Thursday, February 11, 2016

Culture & Psychology Summer School in Nagoya, Japan

We are super-excited to announce all the details for the next IACCP Culture & Psychology Summer School. The Culture & Psychology School is open to students at PhD and MSc level and is sponsored by the International Association for Cross-Cultural Psychology (IACCP). The goal is to provide specialized training by experts in topics of importance and relevance for studying psychology and culture in context.  In addition to its educational benefits, the programme is designed to facilitate cross-cultural contact and understanding among future academic leaders and to broaden their academic vision.  We really look forward in bringing bright minds from all corners of the world together and help them develop new research ideas and collaborations. The Culture & Psychology School is run in association with the 23rd IACCP conference to take place in Nagoya, Japan. 
A project presentation during the 2012 Stellenbosch Culture & Psychology School

There have been numerous changes based on the feedback and suggestions that we received after the last one in Reims, France. We are super-excited about the line-up and new programme. Here is an overview of the new programme, the stream leaders and content.

The new programme


We have received a lot of feedback and we have remodeled how we plan to run it. The major difference this year is that we have more methods. You will be able to choose both a content stream and a method stream. On the first day after some introduction and overview, you will be working with people in your content stream. On the second day, you can then choose one of three method streams and work hands-on under the guidance of experts. On the third day, you rejoin your content stream and you will integrate your new methods learned to your content area of study.

To make this work, we will expect that you do some prep work before coming to Nagoya. The stream leaders will provide some reading lists and tasks for you to complete before you arrive in Nagoya to get everyone up to speed with basics. Think of it as a mini-online course to help you get familiar with some of the material to make the most of your learning experience. We will facilitate this as best as possible and we are confident that you will enjoy this opportunity to interact online with your stream colleagues.  

Here are the stream leaders and their content and methods sections:

Cristine Legare


Cristine is a cognitive scientist who studies the ontogeny of cultural learning. She examines the interplay of the universal human mind and the variations of human culture to address questions about cognitive and cultural evolution. Her research and training reflect her commitment to an interdisciplinary approach to the study of cognitive development. Cristine draws on insights from cognitive, cultural, developmental, educational, and evolutionary psychology as well as cognitive and evolutionary anthropology and philosophy, with the aim of facilitating cross-fertilization within and across these disciplines. Her website can be found here



Content Stream: Cognition in Cultural Context


Human cross-cultural variation is unique among all animals in both its extent and structural complexity. Cultural variability is one of our species’ most distinctive features, yet the vast majority of psychological research continues to examine a population that is unrepresentative of human culture globally and historically—those from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) backgrounds. Cristine proposes that cultural diversity is inextricably tied to childhood. The human capacity for cultural variability within and between groups must be ontologically prepared by a set of characteristics that enable, structure, and stabilize group-specific cultural information much beyond anything that has been observed in other primates. She will discuss how the capacity to learn, create, and transmit culture increases our understanding of the cognitive and cultural evolution of our species. Cristine will describe how my experimental and ethnographic research integrates theory and methodology from cognitive and evolutionary anthropology, psychology, and philosophy to examine the co-construction of cognition and culture. She will also provide an overview of research conducted at field sites in southern Africa, the U.S., Brazil, and Vanuatu (a Melanesian archipelago).

Methods Stream: From Ethnography to Experiments and Back Again


Cristine conducts mixed-methodological, cross-cultural research to examine cognition in context. She will discuss how she “mines ethnography” to inform her experimental research and the ways in which experimental research can be used to test hypotheses about the cognitive psychological underpinnings of cultural beliefs and practices. Cristine will also discuss best practice for elevating the state-of-the-science in cross-cultural research as well as strategies for publishing interdisciplinary research.

Matt Easterbrook


Matt is part of the Social and Applied Psychology Research Group at the University of Sussex, UK, where he researches and lectures on the psychology of inequality.  His research investigates how selves and identities are influenced by different social structures, cultural orientations, and group memberships, and the consequences of these things for personal well-being, trust, motivation, and socio-political outcomes.  His research often uses multilevel and longitudinal study designs and advanced statistical analyses to investigate these issues. His website is here.



Content stream: The self and social inequality 


Against a backdrop of unprecedented and rising levels of inequality across the world, this stream will cover contemporary social psychological theories of inequality and social class.  We will begin by reviewing the broad consequences of inequality for nations and individuals, before discussing the pivotal role of the self as the explanatory nexus between structural inequality and individual characteristics.

Method stream: Multilevel modelling


This stream will begin with a discussion of the research designs that give rise to multilevel data, and why multilevel modelling of nested data is important and useful.  We will then cover how to manage and set up multilevel data in SPSS, and how to import, run and understand different multilevel models using HLM.  

Nicolas Geeraert


Nicolas is a senior lecturer in Psychology at the University of Essex (UK). He trained as a social psychologist (PhD, 2004, Louvain-la-Neuve, Belgium) looking at stereotypes and attribution theory. His current research interests are in cross-cultural psychology and acculturation. He has extensive experience in conducting longitudinal projects which he has used in a number of acculturation projects. His website is here.

This is Nicolas one or two weeks ago ;)


Content stream: Acculturation & intercultural contact


Intercultural contact leads to challenges and changes. This stream will explore how acculturation unfolds as a process requiring the acculturating individual to copes with cultural stressors and cognitively organize their heritage and settlement cultures. We will discuss how acculturation takes place within the ecological context of families, institutions, and society.

Methods stream: Longitudinal methods


Longitudinal research is an increasingly popular tool for cross-cultural researchers. This workshop will explore the strengths and advantages of longitudinal research and how to practical set-up a longitudinal study in terms of design, participant management, data preparation, etc. Different methods to analyse longitudinal data will also be discussed. 


