Friday, June 27, 2008

Reading Response to Giudice (2008)

Reading Response to Giudice (2008)

Abstract

Our lab’s research in China does not show gender differences in insecure attachment patterns. We believe that cultural differences between Chinese and Western societies may help to explain this phenomenon. Mating and parenting circumstances in China do not allow males to adopt a zero-investment strategy. In addition, attachment styles are transmitted across generations and last for the whole lifespan. Here, we argue that the influence of mating and parenting on the well-developed attachment patterns in childhood is relatively small.



Full Text

In section 6 of the target article, Giudice (2008) reported a significant gender difference in insecure attachment: Whereas females were more likely to be ambivalent, males were more likely to be avoidant. However, gender differences have rarely been reported in prior studies (Crittenden 2000; Schmitt 2003). We believe a cross-cultural perspective may help to reconcile this apparent contradiction. In particular, attachment studies in Asian cultural samples, such as China, should be taken into account for a more comprehensive analysis.

Our recent studies in China suggest that there are no gender difference in insecure attachment styles (Li 2005; Li & Du 2005; Li & Kato 2006; Li et al 2006a; b; c; 2007; in press; Wang & Li in press). Table 1 summarizes our results across a variety of demographic groups (middle school students, undergraduates, company employees, inpatients and mothers). Pearson Chi-square tests showed that neither sample had significantly different attachment patterns between males and females. We also note that in the mother sample, there were more avoidant females than anxious/ambivalent ones.



Giudice argues that males and females strive to maximize their reproduction of genes. Gender differences in mating, reproduction and parenting efforts lead to diverse attachment styles: insecure females tend to be anxious/ ambivalent, while insecure males tend to be avoidant (sect 6.3.1, para 5). However, reproductive investment alone does not account for the total cost of reproduction and parenting. Females have the privilege to select the most suitable male who help child-rearing (Clutton-Brock 1991). Transitional China since the 1980’s is one such example where parental investment is significantly higher than that in Western nations (Wang & Ollendick 2001). During the 1980’s, the Chinese government began to implement family planning (“one child”) policy to control population growth, which profoundly changed the demographic as well as cultural values in Chinese society (Arnold & Liu 1986; Xu et al. 2007). First, this policy does not allow males to have multiple children, which requires males to invest in the quality of offspring, rather than quantity (Wang & Ollendick 2001). This greatly reduces the likelihood of males taking a zero-parenting strategy. Secondly, the traditional son preference was even exaggerated and the “one child” policy often became a “one son” policy, creating an unbalanced gender ratio (Chan et al 2006). In this case, males have to compete for limited number of females. Finally, the woman’s rights movement has been widespread since the communist liberation in the early 1950s when the socio-economic status of women improved considerably. Recent studies have shown that during family purchase decisions, females now play an equal- status role as males (Dong & Li 2007). Thus, for contemporary Chinese females, although they cannot shift the balance between parenting and mating effort as easily as men, they do not need to develop an anxious/ambivalent attachment strategy to invite paternal investment (Archer & Mehdikhani, 2000).

A gender difference in insecure attachment could also be explained from the perspective of intergeneration transmission. According to Bowlby (1980), people develop their mental representations of the environment and significant others based on their experience with parents or other caregivers. Bowlby labeled this mental representation as an Internal Working Model (IWM). Once formed, IWMs tend to remain stable for the whole lifespan (Hu & Meng 2003). The stability of IWM produces similar attachment patterns from childhood to adulthood. This argument is supported by cross-sectional and longitudinal studies (Durrett et al. 1984; Brennan et al. 1998; Fraley & Spieker 2003; Hu & Meng 2003; Nakao & Kato 2003; Li & Kato 2006). Li (2006) summarized the distribution of attachment styles in infants and adults in Chinese and American samples. He found that the proportion of each attachment style was similar for both infants and adults. This result suggests that the attachment style may remain relatively stable across the lifespan. Longitudinal studies on attachment development also support the stability of attachment styles within generations (Shemmings 2006; Emery et al. 2008). The stability of attachment from infancy to adulthood suggests that the influence of mate selection and sex competition in early adulthood on attachment patterns is trivial. This may well explain the lack of gender difference in insecure attachment in Chinese samples.

Acknowledgement

This study is supported by National Natural Science Foundation of China (No. ). We thank Adam Pearson and Mark Sheskin for useful comments and suggestions.

References

Archer, J. & Mehdikhani, M. (2000) Strategic pluralism: men and women start from a different point. Behavioural and Brain Sciences 23: 620–621.

Arnold, F. & Liu, Z. (1986) Sex preference, fertility, and family planning in China. Population and Development Review 12: 221-246.

Chan, C., Eric, B. & Chan C. (2006) Attitudes to and practices regarding sex selection in China. Prenatal Diagnosis 26: 610-613.

