Applied Cross-Cultural Psychology: Some ideas for a meaningful science

I just spent the last 72 hours in 3 different countries. Lots of random thoughts raced through my mind while spending time in small eateries, big airports and on roads wide and narrow. How can cross-cultural research contribute to the development and well-being of societies? What are the tools that psychologists interested in culture can use to inform politicians and political decision-making? How can we make cross-cultural relevant to everyday actions and events, considering the massive challenges that humanity faces through globalization, climate change and increasing interdependencies at a global level?


I think there are three different paths that may address these broad questions of policy relevance and societal development. For lack of better words, I will call them culturally sensitive understanding, culturally sensitive change and culturally sensitive evaluation of change. In other words: a) an examination of processes that are of societal importance and relevance, b) development and application of culturally sensitive change programs and c) a culture-sensitive evaluation of existing intervention programs so that the needs of communities are better met. Engaging with bigger questions and practical problems entailed in these three approaches can help sharpening our basic research questions and theories as well as contributing to understanding and managing global issues.

Culturally sensitive understanding of societal level problems


The first option is a focus on a better understanding of psychological processes related to important societal outcomes. There are many debates about how society can be made more humane, healthy and prosperous. What are the psychological processes that are associated with these outcomes? Here, the strength of cross-cultural psychology is the quasi-experimental nature of culture. Societies differ along a number of important outcomes and potential antecedents, cross-cultural psychologists can take these variabilities and study what variables are most likely implicated in the different outcomes across societies. An open, but critical mind about potential antecedents about potential contributing factors is important. Once certain variables have been identified as potentially important, more controlled experiments to test the causality may be conducted. Not all variables can be manipulated in experimental settings (just think of the difficulty of manipulating national histories or seasonal patterns). This option is probably closest to standard psychological research. The main difference is a closer alignment between scientific research topics and questions of practical and societal relevance. 


My own focus has been more along the multi-country, sociological level of inquiry. One example is the work by Seini O'Connor. Corruption and political transparency has been on the minds of politicians, philosophers and political scientists for millennia. One of the major unaddressed questions though is what variables might be implicated in changes of corruption levels over time. There are many theories and ideas of what makes societies more or less transparent. Seini's honours project addressed these ideas through an innovative longitudinal method and found some pretty surprising findings (see http://www.victoria.ac.nz/home/about/newspubs/news/ViewNews.aspx?id=4815&newslabel=, the actual study can be found here: http://jcc.sagepub.com/content/early/2011/06/08/0022022111402344.abstract).


Implementing culturally sensitive change programs


Second, cross-cultural psychologists can engage in developing and running culturally sensitive interventions that address practical problems. Psychologists interested in culture have been relatively successful in developing and running intercultural training programs. At the same time, programs that focus on developing and changing behaviours of individuals and groups have largely been left to general psychologists or other disciplines (e.g., developmental workers, economists, sociologists, political scientists). Only few programs have taken a culturally sensitive approach when trying to change behaviours (for a cool example, have a look at this project: https://blog.itu.dk/MOSP-F2010/files/2010/03/rkhaled_siggraph09.pdf). There is much scope for innovative and important work to be done.


Evaluating interventions in culturally sensitive ways


Third, cross-cultural psychologists could get involved more in assessing existing change programs as they are applied and implemented in diverse cultures around the world. For example, micro-crediting – that is the provision of small loans to individuals or groups - has been used in many disadvantaged communities to fight poverty and contribute to economic growth. Yet, we know relatively little about the effectiveness of these initiatives, especially about how they fit in with the larger cultural norms, beliefs and practices. One of the interesting studies in this regard was reported in a study in Science last year (http://www.sciencemag.org/content/332/6035/1278.abstract) . Karlan and colleagues demonstrated that micro-crediting in the Philippines led to down-sizing of enterprises and higher stress among recipients, which is contrary to common expectations about the effectiveness of micro-crediting. This study was conducted by economists who have little interest in examining the cultural (or even psychological) processes. Cross-cultural psychologists could significantly contribute to such research and help in evaluating programs so that they better meet the needs of the communities.

Tales from the field: the dawn of day 3

It is a refreshing morning, the birds are chirping in the trees, a gentle breeze is playing with the banana leaves and the village roosters are advertising the blood red sun over the sea. 

Today is the day that turned yesterday into a strange and nearly frustrating experience. The local world does not play by the rules of the minds of the Western educated, science-oriented aliens that descended upon this little island to study their strange customs. A pre-test a few days ago revealed that the main measure is likely to be contaminated – a beautiful word for saying that somebody had worked out what the main dependent measure of the field study was and is likely to have instructed people how to answer it. A major debacle for the motley crew of international researchers hoping to study a fascinating religious ritual, with the high hopes to help humanity understand why engaging in seemingly insane and dangerous things (think of getting pierced, walking 4 to 6 hours in the tropical heat to finish off the day with a nice stroll over some gentle burning fire – who in their right Western mind would want to do something like this?). 

However, the one thing that should have sealed the study, the brilliantly devised and simple variable to measure how truly connected people feel to their religion and their religious fellows may not work anymore. The frustration turned into a heated debate about behavioural economics, a field of science that most villagers probably will never encounter in their whole life. Hours passed debating the pros and cons of games with the appealing names like dictator or prisoner dilemma game. 