Endorsement by Previous Participants


Ceren Gunsoy


I attended the IACCP summer school in Reims, France, as a third year PhD student at Iowa State University. It was a great experience! Not only the topic stream that I was part of but also the talks, discussions, and social activities were very informative, thought-provoking, and fun. On top of that I met great people and am still in touch with them. I strongly recommend this program!




Colin Scott


The 2014 Summer School in Reims, France, was a fantastic opportunity to connect with researchers interested in a range of topics in cross-cultural psychology.  Seminars with leaders in the field offered a hands-on opportunity to build new collaborations while getting critical and constructive feedback from faculty and students on our own work.


Costs


The cost for the summer school will be 200 Euro for participants from high-income countries (as per IACCP fee structure) and 150 Euro for participants from low income countries. The fee includes accommodation, welcome dinner, lunches and coffee breaks. This is pretty damn good value for a three full day workshop with world leaders in the field of psychology and culture, providing you with cutting edge skills and material. 

The Schedule


March 20: Deadline for applications
April 3: Decisions on applications
April 4: Work in online study groups commences
June 20: Submitting initial research ideas to stream leaders

July 26- 30: Culture & Psychology School in Nagoya Hill
July 26 evening – arrival at Nagoya hill station, general welcome and get to know each other
July 27– Introduction & work in content streams
July 28– work in methods streams
July 29 morning – we bring content and methods back together, discussion of research ideas and plans
July 29 afternoon – sharing experiences by stream leaders on how to publish cultural research
July 30 – transfer to Nagoya.

July 30 – Aug 3: IACCP conference in Nagoya

Accommodation & Logistics


The school will take place in a mountain retreat two hours away from Nagoya. It will be a fascinating cultural experience since we are going to be in a small community off the beaten track in a more traditional Japanese environment. All sleeping places are shared and we will sleep on futons. Please bring your own toiletries and towels be prepared to share a communal space. There will be NO towels available and only shared shower facilities. It is a traditional setting and we only have one hour per day in the evening when you can take a shower. I realize that this may be unusual or inconvenient, but I really hope you will be able to use this as a cultural learning experience and enjoy this cultural challenge. 

We have your dietary requirements and will try our best to accommodate them. The food will be in traditional Bento style. Feel free to buy additional food of your preference on the way, there is some (limited) refrigerator space where it can be stored. Here is the link to the place (use google translate to get it in English).

Application and Further Info


The application form is now available. Check here for any updates. A poster to share with colleagues and friends is herePrint it and spread the word! If you have any questions about the programme, the stream leaders or the general procedure, please do not hesitate to contact me or Yasin.  

Happy to answer your questions and look forward to seeing you all in Nagoya in a few months!


Monday, March 2, 2015

A gentle intro to cross-cultural equivalence - or how can we measure across cultures?

Psychology is the study of human behaviour and mental processes through scientific methods. The claim of psychology is often to be universal, that is applicable to all of humanity. Using scientific methods, we psychologists rely on a systematic and objective process of proposing and testing hypotheses and making predictions about the state of human nature.  Ever since the beginning of psychology as an academic discipline, the scientific quest to quantify natural occurrences to better understand and predict them in the future became one of the ultimate goals. Of course, this requires often extensive qualitative research, but ultimately the hope was and is that we can understand a behaviour or mental process so precisely that we can quantitatively measure it and also change it.



The application of such quantitative methods are now often taken for granted, even though the levels of quantification may vary. For example, we may want to select the most able person for a particular job, refer a child with learning problems to a specialist or we may wish to help a person with mental health problems to fully function in society again. Even though all these problems can be phrased in qualitative terms (a good person for the job, a child that has problems learning, a person who is not well), these are essentially quantitative problems because they always have some reference to implicit or explicit standards. A person might be BETTER qualified than another to take up a job or a person may have GREATER problems understanding concepts or material than 75% of the children of her age. Therefore, in many day-to-day situations we make implicit and intuitive quantitative statements.

If we want to make quantitative statements about a scientific concept, we run into one of the central problems in psychology. This is namely WHAT do we want to make a comparison about? Or in other words, how do we define a psychological construct so that we can measure it? A geographer, chemist or physicist is unlikely to phase the problems that psychologists have… after all, we can easily measure distances (e.g., how far is Auckland from Wellington), we have ways of dating the age of a piece of rock or we can measure the energy of particles when we collide them at the near speed of light. Psychologists on the other hand are dealing with intangible concepts that are difficult to specify. Most of you are familiar with concepts such as intelligence, attitudes, personality traits, depression or identity. However, if we were to ask you to pinpoint any of these concepts in the real world, we would be unable to do so. Our psychological terminology refer to unobserved mental constructs that we create in our community of fellow psychologists to indicate a particular set of problems, describe a particular set of behaviours or mental representations. I would argue that underlying many of these psychological terms are assumptions about relative coherence, stability, generalizability and potentially even some general biological foundations that lead to the emergence of such a syndrome. Therefore, we don’t just invent these terms on a whim, but we think that there is something meaningful to them that we think is important enough to look into and tell other people about.

