To carry out this factor analysis, you will need to use SPSS or a similar statistical package.
- Open the dataset in SPSS. Make sure that the combined weight is on.
- Choose ‘Analyze’ from the menu, and proceed with ‘Data Reduction’ – ‘Factor’.
- Put the indicators from the table below into the frame called ‘Variables’.
Table 2.1. Items to be included in the factor analysis in exercise 3 Variable labels Variable names How satisfied with life as a whole STFLIFE How happy are you HAPPY How often socially meet with friends, relatives or colleagues SCLMEET Anyone to discuss intimate and personal matters with INMDISC Take part in social activities compared to others of same age SCLACT Subjective general health HEALTH Always optimistic about my future OPTFTR In general feel very positive about myself PSTVMS At times feel as if I am a failure FLRMS On the whole life is close to how I would like it to be LFCLLK Felt depressed, how often past week FLTDPR Felt everything did as effort, how often past week FLTEEFF Sleep was restless, how often past week SLPRL Were happy, how often past week WRHPP Felt lonely, how often past week FLTLNL Enjoyed life, how often past week ENJLF Felt sad, how often past week FLTSD Could not get going, how often past week CLDGNG Had lot of energy, how often past week ENRGLOT Felt anxious, how often past week FLTANX Felt tired, how often past week FLTTRD Absorbed in doing, how often past week ABSDDNG Felt calm and peaceful, how often past week FLTPCFL Felt bored, how often past week FLTBRD Felt rested when woke up in morning, how often past week FLTRSTM Free to decide how to live my life DCLVLF Seldom time to do things I really enjoy ENJSTM Little chance to show how capable I am LCHSHCP Feel accomplishment from what I do ACCDNG Like planning and preparing for future PLPRFTR When things go wrong in my life it takes a long time to get back to normal WRBKNRM My life involves a lot of physical activity PACTLOT Satisfied with how life turned out so far STFLFSF Satisfied with standard of living STFSDLV How much of the time spent with immediate family is enjoyable FMLENJ How much of the time spent with immediate family is stressful FMLSTRS Chance to learn new things CHLRNNW Feel people in local area help one another PPLAHLP Feel people treat you with respect TRTRSP Feel people treat you unfairly TRTUNF Feel you get the recognition you deserve for what you do RCNDSRV Feel what I do in life is valuable and worthwhile DNGVAL There are people in my life who care about me PPLLFCR Feel close to the people in local area FLCLPLA
- Setting the parameters:
- Click ‘Extraction method’, select the maximum likelihood method, including all factors with eigenvalues of 1 or over. Click ‘Continue’.
- Click ‘Rotation’ and select varimax rotation. . Select ‘Loading plot(s)’. Click ‘Continue’.
- Click ‘Options’, select ‘Suppress absolute values less than’ and set the value 0.3. This will suppress all factor loadings below 0.3 from the output.
If you are getting stuck, you might want to use the SPSS syntax here.
Interpreting the results
You should get 11 factors with eigenvalues above 1. Note that the first factor contains 21.4% of the variance . This means that over one fifth of the variance in all data is explained by a single factor. This is a lot, but it does mean that, by including the 44 questions we do here, we are getting almost five times as much information about well-being as we would just looking at a single factor.
Have a look at how different questions have loaded onto different factors. Attempt to summarise what each of the 11 factors captures, in terms of the concepts tapped by the questions loading on it. For example, you might broadly label the second factor as evaluative because it includes questions that ask people to assess their life as a whole, and doesn’t directly ask about their emotions - whether they feel ‘happy’ or ‘sad’. If you can’t think of anything, then click here for some suggestions.
|4||Sense of community|
|5||Family and community|
|6||Optimism and self-esteem|
|9||Autonomy and competence|
This pattern of results does seem promising, and might lead one to feel that something similar to the framework shown in Figure 2.1 has been confirmed, and that one should use these 10 factors to define well-being. However, digging deeper reveals that things are not that simple. Another factor has been conflated1 with the conceptual dimension – that is to do with question format and response codes.
It is well recognised that question formats and response codes can lead to patterns in results. Some individuals are more likely to agree to statements, whatever they are - others are more likely to always ‘neither agree nor disagree’. Some individuals use the extremes of a scale when offered the choice, otherwise tend to stick to somewhere around the mid-points. Question ordering is also known to play an important part, with people’s responses to one question dependent on their responses to previous questions. Let’s see how these effects may have played a role in the factor structure generated above.
Take, for example, the first factor, which we have labelled ‘negative affect’. With one exception, all the questions loading high onto this factor ask respondents to report how often they have felt different ways in the past week. Furthermore, all the questions are negatively worded - that means that saying you felt a certain way ‘often’ means you had low well-being. Importantly, and this is where the factor structure seems to follow more the structure of the questions than their content, no distinction emerges between the various types of feelings identified in the survey - between those that are more about having energy, those that are about sadness or depression, and those that are about stimulation. These are all quite different concepts, and you would expect them to load onto different factors if the factor structure was based on the actual content of the questions rather than their format. However, all having the same question format and response codes, and all appearing in a group together in the survey, responses to one of these questions tends to correlate to responses to all the others.
What about the ‘positive affect’ questions? The good news here is that the two questions on how often respondents enjoy life or feel happy (factor 7) do indeed separate from the more vitality-type questions (factor 8) - there is some evidence then for factors being driven by concepts rather than merely question format. However, the problem here is an absence: As well as the question on how often someone feels happy, there is also a question simply asking them how happy they are (on a scale of 0-10). Of course these are slightly different questions, but one would expect them to both load onto the same factor. They don’t - the ‘how happy are you’ question instead loads most with the evaluative questions (factor 2), which incidentally include three questions that use the same 0-10 scale.
Another example to note is factor 9. This actually includes just two questions, which don’t really have much in common in terms of content - one is about having free time/autonomy, the other is about competence. Other questions on autonomy and competence in the question set load elsewhere. So why do these particular two load together? Perhaps it’s the phrasing of the questions (which are both to be responded to on Likert scales):
- In my daily life, I seldom have time to do the things I really enjoy.
- In my daily life, I get very little chance to show how capable I am.
In conclusion: The factor structure does appear to identify certain conceptual groupings. However it appears to also be governed by non-interesting determinants such as question format and response codes. The two interact, sometimes contradicting each other and sometimes conflating each other, meaning that, for some conceptually-driven factors apparently emerging, it is hard to confirm that they indeed constitute confirmation of conceptual groups. Given this situation, it may be prudent to abandon any attempts to use a purely data-driven approach to grouping questions, and rely instead more on conceptual approaches.
Exercise 3 reveals that, whilst at first sight it might appear that the ESS has a factor structure which could be used as a framework for accounts of well-being, this structure seems more likely to be the product of question format than similarities in substantive meaning. As a result, we are forced to discard the purely bottom-up approach for determining our framework. Furthermore, factor analysis relies on an assumption that items that should be grouped together are likely to correlate more. This may be true in some cases, but it is not necessary in the case of well-being. For example, consider questions on family and friends. It makes sense to group them together under an overall heading such as social well-being, or supportive relationships. However, there are many reasons why the quality of family life need not correlate with the quality of relationships with friends. Indeed, the opposite may be more likely, as different people or cultures satisfy the need for supportive relationships in different ways.
-  When two factors are conflated, this means that it is hard to separate their roles in terms of explaining results. For example, imagine there was evidence to suggest that lawyers were happier than hairdressers (in fact the opposite is true!). A researcher might assume that this is because they have a higher income. However, they would be conflating income with (at least) one other variable - for example levels of education. In this context it would be hard to separate out the effects of the two different variables, as well as many others.