Explore the world of informetrics and scientometrics! Learn to measure information, analyze scientific impact, and understand knowledge growth. Gain valuable insights into research trends and data analysis techniques essential for library science and beyond.
Learn about data types and four measurement scales (nominal, ordinal, interval, ratio) to ensure correct statistical analysis for your research.
Learn to choose the right chart for your data: dotplots, stemplots, bar graphs, histograms, box plots, and pie charts explained.
Learn about mean, median, and mode—key measures of central tendency. Understand their calculations, uses, limitations, and how they describe data distribution.
Understand data variability: learn range, standard deviation, variance, coefficient of variation, skewness, and kurtosis to accurately interpret research and bibliometric data.
This guide explains correlation and regression, their math, and how to apply them for understanding and predicting relationships in research data.
Unlock probability’s core: from classical roots & axiomatic foundations to key theorems like Bayes’ theorem & practical applications in information science.
Learn about random variables: discrete vs. continuous, probability distributions, mean, and variance, with applications in informetrics and scientometrics.
Explore joint probability distributions: understand how multiple random variables behave together, revealing dependencies crucial for informetrics, weather forecasting, and market research.
Understand conditional probability: P(B|A). Learn the formula, its real-world applications in medical diagnosis, finance, and how Bayes’ Theorem updates beliefs.
Explore binomial, Poisson, and normal distributions—key statistical tools for understanding data patterns in research, economics, and information science.
Probability shapes decisions under uncertainty, models complex systems, and powers statistical inference in finance, health, tech, and science.
Learn simple linear regression: understand variables, the regression equation, how the line fits, key assumptions, and model interpretation for informetrics research.
Learn multiple regression: predict outcomes with multiple factors, understand the equation, interpret coefficients, and evaluate model fit with R², F-test, and t-tests.
Learn about stepwise regression for variable selection in predictive models, its types, applications in scientometrics, and vital overfitting risks.
Learn how to use dummy variables in regression analysis to effectively incorporate qualitative data like discipline or institution type into your models for better insights.
Learn how multidimensional data and matrix analysis are crucial in scientometrics for understanding research patterns through cluster analysis, PCA, and factor analysis.
Understand cluster analysis methods (hierarchical & non-hierarchical), distance measures, and practical steps for grouping data in scientometrics and informetrics.
Learn about Factor Analysis, a statistical method that uncovers hidden patterns and latent relationships in large datasets, and how it differs from PCA.
Explore cluster and factor analysis to make sense of complex research data. Group countries by chemistry specialization and map AI’s intellectual base.