Therefore, the first issue in any psychological study, even though it may not seem obvious anymore, is to clearly and unambiguously define and specify what we want to study. What is our construct or process of interest? It is at this point, that culture will throw the first curve ball at any psychologist attempting to address this question. How can we make sure that our definition or mental construct of our psychological term or process is actually valid or does have some meaning in another cultural context? How does our upbringing in a highly developed Western society influence how we think about psychological constructs? Can we assume that identity is a concept that is meaningful in a village in the lowland Amazon basin? Is our definition of depression applicable to refugees coming from Syria or Iraq? Is conscientiousness a useful term to screen out applicants for jobs in an international organization? Therefore, the first problem in any psychological study is to unambiguously define and describe the psychological process for all the populations that we are interested in. We could think of this as a mental bubble that we draw around some problem or process. Does this bubble ‘exist’ in all the different cultures that we want to include in our study? How can we find out whether this bubble is meaningful and has some value or relevance for all the local populations? We will discuss this as the question of functional equivalence.

If we are confident that there is some value to this mental bubble of ours (let’s say, depression, personality or identity) and that the terms are meaningful in two or more cultures, then we need to find good indicators for it. In psychological terms, this is called operationalization. How can we empirically say that one person has more of this latent category quality that we just created with our mental bubble compared to another person? What would be a good indicator to tell us that one person is better for a job compared to another person or that one person is a better learner than another, who in turn may need some help? Here again, culture will throw lots of beautiful little challenges at us. We need to find indicators that are meaningful and relevant in each cultural context, but obviously we would still need to be able to compare the results across contexts. Therefore, we can’t have indicators that are relevant and meaningful in each context, but cannot be compared across cultures. We want to aim for some level of comparability. For example, is staying late at your desk a good indicator of being conscientious? Or could it be seen as being disorganized and incompetent? What if people are unfamiliar with office jobs? Is the number of items that you circled the temple this morning before going to work a better indicator of your conscientiousness? Is the ability to track animals over long distances and varied terrain a good indicator of concentration?  Or should we give people lots of d’s and b’s and p’s and q’s and then ask them to count how many p and q’s were together in each line? Should we measure intelligence by asking people to name as many types of medicinal plans for diarrhoea? Or give them complex questions about history and philosophy? This problem of identifying good measurement indicators will be called structural equivalence. Obviously, how we define and how we operationalize a construct is very much dependent on each other. For this reason, some researchers lump the two terms together as construct equivalence. For reasons that we will discuss later, I prefer to keep them separate.

So, we now have a mental bubble and we have a number of indicators that give us some clue about the latent bubble. However, we don’t actually know how good each of these indicators is in representing that latent bubble. We need to find a way to show us how well each indicator works in each of our cultures. In other words, is the same indicator better in capturing a key aspect of our construct in one culture compared to another? For example, is going to parties and having lots of friends a good indicator of extraversion? Is having many wives a good indicator of social status in all cultures? Is staying late at work to finish a good indicator in all cultures for high conscientiousness? This problems is called metric equivalence. It is the question about the relative strength of the indicator-latent variable relationship. In technical terms, we are concerned with the equivalence of factor loadings or item slopes in classic test theory or the item discriminability in item response theory.

Finally, we may be convinced that our indicators work equally well in all contexts. Each questionnaire or test items is really giving us a good and reliable insight into the construct. But there may be still problems. Some items, even though they have the same relationship with the latent construct in all cultures, may still be a bit more difficult or easier in one context compared to another.  If I would ask you to name the capital of Benin, most of you would probably struggle finding the correct answer. Benin is a country that is quite far from our thoughts and most of us will never set foot in this place or may not have heard about it in the media. However, if I would ask you about the capital city of one of your neighbouring countries, you would probably quite easily be able to name it. Therefore, asking about the capital of Benin would be easier for somebody living in Togo or Nigeria compared to somebody living in NZ or Denmark. This is the issue of full score or scalar equivalence. Technically, we would look at the invariance of item intercepts (in a multi-group CFA) or the differential item difficulty (in IRT).


In summary, measuring psychological attributes or processes across cultural contexts is quite difficult. I gave some relatively superficial and easy examples to make this a relatively non-technical and easy intro to the problem. We need to define our construct – draw our mental bubble around what we want to study. The first step in any cultural study then is to make sure that this construct or mental bubble is meaningful and functional in all cultures that we want to study. Once we think this is the case, we need to find good indicators that are observable and give us some insight into the position or state of an individual in relation to our mental bubble. We then need to discuss whether the indicators are equally good in all contexts or whether some are better in telling us something about a person or process in one cultural context compared to another. Finally, we need to find out whether all indicators are equally easy or difficult. Only once we have fulfilled this last criterion can we actually make any comparisons between individuals or groups across cultures. This is a tough task and unfortunately, most studies that you will see in the literature do fall well short of it. But this is the challenge that we really need to meet in order to develop a meaningful and universal psychological science. 

Sunday, December 21, 2014

Cross-cultural measurement invariance testing in R in 5 simple steps

Here I am presenting a quick crash course to run invariance tests for cross-cultural research in R. R is a free programme and has an expanding list of awesome features that should be of interest to people doing cross-cultural work. 

I am working with RStudio, but there are other options for running R. Here are the basic steps to get you started and run a cross-cultural equivalence test. 


1. Set your working directory


This step is important because it will allow you to call your data file later on repeatedly without listing the whole path of where it is saved. For example, I saved the file that I am working with on my dropbox folder in a folder called 'Stats' that is in my 'PDF' folder. 
I need to type this command:

setwd("C:\\Users\\Ron\\Dropbox\\PDF\\Stats")

Two important points: 
a) for some strange reason you need double \\ to set your directory paths with windows.
b) make sure that there are no spaces in any of your file or directory paths. R does not like it and will throw a tantrum if you have a space somewhere. 


2. Read your data into R


The most convenient way to read data into R is using .csv files. Any programme like SPSS or Excel will allow you to save your data as a .csv file. There are a few more things that we need to discuss about saving your data, but I will discuss this below. 

