explain sampling uncertainty and use confidence intervals in education reporting.

• Target populations, sampling frames and analytic samples clearly distinguished
• Probability and non-probability sampling implications for education evidence
• Standard errors and confidence intervals for estimated means and proportions
• Sampling variation demonstrated through repeated samples and simulated estimates
• Statistical significance interpreted alongside uncertainty and substantive importance
• Type I and Type II errors in intervention evaluation decisions

Participants can formulate testable hypotheses and interpret results beyond p-values.

• Null and alternative hypotheses stated for education research questions
• P values interpreted without treating thresholds as proof
• Cohen's d for standardised differences between learner groups
• Eta squared and partial eta squared for explained variance
• Cramer's V quantifying association in categorical education data
• Confidence intervals used to judge plausible effect-size ranges

Participants can compare education groups and repeated scores using appropriate t-tests.

• Independent-samples t tests comparing mean outcomes between two groups
• Paired-samples t tests assessing pretest and posttest score change
• Levene's test and unequal-variance interpretations for group comparisons
• Assumption checks using distributions, outliers and group sample sizes
• Mean differences reported with confidence intervals and Cohen's d
• Baseline differences considered before attributing intervention effects

Participants can use ANOVA methods to compare outcomes across several education groups.

• One-way ANOVA comparing attainment across schools, programmes or cohorts
• Post hoc comparisons using Tukey, Games-Howell and Bonferroni procedures
• Homogeneity of variance assessed through Levene's test and diagnostics
• Two-way ANOVA examining group differences and interaction effects
• Estimated marginal means interpreted for factorial education designs
• ANOVA results reported with F statistics, effect sizes and intervals

Participants can select non-parametric methods when data do not meet parametric requirements.

• Mann-Whitney U tests comparing ordinal or skewed group outcomes
• Wilcoxon signed-rank tests evaluating paired ordinal survey responses
• Kruskal-Wallis tests comparing several independent education groups
• Friedman tests analysing repeated ratings from the same participants
• Chi-square tests examining associations between categorical learner variables
• Exact tests considered where expected cell counts are small

Participants can assess and report relationships between education variables without causal overclaiming.

• Pearson correlation for approximately linear continuous variable relationships
• Spearman correlation for ordinal, ranked or non-normal education measures
• Correlation matrices screened for patterns, outliers and missingness
• Scatterplots used to inspect linearity, clusters and influential cases
• Correlation coefficients interpreted by magnitude, direction and uncertainty
• Association language avoiding unsupported causal or predictive claims

Participants can build and interpret linear regression models for continuous education outcomes.

• Regression questions specifying outcome, predictors and theoretically relevant controls
• Enter, hierarchical and stepwise methods distinguished for model construction
• Dummy coding categorical predictors such as programme or school type
• R squared and adjusted R squared interpreted for model fit
• Regression coefficients interpreted with confidence intervals and practical units
• Residual diagnostics checking linearity, normality, homoscedasticity and influential cases

Participants can model binary outcomes such as completion, retention and proficiency status.

• Binary outcome coding for dropout, completion, pass status and referral
• Logistic regression models estimating odds of defined learner outcomes
• Odds ratios interpreted with confidence intervals and meaningful reference groups
• Classification tables assessed against prevalence and decision consequences
• Predicted probabilities compared across realistic learner-profile scenarios
• Model limitations stated for observational administrative-data analyses

Participants can score education questionnaires and evaluate internal consistency appropriately.

• Likert-scale coding including reverse-scored attitude and engagement items
• Missing-item rules defined before calculating questionnaire scale scores
• Cronbach's alpha interpreted with item count and construct considerations
• Corrected item-total correlations identifying weak or inconsistent items
• Alpha-if-item-deleted reviewed without mechanically removing content
• Scale-score distributions inspected for floor and ceiling effects

Participants can conduct exploratory factor analysis to investigate questionnaire construct structure.

• Kaiser-Meyer-Olkin measure and Bartlett's test assessing factorability
• Principal axis factoring distinguished from principal components analysis
• Scree plots and parallel analysis informing factor-retention decisions
• Oblimin rotation used when education constructs are expected to correlate
• Factor loadings, cross-loadings and communalities evaluated against criteria
• Factor scores and scale revisions documented for future validation

Participants can analyse learner change across time while accounting for repeated observations.

• Pretest-posttest designs distinguished from simple cross-sectional group comparisons
• Repeated-measures ANOVA analysing outcomes across multiple assessment occasions
• Sphericity assessed using Mauchly's test and corrected degrees of freedom
• Time-by-group interactions interpreted for intervention and comparison groups
• Change scores, gain scores and baseline adjustment compared critically
• Attrition patterns examined before interpreting longitudinal outcome changes

Participants can manage missing education data and apply responsible reporting controls.

• Missing completely at random, at random and not at random
• Item nonresponse and unit nonresponse quantified before analytical decisions
• Listwise deletion limitations assessed against missingness patterns and sample loss
• Multiple imputation principles considered for suitable education research designs
• Small-cell suppression protecting learners, staff and sensitive subgroup identities
• Consent, purpose limitation and retention rules recorded in analysis plans

Participants can turn SPSS output into clear, proportionate and decision-ready evidence.

• APA Style statistical notation for tests, estimates and effect sizes
• Results tables separating descriptive statistics, model estimates and interpretation
• Figures selected to answer stakeholder questions without visual distortion
• Plain-language findings distinguishing evidence, inference, limitation and recommendation
• Technical appendices documenting variables, syntax, assumptions and exclusions
• Executive summaries linking findings to programme, policy or resource decisions

Participants can build, review and present a complete documented SPSS analysis of an education dataset.

• Analysis brief defining decision question, population, outcomes and stakeholders
• Cleaned SPSS dataset with codebook, transformations and documented syntax
• Method selection justified through variable types, design and assumptions
• Descriptive, inferential and model outputs checked for interpretation accuracy
• APA-style tables and figures prepared for a defined education audience
• Presentation and peer review addressing limitations, ethics and recommended actions