SPSS Masterclass: Learn SPSS Scratch to Advanced

Explore the fundamentals of statistics, including descriptive statistics like mean, median, mode, and standard deviation, and how to draw inferences with parametric and non-parametric tests.

Compare parametric and non-parametric tests and explain core assumptions—normality, homogeneity of variance, independence, and randomness—while noting interval-scale data and the central limit theorem for generalizable results.

Conceptualize and name research variables in SPSS, identify independent and dependent variables, mediators, moderators, and controls, and model relationships with concrete examples like performance, intelligence, and stress.

Define numeric SPSS variables by entering names, setting width and decimals, and using labels to describe data; enforce valid names by avoiding spaces and starting with numbers or symbols.

Explore scientific notation as a SPSS variable type, converting numbers like 10 raised to power n and 0.001 mm into 1.00 e minus 3, with decimal adjustments.

Learn to define and convert a date variable in SPSS, apply the dd-mmm-yyyy format, enter birth dates, and explore complex timestamps up to milliseconds for time-sensitive data.

define the dollar variable type in spss, explore currency formats from one dollar to million, and apply them to income data, including converting values from scientific notation.

Master SPSS custom currency options to define multiple currencies (CCA, CCB, CCC), apply rupee prefix, handle negative values, and choose period or comma decimal separators.

Learn how to work with string variables in SPSS, storing qualitative data like names and feedback, converting from numeric to string, and managing width to prevent truncation.

Learn how restricted numeric variables in SPSS handle positive values without digit grouping, and how width and leading zeros affect data entry.

Define value levels and labels in SPSS using the values option, with gender and income levels, label Likert items, and edit or remove levels as needed.

Identify and define missing values in SPSS using discrete, range, and system-missing options, exclude them from analysis, and replace with identifiable numbers for clear modeling.

Learn how columns and alignment control the width and placement of SPSS variables, including the default width of 8 and right alignment, and how to adjust them.

Explore SPSS measure options, defining nominal, ordinal, interval, and ratio scales, with categorical and continuous distinctions; understand mutually exclusive categories, rank order, and absolute zero in social science data.

Explore the compute variable function in the transform menu to perform arithmetical and logical operations in SPSS, including abs, cos, exponential, and logarithmic transformations with base 10.

Learn to compute total scores in SPSS with the compute function using arithmetic operators, defining target variables, and creating subscales for neuroticism, stability, introversion, and extroversion.

Practice calculating the salary difference between current and starting pay for minority employees using SPSS conditional compute on the employee dataset.

learn to create a conditional compute in SPSS to calculate salary differentials for minority employees by subtracting beginning salary from current salary and filtering with the minority classification equals one.

Learn how to use the SPSS record function to reverse score negative items and compute total scores for a personality scale.

Discover why SPSS offers two record functions in transform: record into same variable or record into different variable, enabling reverse scoring while preserving original data.

Learn to recode into different variable in SPSS by selecting reverse-scored items, defining output names, mapping old and new values, and converting to scale for a total personality score.

Compute the total personality score using the compute variable option, applying reverse-coded items (1, 3, 7, 11, 13) and summing all items; a higher score reflects more desirable work traits.

Explore using SPSS recordIntoSameVariable to reverse code negatively worded items and compare with recordIntoDifferentVariable, demonstrating data preservation versus replacement and total score equivalence.

Learn to calculate descriptive statistics in SPSS by importing a data set and analyzing variables like age, glucose level, income, and treatment cost, including mean, median, mode, and percentiles.

Explore SPSS descriptive statistics to compute frequencies, measures of central tendency, dispersion, distribution, and percentiles, including mean, median, mode, standard deviation, skewness, and kurtosis.

Learn to use SPSS descriptive stats to partition results by a factor with the explore option, compare averages across education levels, and generate z-scores.

Learn to use the frequencies tab to build tables for continuous and categorical data. Interpret counts, percentages, valid percentages, and cumulative percentages with examples from bank loan and education data.

Learn to generate and interpret crosstab descriptives in SPSS masterclass, using row and column percentages to compare defaulters and education levels, with guidance on interpreting column and row percentages.

Discover how to compute mean, median, mode, and sum in SPSS, and learn when to report mean versus median for non-normal or highly dispersed data.

Compute mean, median, and mode for age and income, interpret differences (mean 44, median 45, mode 51), and explain when to rely on mean or median with continuous, non-normal data.

Explore how to confirm the mode with frequencies, compare mean and median for age and household income, and recognize why the mode can be misleading for continuous data in SPSS.

Apply the explore option in this SPSS masterclass to generate grouped descriptives for automobile and truck types, reporting 95 percent confidence interval of the mean for sales and resale prices.

Explore how SPSS presents groupwise means and their 95% confidence intervals for sale and resale values across automobiles and trucks.

Learn the 5% trimmed mean and why trimming outliers matters for non-normal data. The trimmed mean may differ from the simple mean, highlighting why reporting it with reasons improves interpretation.

Learn how the median reveals non-normal data behavior, compare mean and mode, and compute standard deviation and variance, then apply minimum, maximum, and range in SPSS.

Explore quartiles and the interquartile range in SPSS, focusing on P25, P50, and P75, and see how IQR and standard deviation reveal the sales spread between automobiles and trucks.

Explore skewness and kurtosis as measures of deviation from normality, including positive and negative skewness and real-world salary examples that illustrate mean-based deviations, in the SPSS masterclass.

Calculate significance of skewness by dividing skewness by its standard error, using thresholds 1.98, 2.58, and 3.98 for 0.05, 0.01, and 0.001, and apply to sales data comparisons.

Explore kurtosis as a measure of deviation from normality, distinguishing leptokurtic, mesokurtic, and platykurtic shapes, and learn why highly skewed, positively kurtotic data favor medians over means.

Understand the standard error of the mean, which measures how a sample mean may deviate from the population mean using standard deviation divided by square root of sample size.

Explore descriptive statistics in SPSS with population descriptives, compare population and sample results, and report mean, standard deviation, and variance with the correct n or n-1 denominator.

Learn to calculate differences between two group means with the independent sample t-test, convert a string gender variable to numeric, define groups, and compare salaries by gender.

Explore independent sample t-tests by interpreting descriptive output, comparing mean salaries for gender groups, and understanding standard deviation and standard error of the mean in population inference.

An independent samples t-test reveals significant salary differences between males and females, accounting for non-homogeneous variances via Levene's test, with p<0.001 and a 95% confidence interval for the mean difference.

Learn to write APA style results for an independent sample t-test, including reporting the t value, degrees of freedom, p values, 95% confidence interval, and group means.

Learn how to apply the paired-sample t-test, also called dependent or collected-sample t-test, for repeated-measures designs to detect pre-post differences in performance, mood, and attitudes.

Learn how to perform a paired sample t-test in SPSS to compare employees' current salaries with their beginning salaries, defining pairs and noting that variable order can affect the t-value.

Interpret paired sample t-test outputs by comparing descriptive statistics and correlation to show salary growth for the same employees; current salary 34,419 vs beginning 17,016, correlation 0.88, alpha 0.001.

learn to report a paired sample t-test in APA style, presenting means, standard deviations, degrees of freedom, and the t value to show a significant salary increase since joining.