Type:
justice <- read.csv("justice.csv", header = TRUE)

R is an object oriented language, which means we will constantly create objects by calling on functions: object <- function. This may seem weird at first, but will allow you to do lots of cool stuff in a very efficient way.

I am using a data that tested a justice scale, so I am calling my object 'justice'. 


3. Deal with missing data


If you have absolutely no missing data in your data file, skip this step. However most mortal researcher souls will have some missing data in their spreadsheet. R is very temperamental with missing data and we need to tell it what missing data is and how to deal with it. Some people (including myself) who are used to SPSS typically leave missing data as a blank cell in the spreadsheet. This will create problems. The best option is to write a little syntax command in spss to recode all blank cells to a constant number. 
For example, I could write something like this in SPSS:

recode variable1 to variable12
(sysmis=9999) (else=copy) into
pj1 pj2 pj3 pj4 ij1 ij2 ij3 ij4 dj1 dj2 dj3 dj4. 
Execute. 

Now I can save those new variables as a .csv file and read into R using the step above.

Once you have executed step 2, you need to define these annoying 9999s as missing values.
The simplest and straightforward option is to write this short command that converts all these offending values into NA - the R form of missing data.

justice[justice==9999] <- NA

Note the square brackets and double ==. If you want to treat only a selected variable, you could write:

justice$pj1[justice$pj1==9999] <- NA

This tells R that you want only the pj1 variable in the dataframe justice to be treated in this way. 

To check that all worked well, type:

summary(justice)

You should see something like this:

If all went well, now your minimum and maximum values are within the bounds of your original data and you have a row of NA's a the bottom of each variable column.


4. Loading the analysis packages


R is a very powerful tool because it is constantly expanding. Researchers from around the world are uploading tools and packages that allow you to run fancy new stats all the time. However, the base installation of R does not include them. So we need to tell R which packages we want to use.
For measurement invariance tests, these three are particularly useful: lavaan (the key one) and semTools (important for the invariance tests).

Write this code to download and install the packages on your machine:


install.packages(c("semTools", "lavaan"))

Make sure you have good internet connectivity and you are not blocked by an institutional firewall. I had some problems recently trying to download R packages when accessing it from a university campus with a strong firewall. 

Once all packages are downloaded, you need to call them before you can run any analyses:

library("lavaan")

library("semTools")

Important: You need to call these packages each time that you want to run some analyses, if you have restarted R or RStudio.


5. Running the analyses


R is an object oriented language, as I mentioned before. The analysis can be executed in a couple of steps. First we need to specify the model that we run by creating a new object that contains all the information. Then we tell R what to do with that model. Finally, we have various options for obtaining the results, that is the fit indices and parameter estimates as well as other diagnostic information. 

Let's create the model first:
cfa.justice<-'
pj=~pj1+pj2+pj3+pj4
ij=~ij1+ij2+ij3+ij4
dj=~dj1+dj2+dj3+dj4'

This creates the model that we can then work with. The '=~' denotes that the items are loading on the latent factor. This is what it looks like:


The next set of commands sets specifies what should be done with the model. In the case of a simple CFA, we can call this function:


fit.cfa <-cfa(cfa.justice, data=justice)

To identify the model, lavaan sets the loading of the first item on each latent variable to 1. This is convenient, but may be problematic if the item is not a good indicator. An alternative strategy is to set the variance of the latent variable to 1. This can be done by adding std.lv=TRUE to the fit statement. 


fit.cfa <-cfa(cfa.justice, data=justice, std.lv=TRUE)

This statement runs the analysis, but we still need to request the output.

The simplest way is to use summary again. Here is an option that prints both the fit indices and the standardized parameters.


summary(fit.cfa, fit.measures= TRUE, standardized=TRUE)

Here is a truncated and annotated version of the output:


lavaan (0.5-17) converged normally after  39 iterations
                                                                  Used       Total
  Number of observations                          2518        2634
 
# The following shows the estimator and the Chi square stats. As can be seen, we have 51 df's, but the model does not fit that well.
  Estimator                                                  ML 
Minimum Function Test Statistic              686.657 
Degrees of freedom                                   51 
P-value (Chi-square)                                 0.000

Model test baseline model:
  Minimum Function Test Statistic            20601.993 
Degrees of freedom                                 66 
P-value                                                     0.000
#The incremental fit indices are in contrast quite good. The CFI and TLI should be ideally be above .95 (or at least .90). So this model does look good.
User model versus baseline model:
  Comparative Fit Index (CFI)                    0.969 
Tucker-Lewis Index (TLI)                       0.960

