GUIA DE TRANSACCIONES U OPERACIONES INUSUALES O SOSPECHOSAS

A standard statistical package used for Q methodology studies, PQMethod (Schmolck, 2002), was used to process the data. Th e sorted statements were entered into the program in three separate sets – one for each country. In addition, a combined set of all respondents was created and also processed. PQMethod employs computational methods and default procedures which are widely tested and accepted by the academic community familiar with Q methodology. In this particular case, a PCA factor extraction routine was used in a combination with a Varimax rotation to maximize the diff erences between the factors.

Th e most important data outcomes can be found in the subsequent chap- ters (see list of tables and fi gures). Further, I will explain the kind of outcomes and how they were employed in the overall interpretation of the results.

Fa ctors are groups of respondents with similar views

The meaning of a factor is, simply put, a group of respondents (in this study a group of teachers) who adhere to approximately the same pattern of sorting. This means that the respondents, and not the statements, are the variables, contrary to conventional studies, where factor analysis groups statements in clusters. A factor is thus represented by an ideal type of sort- ing pattern. The respondents are grouped according to the extent to which they have sorted their statements in a pattern similar to the ideal one for this particular factor. Typically, a respondent is ‘loading’ on a factor if their pattern of sorting is at least 50% similar to the ‘ideal’ one, represented in this factor. All sets of ‘ideal’ rankings are presented in the respective chap- ters (see list of tables and figures).

Th ere are also confounding respondents, people who belong to more than one group. Th eir views are also interesting to take into account, as they often off er unique meta-refl ective perspectives and/or explicate the common grounds between the diff erent factors.

Th us, the number of factors indicates the number of distinct views revealed within the sample. Q methodology has no claims on extending the fi ndings out- side the sample, so we cannot make any inferences regarding the total number of possible views existing in a general population. In our case, say, if all teachers in

Bulgaria were included, there might be other types of views revealed. Even more, sometimes the absence of a particular expected type of view may pose questions worth exploring in further research.

It is important to keep in mind that no claims on representation can be made, particularly no claims based on respondents’ attributes such as gender, age, experience. Sometimes these attributes (e.g. young) are used to explain a particular respondent’s position, but in no case this would imply that, say, all young teachers in the country express similar views.

As it is indicated in Figure 9, four sets of factors are presented and analyzed in the following chapters: fi ve factors for Bulgaria, four each for Croatia and the Netherlands, and fi ve for all the respondents from the three countries combined. Th ese results will be discussed in the next four chapters and are mentioned here to make the further steps in the analysis easier to follow.

Co rrelation between factors

Th e correlation between factors indicates the degree of similarity between the fac- tors. A high correlation would mean greater similarity. A low correlation means that the views presented are considerably distinct from each other. Further analy- sis of the data is employed to indicate the areas in which the factors are similar or diff erent from each other. Th e statistical diff erence is indicative only and has to be substantiated by the qualitative material.

Co nsensus statements and distinguishing statements

Th e PQMethod package generates an output with includes, besides the fac- tor extractions, a list of consensus and distinguishing statements per fac- tor. Consensus statements are the ones that are ranked in a similar fashion by all factors. Th is could be a positive consensus, i.e. all factors approving (strongly) of a certain statement; a negative consensus, i.e. a (strong) rejec- tion of certain statements, and consensus on items ranked as neutral. When the qualitative data is analyzed, it becomes clear, again, that the numbers do not tell us everything. It so happens that some statements are accepted or rejected on very diff erent grounds, so what looks like consensus reveals diff erence in opinion, after all. Also, in the ‘neutral’ category, it turns out that some statements are really considered uninteresting and other are seen as ‘taken for granted’, ‘uncontested’ and therefore not worth discussing. All these nuances are taken into account in the description of the factor profi les in the next chapter, as explained in more detail below.

Th e distinguishing statements are statements that are unique for a particular factor. Th ey are not to be mistaken with ‘typical statements:’ they are not neces-

sarily the ones that constitute the core narrative of a factor. In Q methodology, the overall pattern of ranking is important, and thus the statements in relation to each other: no particular statements are singled out as ‘typical’ for a factor. Statement rankings per factor

In Figure 9 (fl owchart) at the beginning of the chapter, the next piece of quantitative data analysis output are the standardized ranking scores per fac- tor, which are used to construct the ‘ideal’ factor sortings, as explained above. Th e ideal sortings are used as a guideline in the further steps in data analysis. Th ey should be considered only indicative, due to the obvious limitations of assigning average ‘scores’ of eigenvalues (unique to each ranked statement) back to ranking positions (further in the data output these are indicated as ‘normalized factor scores’).