forklaringsfaktor

Forklaringsfaktor is a term used in statistics and research methodology to denote a factor that helps explain the outcome or pattern observed in data. The exact meaning depends on the analytical approach, but it generally refers to a variable or latent construct that accounts for part of the variance in the dependent variable or across a set of observed variables. The term is common in Norwegian and Danish statistical literatures and is used as a descriptive label for components that contribute to explanation in a model.

In regression analysis, an explanatory factor (forklaringsfaktor) corresponds to an explanatory or predictor variable. The coefficient attached to the factor indicates the expected change in the dependent variable when the factor changes, assuming other factors are held constant. This makes the forklaringsfaktor a central element in assessing influence and significance within the model.

In factor analysis and related methods, a forklaringsfaktor can refer to a latent construct that explains covariation among observed variables. Here, factor loadings show how strongly each observed variable relates to a factor, and the amount of variance explained by the factors is summarized by measures such as communalities or the explained variance percentage.

Because the term is used in multiple disciplines, its exact interpretation varies. It is not a single universal statistic, but rather a descriptive label for anything that helps explain data patterns—ranging from measured variables to latent constructs derived from a model.

Example: In a study on student achievement, study time and prior knowledge can be considered forklaringsfaktorer for exam scores; a regression model would quantify their association with scores, while a factor model might group several related measures under a single explanatory factor such as academic preparedness.