In addition to entering data and navigating through the corpus, Puck allows not only to explore the kinship environment of individuals, but also to run structural socio-centered analysis. The Analysis menu contains several tools whose functions concern such kind of analysis.
Pedigree and Progeniture
The commands Analysis > Pedigree and Analysis > Progeniture produce a complete list of Ego’s ascendants or descendants, up to a given degree. After executing each one of these commands, a dialog window opens asking to specify the maximal generational depth of the ascent/descent ties you're looking for. This will be done by entering a single number that indicates the generational limit of your search. For instance, entering “3” produces a tree structured report of Ego's known ascendants/descendants up to great-grand-parents/children.
The command Analysis > Relatives enables to obtain a complete list of Ego’s relatives of a given type. This is done by entering in the dialog window a structure formula in positional notation, just as in the case of a matrimonial or relational census (see infra). Note, however, that the present function is ego-centered : the first individual in the formula is the currently selected individual.
For instance, entering “XX(X)XX” will produce the list of all Ego's cousins (with their names, identity numbers, and exact kinship relation types).
The command Analysis > Kinship Chains enables to obtain an exhaustive list of the kinship chains connecting Ego to another individual. This is done by entering two numbers, where the first one is alter’s identity number, and the second the canonical degree (maximal genealogical depth). A third number can be entered which specifies the maximal order of the chain, that is the maximal number of marriages it may contain. Puck lists all tracks between ego and alter within specified bounds in the classification of your choice.
The command Analysis > Distances allows classifying ego's relatives depending on the genealogical distance existing between them. When executing the command, a dialog window opens asking to specify which kind of ego's relatives are to be taken into account. You can do so by selecting the wanted Filiation Type in the check-box list ; and than specify the the upper limit of your search into the Max Distance field.
When launching the count, PUCK automatically introduces a new attribute to all individuals. The attribute Label will be, i.e., "DIST 1" if ego Id number is "1", and its value will correspond to the genealogical distance between ego to alter (which is the minimal number of arcs connecting them).
The command Analysis > Basic information (CTRL+B) gives access to the basic information of a dataset, which is the starting point for the analysis of its structure.
It produces a report that contains many basic information such as :
- The number of individuals (differentiated by gender : men/women/unknown)
- The number of marriage relations and unions (differentiated by gender)
- The number of parent-child relations
- The number of fertile marriages (couples with children), in absolute terms and as a percentage of total marriages
- The number of co-spouse relations (relations between co-wives and between co-husbands).
- The number of components (maximal connected subnetworks), the size of the largest component, which is useful to evaluate the dataset cohesion (or disintegration).
- The mean share (size divided by total network size) of agnatic/uterine components and the share of the largest agnatic/uterine component, and the percentage of marriages involving a member of the largest agnatic/uterine component.
- Elementary Cycles : The cyclomatic number (number of independent cycles) of the network.
- The Density of the kinship network : The number of marriage and filial relations divided by the total number of possible relations between two different individuals.
- Maximal and Mean Depth : the mean genealogical depth is computed as an average of the mean generational depth of each individual’s pedigree, according to the formula of Cazes (Cazes & Cazes, 1996).
- Mean number of spouses (differentiated by gender).
- Mean fratry size (mean number of cognatic, agnatic and uterine groups of siblings).
After identifying the errors affecting the dataset, the researcher should take into account its limits and biases. This can be done by exploring the network morphology and it should be seen as a precondition for any analysis or matrimonial census. In order to do so, Puck offers a wide range of tools, accessible from the command : Analysis > Statistics (CTRL + G). When executing the command, the Statistics Input Window automatically opens. This is a fundamental tool for the dataset diagnostics, which constitute one of PUCK main functions. A specific section of this guide is thus dedicated to it. To move there, you can click here or use the "Functionalities" menu.
Note : the use of this command presume a basic knowledge of the partitioning process. To read more about partitioning, see here.
The command Analysis > Partition Statistics allows producing statistics concerning the distribution of given partitions on the dataset. When executing the command, a dialog window opens asking to set the Partitions Statistics Input criteria. This can be done, firstly, in the Partition Diagrams Criteria frame. Here you can set more than one partition criterion at the time. ***GAP Secondly, the Split Partition Criteria frame allows [...].
After launching the count, PUCK shows the results in a new tab, both as diagrams and as tables. In the diagrams, the abscissa indicates the size (number of members) of each resulting partition ; the ordinate indicates the number of existing partitions of each size.
One of the most important PUCK functions consists in running a circuit census of your kinship network. This can be done by executing the command Analysis > Circuit Census... (Ctrl+H), which automatically opens the dialog window called Census Reporter Inputs. A number of settings can be chosen from it, following your analysis needs.
A specific section of this guide is dedicated to the use of the Census Reporter Inputs. To move there, you can click here or use the "Functionalities" menu of this guide.
The command Analysis > Differential census allows conducting a segment-based census and provides several comparative means, which especially concern the relations between members of identical segments. The significant advantage over a global census is the possibility to consider several segments separately. By applying a differential census to all the dataset’s clusters configured by a certain segmentation, statistic results can be achieved concerning for instance uterine-agnatic relations within a household or kinship relations within a profession. The results of a Differential Census appear both as diagrams and as tables. A Differential census produces : relational statistics for each cluster ; global and mean percentages of relations ; distribution of relation percentages by cluster size.
In the diagrams, the abscissa orders the selected partitioning criterion (i.e., occupation) and the ordinate [...]