Logistic regression regards regressing a dichotomous dependent variable on the basis of one or more independent variables. In the case of exploratory logistic regression, there are no a priori hypotheses. It makes use of maximum-likelihood estimation which selects coefficients that make the observed values most likely to have occurred.

Logistic regression cannot make use of linear regression because it violates the assumption of linearity (1), homoscedasticity (2) and normally distributed residuals (3). This is because it makes use of a dichotomous dependent variable.

Logistic regression makes use of a different formula than linear regression

This can be interpreted as the probability that y(i) = 1 equals some function of the intercept plus the independent variable weighed by some variable. The scale is between 0 and 1.  This allows researchers to approach the data in an

Evidence-based treatment refers to interventions or techniques that have therapeutic changes in controlled trials. Disadvantages are that key conditions and characteristics of treatment research are different from clinical practice and that research tends to focus on symptoms, rather than the patient as a whole. Determining whether a treatment is evidence-based or empirically supported has different criteria (1), makes use of arbitrary rating scales (2) and often has to deal with mixed results (3).

Evidence-based practice refers to clinical practice informed by evidence about interventions, clinical expertise, patient needs, values and preferences. Concerns regarding this are that not everything has been researched (e.g. rare disease) (1), clinical decision making is unreliable (2), generalization of results is difficult (3), determining which variables make the difference in a treatment is difficult (4) and clinical progress is often evaluated based on clinical impressions (5).

The goals of research

In the advanced neuropsychological diagnostics infrastructure (ANDI), datasets of several research groups are combined into a single database. It contains scores of neuropsychological tests from healthy participants. This allows for accurate neuropsychological assessment as the quantity and the range of the data surpasses most traditional normative data. It facilitates normative comparison methods (e.g. those in which entire profiles of scores are evaluated).

An important element of neuropsychological practice is to determine whether a patient who presents with cognitive complaints has abnormal scores on neuropsychological tests. In the diagnostic process, a number of neuropsychological tests are administered and the test results of a patient are compared to a normative sample.

Scores of patients are typically compared to normative data published in the manuals of an instrument. However, this data may be outdated (1), it may lack norms for very old populations (2), some tests do

Multilevel modelling (MLM) is becoming the standard for analysing nested data.

The unit of analysis problem refers to when the moderator variable exists at a different level than the independent and dependent variable (e.g. moderator variable is on the level of the course itself and the independent and dependent variables are on the individual level). Multilevel modelling (MLM) or hierarchical linear modelling is designed to analyse data that exists at different levels.

When individuals exist in natural groups such as schools, there is a hierarchical or nested structure. Ignoring the nested structure of the data could have adverse consequences. Person-analyses with nested data ignore the fact that individuals sharing a common environment and are more similar to each other than they are to individuals from another environment. Nested data may thus violate the assumption of independence of observations. This leads to an increase of