Loglikelihood and Information Criteria:
  Loglikelihood user model (H0)             -44312.277 
Loglikelihood unrestricted model (H1)     -43968.949
  Number of free parameters                         27
#The AIC and BIC are useful for comparing models, especially non-nested models. Not the case right now.
  Akaike (AIC)                               88678.555 
Bayesian (BIC)                             88835.998 
Sample-size adjusted Bayesian (BIC)        88750.212
#The RMSEA should be small. Values smaller than .08 are deemed acceptable, below .05 are good. We are doing ok-ish with this one here.
Root Mean Square Error of Approximation:
  RMSEA                                          0.070 
90 Percent Confidence Interval          0.066  0.075  
P-value RMSEA <= 0.05                          0.000
#Another useful lack of fit index. The SRMR should be below .05 if possible. This is looking good.
Standardized Root Mean Square Residual:
  SRMR                                           0.037
#Now we have a print out of all the parameter estimates, including the standardized loadings, variances and  covariances. Here it is important to check whether loadings are relatively even and strong, and that the variances and covariances are reasonable (e.g., we want to avoid very high correlations between latent variables). It is looking ok overall. Item ij4 may need some careful attention.
Parameter estimates:
  Information             Expected  Standard Errors                             Standard
                   Estimate  Std.err  Z-value  P(>|z|)   Std.lv  Std.all
Latent variables: 
pj =~    pj1   1.000                                           1.202    0.789   
pj2               1.009    0.027   38.005    0.000    1.213    0.790   
pj3               0.769    0.025   31.003    0.000    0.924    0.643   
pj4               0.802    0.024   33.244    0.000    0.963    0.687 
ij =~    ij1   1.000                                             1.256    0.879   
ij2               1.064    0.014   75.039    0.000    1.335    0.957   
ij3               1.015    0.015   68.090    0.000    1.274    0.910   
ij4               0.808    0.022   37.412    0.000    1.014    0.644 
dj =~    dj1 1.000                                              1.211    0.821   
dj2               1.013    0.019   51.973    0.000    1.227    0.871   
dj3               1.056    0.020   52.509    0.000    1.279    0.877   
dj4               1.014    0.021   48.848    0.000    1.228    0.834
Covariances: 
pj ~~    ij    0.828    0.041   20.435    0.000    0.549    0.549   
dj                0.817    0.040   20.187    0.000    0.562    0.562 
ij ~~    dj    0.711    0.037   19.005    0.000    0.467    0.467
Variances:   
pj1               0.874    0.036                      0.874    0.377   
pj2               0.889    0.036                      0.889    0.377 
pj3               1.213    0.039                      1.213    0.587   
pj4               1.040    0.035                      1.040    0.528   
ij1               0.464    0.016                      0.464    0.227   
ij2               0.164    0.011                      0.164    0.084   
ij3               0.336    0.013                      0.336    0.172   
ij4               1.448    0.042                      1.448    0.585   
dj1               0.709    0.025                      0.709    0.326   
dj2               0.479    0.019                      0.479    0.242   
dj3               0.489    0.020                      0.489    0.230   
dj4               0.662    0.024                      0.662    0.305   
pj                1.444    0.066                      1.000    1.000   
ij                1.576    0.057                      1.000    1.000   
dj                1.466    0.060                      1.000    1.000



However, we want to do an invariance analysis. Right now we collapsed the samples and ran an analysis across all groups. This can create problems, especially if the samples have different means (see my earlier blogpost for an explanation of this problem). 

The grouping variable should be a factor, that is a string variable that has the labels. You can also use continuous variables, but then you will need to remember what each number means. In this example, I have data from three samples:

> summary(justice$nation)
     Brazil        NZ         Philippines 

        794        1146         694 

It would be informative to see whether the item loadings are similar in each group. To do this, we only need to add group="nation" to our cfa statement.

fit.cfa.separate <-cfa(cfa.justice, data=justice, group="nation")

We can then print the results by using the summary statement again (remember that we have to call the new object for this analysis):

summary(fit.cfa.separate, fit.measures= TRUE, standardized=TRUE) 

I am not printing the output.

Of course, this is not giving us the info that we want, namely whether the model really fits. In addition, we could ask for equal loadings, intercepts, unique variances, etc. I can't go into details about the theory and importance of each of these parameters. I hope to find some time soon to describe this. In the meantime, have a look at this earlier article. 

In R, running these analyses is really straightforward and easy. A single command line will give us all the relevant stats. Pretty amazing!!!!!

To run a full-blown invariance analysis, all you need is to type this simple command:

measurementInvariance(cfa.justice,
                                      data=justice,
                                      group="nation",
                                      strict=TRUE)

You can write it as a single line. I just put it on separate lines to show what it actually entails. First, we call the model that we specified above, then we link it to the data that we want to analyze. After that, we specify the grouping variable (nation). The final line requests strict invariance, that is we want to get estimates for a model where loadings, intercepts and unique variances are constrained as well as a model in which we constrain the latent means to be equal. If we don't specify the last line, we will not get the constraints in unique variances. 

Here is the output, but without the strict invariance lines:


Measurement invariance tests:
Model 1: configural invariance:
    chisq        df    pvalue       cfi     rmsea       bic
  992.438   153.000     0.000     0.962     0.081 86921.364
Model 2: weak invariance (equal loadings):
    chisq        df    pvalue       cfi     rmsea       bic
 1094.436   171.000     0.000     0.958     0.080 86882.400
[Model 1 versus model 2]
  delta.chisq      delta.df delta.p.value     delta.cfi
      101.998        18.000         0.000         0.004
Model 3: strong invariance (equal loadings + intercepts):
    chisq        df    pvalue       cfi     rmsea       bic
 1253.943   189.000     0.000     0.952     0.082 86900.945
[Model 1 versus model 3]
  delta.chisq      delta.df delta.p.value     delta.cfi
      261.505        36.000         0.000         0.010
[Model 2 versus model 3]
  delta.chisq      delta.df delta.p.value     delta.cfi
      159.507        18.000         0.000         0.006
Model 4: equal loadings + intercepts + means:
    chisq        df    pvalue       cfi     rmsea       bic
 1467.119   195.000     0.000     0.942     0.088 87067.134
[Model 1 versus model 4]
  delta.chisq      delta.df delta.p.value     delta.cfi
      474.681        42.000         0.000         0.020
[Model 3 versus model 4]
  delta.chisq      delta.df delta.p.value     delta.cfi
      213.176         6.000         0.000         0.009


How do we make sense of this?

