Coding and qualitative analysis

The interviews that form the data basis of this study have made for very rich material. This depth allows for a back and forth between condensing the information provided about an organisation into a numerical position, as I will do by way of coding on several axes, and a more detailed discussion of the actual reasoning and the organisation’s understanding of the issue. This allows me to combine the more systematic presentation of how the organisations relate to each other in a policy space with a more qualitative analysis of the logic of the organisation as perceived by the interviewee. The analysis will make clear not only where the organisations stand with regard to a policy issue, but also how close or distant they are from each other.

This type of analysis also offers a chance to incorporate details on the background of opposition or support for a policy issue as they may play out from the point of view of the organisation’s members. The thematic coding (see below) sorted the data into thematic units (Meuser and Nagel 1997: 488f), so that for the qualitative analysis, all statements pertaining to one aspect could be seen together. The organisations’ representatives may not be actual proxies for what their members think, but unlike survey data, the qualitative interviews leave room for narratives and elaboration. They can support – or challenge – the results of the systematic coding and offer insights into the kind of policy changes that may be more or less desirable to the organisations’ respective clientele.

For this analysis, the interviews were thematically coded in MaxQDA 10, meaning that all sections or statements relating to the retirement age were coded as such and then available at a click. This stage of coding organises the data around thematic nodes and already gives the researcher an overview of which themes and ideas are present in the discourse of these organisations.

Coding adapted a scheme used by Häusermann (2010) in her study of unexpected institutional change in continental welfare systems. The interview segments were coded numerically on a 0 to 2 scale, both for support/opposition of a reform item as well as for its relevance. This is a fairly simple coding scheme, which on the face of it provides less nuances than a 4-point or 5-point scale, but makes for relatively easy and clear coding decisions. The nuances of the organisations’ positions are still reflected in the final scores because qualifying statements expressed in other parts of the interview will also be coded and will act as a counterweight if they are in contradiction with the general position.

For example, in the coding on the retirement age, 2 indicates active support for a raised retirement age, whereas 0 indicates strong opposition against the higher retirement age. Statements that either expressed very conditional support, neutrality, or only mild disapproval were coded as 1. For instance, if an interviewee declares, “We are against the higher retirement age,” this will be coded as 0. However, if at another points in the interview she expresses that her organisation was, for example, willing to trade on the retirement age in favour of other policy issues, this would be coded as 1. The overall score for that organisation would thus be higher, reflecting less stringent opposition, than for an

organisation that only expresses opposition throughout. The more caveats are given to the main position, the more the overall score will be affected; the more the interviewee stresses the main position, the heavier this will be reflected in the final score.

The following two interview segments may serve as an example.

Well, I think the first thing to say is that Tim Jones and his team at NEST have done a brilliant job of building what they've built. They've delivered exactly what the government asked for, on time and probably within budget. And I have a huge respect for what they've done. And I think on balance it's probably necessary, we need auto-enrolment to succeed. To make it succeed, we need NEST. Will auto-enrolment succeed? I've got some worries about it, but nothing to do with NEST, it's to do with the way we've gone about auto- enrolment, it's to do with the damage to the pension brand in the UK. And it's to do with affordability on the part of the employee... But in terms of NEST, yeah, they've done a great job...

Coded as: Support 2

But one of my frustrations in auto-enrolment is that we're proposing to auto-enrol people into an architecture which when they understand it they just go "Uh, no, I don't want anything to do with that, it's just too complex, it's too rigid" and we can see that people are already making decisions about where they put their retirement savings because we have got over 350 billion of assets now in individual savings accounts.

Coded as: Support 1

The interview these segments are from contained 10 segments in total that addressed the issue of auto-enrolment. After the thematic coding, those segments all show up in a thematic cluster. The example above is tidied up for grammatical errors, but the complexity of the statements is maintained. I coded each of the thematically sorted segments according to their central point. In the first example segment above, I weighed the sentence “And I think on balance it's probably necessary, we need auto-enrolment to succeed” and the theme of “worry” over auto-enrolment’s success as an indication of clear support, and coded the segment with 2. In the second segment, the criticism of the details of the implementation of auto-enrolment is the most dominant, and I have thus coded it as 1: in this case, a “yes, but”.

In the example of this organisation, the ten text segments were all coded in the same manner, and the results were averaged; the organisation these segments pertained to averaged out as a 1.4 support score.

Therefore, while the basic coding method breaks down nuance into very simple categories, overall nuance is retained because of the inclusion of all statements pertinent to the reform item. The final scores are then qualitatively checked against the interview, at which point any gross miscoding would also become noticeable. The importance and strength of a qualitative discussion also becomes clear in the example above: caveats that lower an organisation’s support score can have different reasons behind them. Only the qualitative discussion can, for instance, clarify if a 1.4 score of qualified support is due to doubt about the principle or doubt about the execution of a reform.

Similar coding has been done on the issue of relevance. While organisations may hold supposedly strong views for or against a political measure, if they assign a low priority to it, one would assume that they are less likely to go to great lengths in terms of lobbying work and political pressure. Combining the stated position on the issue with relevance therefore gives a more comprehensive view on where the organisation stands.

The degree of relevance was coded with the same approach to nuance in the coding of the support/opposition question statements. Expressing that the retirement age was an active issue of lobbying for an organisation were coded with 2; statements expressing that the retirement age did not play a significant role for the organisation were coded with 0. Segments where interviewees indicated that there was a gap between their organisation’s official position and what they are really lobbying on were coded with 1, so as in the case of support/opposition, statements like these would lower or raise the overall score.

All coding scores were averaged and the results rendered in multidimensional charts, making visible which organisations – or types of organisations – have similar positions on the reform item in question.