Microsoft Word 1-Experimental Analysis on the Development of Cognitive Processes in Childhood through Body Experience

Instruments 
The measurement was performed by using the neuropsychological test battery NEPSY-II (Urgesi, 
Campanella, & Fabbro, 2006). NEPSY is a unique neuropsychological assessment battery as the tests are 

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COGNITIVE PROCESSES IN CHILDHOOD THROUGH BODY EXPERIENCE 
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specifically designed to assess both the basic and the complex aspects of basic cognitive skills, to learn and be 
effective, both within the school environment and in everyday life. The tests are aimed at ascertaining the 
cognitive skills related to the disorders, generally diagnosed for the first time in childhood, and at assessing the 
skills required to succeed at school, thus allowing describing the child’s cognitive profile and delineating 
strengths and weaknesses. While employing it, the authors (Korkman, Kirk, & Kemp, 2007) found a particular 
sensitivity of NEPSY-II in assessing multiple pathological patterns, such as ADHD, Learning Disorders (in 
reading and calculation), Language Disorders, Autism Spectrum Disorders, Asperger Syndrome, Brain Damage 
of Traumatic Origin, Deafness and Hypoacusia, Emotional Disorders, as well as Mean-Degree Intellectual 
Disability. In particular, for this study, the linguistic and mnemonic NEPSY-II tests, specific for the age group 
between three and six years, were selected, such as: Immediate Drawings Memory (M3), Narrative Memory 
Recall (M6-REC), Narrative Memory Recognition (M6-Recognition), Comprehension of Instructions (LI), 
Speeded Naming (L3), and Phonological Processing (L4). 
Data Analysis 
In the presence of two or more categorical variables, the logic of interactions is applied; more specifically, 
if the effect of one variable changes when the other changes too, then we will have an interaction, being the 
effects of categorical variables defined by the differences between the means of the dependent variable in the 
groups defined by the independent. We can affirm that the interaction defines how the differences between 
groups defined by a variable change for the different groups defined by the other one. In particular, the 
interaction between categorical variables is very relevant, and often constitutes the most interesting effect in the 
study of factorial drawings, i.e., those research drawings that cross two or more independent categorical 
variables. The majority of experimental studies are based on factorial drawings, in which every single case 
belongs to a group, and the group consists of the crossing of the categories defined by the independent variables 
(factors). The primary advantage of this type of study is the possibility to investigate both the main effects of 
the independent categorical variables and their interactions. An experiment with more than one factor is called 
factorial. In repeated measurements drawings (RMD) there are several categorical independent variable factors, 
which define the cells of the drawing, and the effects of the factors are estimated by assessing the differences 
between the cell means defined by the factor levels (main effects), and by the combination of the levels of 
several factors (interaction effects). From the statistical point of view, the fact that each analysis unit expresses 
multiple scores makes every score correlated with each other, which makes the remaining ones 
non-independent. We can imagine repetitive drawings as drawings in which a series of measurements are made 
for different levels of independent variables, but in which the observations are grouped in clusters, i.e., using a 
criterion that makes the measurements made within each single cluster more similar than the ones made within 
different clusters. In classic repetitive measurements, clusters are generally the objects of the research, which 
means that the correlation between measures within the single analysis unit, or clusters, is an advantage, as it 
allows to better estimate the error of the statistical model used (Gallucci & Leone, 2016). 
The first two assumptions of the Two-Way Mixed ANOVA refer to the presence of a continuous dependent 
variable, from a “between-subjects” categorical factor composed of two or more levels. To better understand 
this logic underlying the analysis, we take into consideration the cells of the experimental drawing. First of all, 
let’s consider a simple case of a One-Way ANOVA for repeated measurements, looking at the effect of the 
“Time” factor on “Memory”, which we can diagrammatically represent in the following table: 

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COGNITIVE PROCESSES IN CHILDHOOD THROUGH BODY EXPERIENCE 
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Table 1 
Example1 
Time 
Pre Post 
M3 M3 
We can consider the two levels of the “within-subjects” factor, the time (i.e., “pre”, “post”), as two cells of 
a table that represents the study design. Therefore, let’s say that there are two cells of the drawing (in this 
example the values in the cells can be replaced with the mean values obtained on the memory dependent 
variable). Subsequently, if we include a second “between-subjects” factor, the so-called “treatment”, by 
including the conditions, we can modify the table as follows: 
Table 2 
Example 2 
Time 
Didactic type 
Pre 
Post 
Classic M3_C_PRE 
M3_C_POST 
Montessori M3_M_PRE 
M3_M_POST 
Unstructured M3_D_PRE M3_D_POST