Model 1 is the most lenient model, no constraints are imposed on the model and separate CFA's are estimated in each group. The CFI is pretty decent. The RMSEA is borderline. 
Model 2 constraints the factor loadings to be equal. The CFI is still pretty decent, the RMSEA actually improves slightly. This is due to the fact that we have now more df's and RMSEA punishes models with lots of free parameters. The important info comes in the line entitled Model 1 versus model 2. Here we find the difference stats. The X2 difference test is significant and we would need to reject model 2 as significant worse. However, due to the problems with the X2 difference test, many researchers treat this index with caution and examine other fit indices. One commonly examined fit index of the difference is Delta CFI, that is the difference in CFI fit from one model to the next. It should not be larger than .01. In our case, it is borderline - the delta CFI is .01.
We can then compare the other models. The next model constraints both loadings and intercepts (strong invariance). The model fit is pretty decent, we can probably assume that both loadings and intercepts are invariant across these three groups. 
In contrast, constraining the latent means shows some larger problems. The latent means are likely to be different. 


6. Further statistics 

In this particular case, the model fits pretty well. However, often we run into problems. If there is misfit, we either trim the parameter (drop parameters or variables from the model) or we can add parameters. To see which parameters would be useful to add, we can request modification indices. 
This can be done using this command:
mi <- modificationIndices(fit.cfa)
mi

The second line (mi) will print the modification indices. It gives you the expected drop in X2 as well as what the parameter estimates would be like if they were freed.

If we want to print only those modification indices above a certain threshold, let's say 10, we could add the following line:
mi<-modificationIndices(fit.cfa)
subset (mi, mi>10)
mi

This will give us modification indices for the overall model. If we want to see modification indices for any of the constrained models, we can request them after estimating the respective model.

For example, if we want to see the modification indices after constraining the loadings to be similar, we can run the following line:

metric <-cfa(cfa.justice, 
                    data=justice,
                    group="nation", 
                    group.equal=c("loadings"))
mi.metric<-modificationIndices(metric)
mi.metric

This will now give us the modification indices for this particular model. 

There are more options for running constrained models. For example, this line gives the scalar invariant model: 

scalar <-cfa(cfa1,
                   data=justice,
                   group="nation", 
                   group.equal=c("loadings", "intercepts"))

As you can see, these models are replicating the models implied in the overall analysis that we got with the measurementInvariance command above. 



Summary

I hope I have convinced you that measurement invariance in R using lavaan and semTools is a piece of cake. It is an awesome resource, allows you to run lots of models in no time whatsoever and of course it is free!!!!! Once you get into R, you can do even more fancy stuff and run everything from simple stats to complex SEM and ML models in a single programme. 

More info on lavaan can be found here (including a pdf tutorial). 

I am still in the process of learning how to navigate this awesome programme. If you have some suggestions for simplifying any of the steps or if you spot some mistakes or have any other suggestions... please get in touch and let me know :)
If there are some issues that are unclear or confusing, let me know too and I will try and clarify!
Look forward to hearing from you and hope you find this useful!


Wednesday, December 17, 2014

The beauty of black and white

Marcel Cesar, a friend of mine, nominated me for a five-day black and white challenge on FB. The task was to post one picture per day, for five days. He posted some pretty amazing ones and seeing some of those amazing pictures by him and others, I thought I would try to match their brilliance. 
It had been ages since I last took BW photos. I had been trained to do all the processing in a darkroom when I was a kid, but after I switched to digital, I hardly used BW settings or converted pictures to BW afterwards. However, when experimenting in the evenings with some shots that I liked or had taken a while ago, I started to remember what had drawn me to photography as a kid. Pictures in BW have this timeless quality, simple shots are rendered art and captured history at the same time. Simple moments that would pass in a flash suddenly feel like they carry bigger significance, being transformed into instant classics. 

I just wanted to give a bit of background to those five pictures that I had selected. For me good photography (and photos) tell stories. I want to capture a moment that appears significant or beautiful or both. I have memories and thoughts attached to most photos that I take and I want to share a bit of how I see the world. When I see photos taken by others, I also try to find the stories that they capture or the thoughts that the photographer may have had when taking the shot. We probably all see different stories unfold in these frozen moments, but this is the fun to decipher somebody else's mind and see the world through their eyes. 




This is a portrait of Gi, a great friend of mine. We always catch up with each other when I happen to pass through Brazil and we have done a few photo shots over the years. This was on a hot Saturday afternoon in Lapa, the bohemian centre of Rio de Janeiro. Lapa, even though dizzily crowded with party-goers at night and with people hurrying about their business during weekdays, is eerily empty on weekends during the heat of the midday sun. We were just crossing the sun-bleached square in front of the arcs when I snapped this picture. For me, the smile and the sun capture two of the most iconic features of Rio. 



This was the finishing moment of one of the dance performances during the 2014 Diwali celebration in Wellington. Diwali in NZ has turned into a celebration of Indian culture, with dance and music performances by Indian school and community groups, arts and craft shops and lots of food stalls. It is secular public ritual and has quite a different vibe and atmosphere compared to Diwali in India. I like this shot because of its ephemeral grace and humbleness in the gestures of the dancers. 


The sky over Kaikora was breathtaking. We were preparing for some weekend tramp in the mountains around Kaikora and walked out to this spot by the beach to enjoy the sun. Some seals were playing in the water and lots of people were out and about on this sunny autumn day. I had a Canon 10D and was playing around with different tone options at that time. Being shot in sepia, for me, this shot captures the simplicity and timeless beauty of NZ. 



This is a deeply spiritual picture. I snapped this moment at the end of the street procession during the Vegetarian Festival, a Taoist celebration in Thailand. A spirit medium (ma song) is about to awake from trance and the priest (huat kua) is performing the rites to bring the human back. The incense has a deep spiritual significance here, because the central god takes the form of incense smoke. 