It is fascinating to see the research work and weeks of preparation descend into an abyss of confusion, personal convictions, Western bias and scientific despair. One thing that I am wondering is, we don’t understand what these economic games are measuring with well-educated Western participants, despite nearly a century of research. What will it show us in a group that has problems understanding our humble attempts to ask them ‘how do you feel right now’? It makes me wonder how some famous studies published (like the famous series of studies by Joseph Henrich and others, see http://www.sciencemag.org/content/327/5972/1480.abstract) managed to explain complex games that take a page to describe in their widely cited publications to nomadic hunter and gatherer groups in the African bush. The appeal of our measure was its elegant simplicity and meaningfulness in a local community context. Yet, it might have been too easy and too transparent for the smart minds of some local people.

Now it is the dawn of day 3. A new day and a gentle breeze that calms the jetlag and insomnia. The debate was settled in the end late last night over some dinner and beer, we are going to use a similarly simple design, focusing on an unknown local entity, a potential Mead’esque faux pax, but the best that can be done within the time constraints of the study and better than other measures. It will be an exciting study nonetheless. 

The meeting last night hammering out the details, nine curious minds bent on making it work, 70 heart rate monitors to be connected to people participating in the ritual, a pre-post design with control groups and a multi-method design to study a fascinating ritual. And best of all – despite over 12 hours of tormenting debates and tiring preparations – the sun is shining, it is nice and warm and the sea is just meters away. 

And most importantly, it will be a fascinating day following new won local friends in their religious quests. The true beauty of field work. 

How to do Procrustean Factor Rotation with more than 2 groups

Today, I am continuing the torture with a bit more detail on options for comparing factor loadings across three or more groups within SPSS. This is a crucial issue for cross-cultural research and is becoming increasingly important, because researchers start studying more than two groups. More complex designs are more powerful in uncovering processes that can explain emerging behavioural differences, so this research should be strongly encouraged!

Aim: Compare the factor structure when you have more than two cultural groups, get an estimate of factor similarity

Why are we concerned with Procrustean Rotation? Factor rotation is arbitrary, therefore apparently dissimilar factor structures might be more similar than we think; procrustean rotation is necessary to judge structural and metric equivalence

Statistical Procedure:

The same syntax as for the two group case (see previous post: http://culturemindspace.blogspot.co.nz/2012/03/how-to-do-procrustean-factor-rotation.html) can be run with SPSS, but the greater number of countries adds additional problems. You have various options:

1. Run all pairwise comparisons. However, this will lead to a substantive number of comparisons (especially if you have many samples). This also leads to a number of statistical problems (remember family-wise error rate and increased Type I errors)
2. Select one country as your target group. For example, if an instrument was developed in the US, you may want to compare each group to the US.
3. Compute the average correlation matrix and use it for your factor analysis. The average is sometimes called pooled-within matrix. Therefore, you would compare each sample with the average structure across all samples (this can be done via discriminant function analysis in SPSS, you can then read the resulting correlation matrix into spss and use as an input for your factor analysis - see my discussion of how to do this here). This is highly appealing if you have many samples. This procedure of computing the average correlation matrix as input to the factor analysis can be simplified if (a) you have samples with similar sample size (no sample is dominating others; eg., if you have one sample of 10,000 and three samples of 50 participants each, the large sample is driving the factor structure) and (b) you mean centre each item within each sample prior to the overall factor analysis. This is necessary to account for any group mean differences that might obscure relationships if the samples are pooled. See below for a graphical explanation of why this might be a problem. As you can see, the relationship within each sample is negative, more sleep problems within each sample are associated with less laughter by participants. However, one group is consistently higher, for both the reported sleep problems as well as laughing. There may be reasons of why this is the case (I will come back to this example when talking about multilevel analysis), but for our analysis, combining the two samples would mean that we have a positive relationship across both samples combined (compared to negative relationships within both samples separately). This effect is due to the mean differences across both groups (I will post something soon on the beautiful complexity of these multi-level problems in psychology - very fascinating stuff). As a consequence of this confounding of group differences with individual differences, we need to take any such mean differences into account before we can combine the samples. This can easily be done using the z-transformation option in SPSS (‘Save standardized values as variables’ under the ‘Analysis’ -> ‘Descriptives’ option). 

I believe the last option is the most appealing with large data sets.

 However, cross-cultural psych never stops to be complicated. What happens if you find that some samples show good factor congruence with the average factor structure and others not? Ideally, you would exclude those samples from the average factor structure and re-run the analysis. Proceed iteratively till no sample shows any problems with factor similarity anymore.

If you have lots of cultural samples, you are really curious (and stats savvy) and want to find out what is happening in the strange worlds of culture, you may want to run cluster analysis on the congruence coefficients to identify clusters of samples that show greater similarity with each other. This might provide some interesting insights from a cross-cultural perspective. However, it is computationally demanding and relies on purely statistical criteria. There is a neat paper discussing various options and strategies, written by Welkenhuysen-Gybels and van de Vijver (2001, published in the Proceedings of the Annual Meeting of the American Statistical Association – I think this gives you an idea about what level of analysis we are talking about[1]). You can also download a SAS macro (the link is in the paper) that does much of the computational work for you. I have never worked with SAS, it seems a parallel universe to me and I am fascinated, but scared of it. But there are people who think it is easy. Conceptually, it is a nice tool.  

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[1] You can download the paper at: http://www.amstat.org/sections/srms/Proceedings/y2001/Proceed/00106.pdf