This photo was originally shot in BW. It is a scene from a market on the outskirts of the old city in Damascus. I was teaching in Beirut, Lebanon in 2005 and went to Damascus for a weekend. Meandering aimlessly through the maze of 5,000 year old streets, I came out to this little square where neighbors had organized a small fair with very simple carousels and stalls. These kids happily showed me the goldfish that they had just bought. Simple happiness and pleasures. It is sad to know that these peaceful places have disappeared in the bitter brutality of a civil war. 

Wednesday, August 27, 2014

National identity vs genetics: Are haplogroups becoming the new race?

I just got back from the Africa to Aotearoa Project Presentation at the Governor General Residence in Wellington. It is part of Genographic, the National Geographic sponsored project on mapping the human ancestry. It was a fascinating evening at various different levels. For one, the lack of security going into the residence of the de-facto head of state of NZ and the legal representative of the Queen in NZ was pretty amazing. A police officer wanted to see our invitation card at the gate and then just waved us through. No security anywhere. Compare that with just a single trip to any US or UK embassy...
The Governor General gave a brief speech, followed by a great overview presentation by Lisa Matisoo-Smith, one of the NZ leaders of the project. She gave a nice and easily accessible overview of genetic diversity and the shifts that have occurred since we moved out of Africa. The glacial period and the neolithic transition (especially the invention of agriculture) were periods of major changes in our genes. As people moved around the world, further mutations occurred and it is now possible to track the genetic heritage of groups of individuals. Groups who share a common ancestor, that is they share similar mutations, are forming a so-called haplogroup. The haplogroups studied in the Genographic Project are associated with mitochondrial DNA (passed on by the mother) and the Y-chromosome (passed on by fathers). The amazing fact is that this information allows a pretty accurate placing of individuals in terms of their genetic ancestry.

Straightforward genetics and highly fascinating. The current Governor General Sir Sir Jerry Mateparae, the former Governor General Sir Anand Satyanand as well as Gisborne Mayor Meng Foon all provided their DNA and a fascinating review of their genetic ancestry was displayed. 

However, what turned the whole event into a slightly less positive light was the frequent mentioning of national identity. New Zealand is one of those places where national identity is highly contested and politically sensitive. Maori as the first settlers have been around for about 750 years, followed by the European colonization project that started about 200 years ago. Both the Governor General as well as various speakers after him referred to this project as helping to find or determine a national identity for NZ. Some reference was made to the ethnic mixing of people, after all, the haplogroups show how much mingling there has been between individuals and groups on the giant track out of Africa all the way to the end of the world in the Pacific. However, the labels that were applied to individuals - haplogroup R, M, U, etc. created little tribes of related individuals. Photographs were taken of the 'families'. 




There has been a long and controversial tradition of linking identity to race. Social and biological scientists concerned with identity have been battling the common conception that race is a biologically meaningful concept. Good news is that old school race and genetics linkages seem to be waning. But the event tonight seemed to replace this old idea of race with the more 'scientific' and empirical evidence of haplogroups. Despite all the efforts by speakers that we are all mixed, people may start identifying and separating themselves via their ancestral haplogroups. This is the slightly worrying thought for me, namely that simplified and stereotyped haplogroups become the new race in the definition of group identities. What about genetic testing in the future to determine whether you belong to us or not? What haplogroup can be a true New Zealander? 

Why do we need to link a highly fascinating project on our genetic ancestry to national identity? I thought it was a great evening, with some worrying undertones...

Sunday, August 24, 2014

The motivational basis of personality & why threat is important



How do we describe what other people are like? What are the major characteristics that we can use to describe the personality of friends and strangers? As far as we know, these questions has been discussed since the emergence of ancient civilizations in Greece, India and China. In modern psychology, starting with Gordon Allport, a more or less sharp distinction has been made between values - that is motivational goals important for people in their lifes - and personality traits - described as behavioural consistency across situations. The study of values has flourished in social psychology and the study of personality traits has been a core area of research of personality psychology, with little discussion of the overlap or convergence between the two approaches.

Diana Boer and myself were intrigued to test the similarities of these two approaches. We were inspired by newly developed neural network models of personality that conceptualized personality as expressions of basic motivational goal systems. If personality traits are expressions of motivational goal systems, we should see some systematic relationships with values. Furthermore, we were aware of a number of studies that have found quite variable associations between values and personality traits in the Big Five tradition. If there is some systematic association, as we presumed, why should there be such variability in empirical studies?

We set out to address these two broad sets of questions in a paper that is appearing in the Journal of Personality.

We collected all the studies that have reported correlations between any set of Big Five instruments with the circular value theory described by Shalom Schwartz and conducted a meta-analysis to identify the overall patterns that might be obscured in individual studies. Let us first quickly review personality traits and values. 

Overview of Personality & Values


Here is a summary of the Big Five traits from Wikipedia:
Openness to experience: (inventive/curious vs. consistent/cautious). Appreciation for art, emotion, adventure, unusual ideas, curiosity, and variety of experience. Openness reflects the degree of intellectual curiosity, creativity and a preference for novelty and variety a person has. It is also described as the extent to which a person is imaginative or independent, and depicts a personal preference for a variety of activities over a strict routine. Some disagreement remains about how to interpret the openness factor, which is sometimes called "intellect" rather than openness to experience.
Conscientiousness: (efficient/organized vs. easy-going/careless). A tendency to be organized and dependable, show self-discipline, act dutifully, aim for achievement, and prefer planned rather than spontaneous behavior.
Extraversion: (outgoing/energetic vs. solitary/reserved). Energy, positive emotions, surgency, assertiveness, sociability and the tendency to seek stimulation in the company of others, and talkativeness.
Agreeableness: (friendly/compassionate vs. analytical/detached). A tendency to be compassionate and cooperative rather than suspicious and antagonistic towards others. It is also a measure of one's trusting and helpful nature, and whether a person is generally well tempered or not.
Neuroticism: (sensitive/nervous vs. secure/confident). The tendency to experience unpleasant emotions easily, such as anger, anxiety, depression, and vulnerability. Neuroticism also refers to the degree of emotional stability and impulse control and is sometimes referred to by its low pole, "emotional stability".

Shalom Schwartz' theory of values differentiates at least 10 value types that can be organized into two major higher order dimensions. These two major dimensions are openness to change (individualistic) versus conservative (collectivistic) values on one hand and self-enhancing (dominance) versus self-transcendence (altruistic) values. Individual values can now be ordered in a circular structure along the two dimensions. Moving around the circle, Power (PO) captures the goals of striving towards social status and prestige, controlling or dominating over people and resources. Achievement (AC) emphasises personal success through socially approved standards of competence. Hedonism (HE) values focus on pleasure and sensuous gratification of the sense. Stimulation (ST) captures excitement, novelty and pursuing challenging goals in one’s life. Self-direction (SD) entails valuing independent thought and action. Universalism (UN) values refer to the motivation to understand, appreciate, tolerate and protect the welfare of all people and nature. Benevolence (BE) in contrast has a more narrow focus on preserving and enhancing the welfare of people close to oneself (family and close friends). Tradition (TR) values are focused on respecting, accepting and committing to the customs and ideas of the traditional culture and religion. Conformity (CO) refers to restraining actions or impulses that may upset or harm others and violate social expectations and norms. Finally, security (SE) emphasises values around safety, harmony and stability of society, social relationships and the self.

Values and Personality Traits are systematically linked


Our basic argument was that values as motivational goals and personality traits as behavioural consistencies should be systematically linked. Using a new method that we developed in a previous article that allows us to track the systematic relation of personality traits to the underlying structure of values (see our previous article), we were able to examine the overall relationship between the two constructs. 

Schematically, the overall association can be described like in this figure:

Agreeableness and Self-Transcendence (positively) versus Self-Enhancement (negatively) values are strongly and consistently related. Agreeable individuals are also very benevolent and they tend to care about others, close and distant alike. Similarly, Openness personality traits were strongly associated with Openness to Change (positively) versus Conservatism (negatively) values. Open individuals also strongly value stimulation and self-direction values. The correlations for these two traits with values were substantive and not significantly different from correlations between different personality instruments measuring the same trait. Hence, for these two personality traits, the relationship with values is strong and values and personality are highly convergent. 
Extraversion is somewhat more weakly related to Openness values, but also weakly to Self-Enhancement values (achievement and power values). Conscientiousness is related to Conservatism values, but also shows some correlations with Self-Enhancement values (in particular achievement values). These two personality traits correlated significantly weaker with values and there were sometimes notable secondary associations of traits with the other main axis of values. This suggests that these two traits are more complex in their basic motivational structure. 
Finally, Neuroticism is a stability oriented personality trait and no surprisingly shows some weak associations with values that ensure stability (Conservatism values). 

This was a really re-assuring and strong pattern overall. By using a holistic approach to values and personality across a large number of studies, we were able to show the overall systematic relationships between these two constructs. 

Threat undermines the value-personality trait relationship


But moving to the second main question, how can we make sense of the variations in these correlations across different studies? 
Here we need to first briefly consider how values and personality traits might be causally related. First, values may provide the motivational structure for humans, that is then expressed in behaviour (personality traits). Assuming this logic, people who value conformity will follow rules and orders with great care. Values come first and actions follow. Second and alternatively, following classic self-perception theory, people might engage in behaviours in relatively consistent ways, which they then interpret in terms of overarching goals and re-interpret as their stable values. E.g., I am always conscientious and follow rules and orders in a consistent way, therefore, I am probably a person who values conformity and tradition.  Here, actions come first and values are inferred from these actions in a secondary step. 

We thought about what may weaken this link between values and traits. Environmental threats should play a major role in how values and personality traits are linked. In highly threatening environments, that is environments where there is poverty, lots of environmental threats such as cold winters and superhot summers, lots of diseases (you can easily get infected and die), little available food, lots of violence and no personal freedom, people are probably quite restricted in their choices and behaviours. Hence, their personal values may not be expressed in behaviours, because the environment determines their actions more than their personal orientations and motivations. Equally, since the environment strongly influences people's behaviour, individuals may not interpret their own behaviour as reflecting some underlying values because their behaviour is more strongly influenced by environmental pressures. Hence the overall link between values and personality traits should be lower, regardless of how values and personality are causally related. 

We tested this hypothesis using a good number of different indicators of threat. Amazingly, threat turned out to be a strong and consistent moderator for most of the value-personality associations. Hence, our analysis of environmental threat in a broad sense can explain why there was so much variability in the literature previously. 

Exploring Personality Systems


It also could explain why researchers have not integrated personality research with value research in a more systematic way. Given the substantive variability, researchers might have thought that the links are too weak and too inconsistent to be worth pursuing. However, we believe we have shown that by taking a broader picture, examining the value-personality link in a more systematic way and examining the conditions in which the links are stronger or weaker, we can move both value and personality research forward. 

In my view, values and personality traits are expressions of underlying motivational systems that are encoded in similar and overlapping language. It is more of an accident of history that these two systems have emerged in different research fields. I hope this study will help to bring the fields back together and allow a more sophisticated examination of how values and traits are both expression of human personality.