Clutton-Brock, T.H. (1991) The Evolution of Parental Care. Princeton University Press

Dong, M. & Li, S. (2007) Conflict resolution in Chinese family purchase decisions: The impact of changing female roles and marriage duration. International Journal of Conflict Management 18: 308-324.

Emery, J., Paguette, D. & Bigras, M. (2008) Factors predicting attachment patterns in infants of adolescent mothers. Journal of Family Studies 14: 65-90.

Li, T. (2005) Patterns of attachment in adulthood and rearing style. Chinese Journal of Behavioral Medical Science 15: 149-150.

Li, T., Wang, X. & Guo, X. (2006) Attachment style and dyadic heterosexual interaction behavior among junior high school students. Chinese Journal of Behavioral Medical Science 15: 644-646.

Li, T. & Du, S. (2005) Analysis of adult attachment style of 50 surgery patients. Chinese Journal of Clinical Psychology 13: 417-419.

Li, T. & Kato, K. (2006) Measuring adult attachment: validation of ECR in Chinese sample. Acta Psychologica Sinica 38: 399-406.

Li, T., He, J., Guo, X. & Lu, X. (2006) Adult attachment and social support of self-learning students. Chinese Journal of Behavioral Medical Science 15: 1019-1020.

Li, T., Li, J. & Qin, H. (in press) Adult attachment and mental health in Chinese college students. Chinese Mental Health Journal.

Li, T., Li, N. & Zhu, Y. (2007) Adult attachment and subjective well-being of Chinese college students. Chinese Journal of Behavioral Medical Science 16:54-56.

Li, T., Li, N. & Li, M. (2006) Correlation of adult attachment with social support and subjective well-being. Chinese Journal of Clinical Rehabilitation 10: 47-49.

Shemmings, D. (2006) Using adult attachment theory to differentiate adult children's internal working models of later life filial relationships. Journal of Aging Studies 20: 177-191.

Wan, L. & Li, T. (in press) Adult attachment and handling interpersonal conflict of Employees. Chinese Journal of Clinical Rehabilitation.

Wang, Y. & Ollendick, T. H. (2001) A Cross-cultural and developmental analysis of self-esteem in Chinese and Western children. Clinical Child and Family Psychology Review 4: 253-271.

Xu, A., Xie, X., Liu, W., Xia, Y. & Liu, D. (2007) Chinese family strengths and resiliency. Marriage and Family Review 41: 143-164.





Thursday, June 26, 2008

rfLogger: A Logging Browser and Data Processing Method SurLogger: A Logging Browser and Data Processing Method In Web-based Studies



SurfLogger: A Logging Browser and Data Processing Method

In Web-based Studies

Jibo, He

(Department of Psychology, University of Illinois, Urbana/Champaign, IL, 61801, USA)

Jibohe2@uiuc.edu

ABSTRACT

Despite of the increasing interest in web-based studies, researchers lack a convenient tool for data collection. The existing tools have constraints in the data they could collect or in the availabilities to the study environment. SurfLogger, described in this paper, is an automated data logging tool, free, open-source, cross-platform, and easy to modify. SurfLogger is expected to meet the increasing needs of web-based studies.

Keywords

SurfLogger, browser, instrumentation, Web, WWW, Python

INTRUDCTION

In this information age, the World Wide Web (WWW) is the most fast developing information resources (Eighmey, & McCord, 1998). The booming and infinite opportunities accompanying WWW win interests from vast of communities, including web site designers, user interface researcher, cognitive psychologists, E-commence businessman, as well as many others who are interested in characterizing how users interact with web browser and gain information from varied designs of web pages (Eighmey, & McCord, 1998; Wang, Jing, He, and Yang, 2007; Reeder, Pirolli, and Card, 2000).

Despite of the wide interest in web-based researches, there are still no full-fledged and easily accessible tools to collect user log and browser interactive data. Current data collection methods are far from convenient. Some studies collected data from servers or proxies, which is not only bothersome and expensive to configure the servers or proxies, but also cannot capture users’ interaction with the browser and users experience (Pitkow, 1998). Another substitutive solution is to use videotaped data, usually providing more comprehensive users information (Byrne, John, Wehrle, and Crow, 1999). But coding videotaped data is too consuming in time and labor, and accuracies of coding cannot be guaranteed as perfect. Reeder, Pirolli and Card (2000) did a wonderful job to create WebLogger for data collection in web-based studies. Sadly, WebLogger was written in Visual Basics and depends on Microsoft’s Internet Explorer 6.0 (IE) (Reeder, Pirolli and Card, 2000, 2001). WebLogger cannot be used after IE updating to the latest version of IE 7.0, and neither in Linux and Mac Operating System.

To meet the needs for such a tool in web-based research, I have developed SurfLogger, which collects users’ interaction data with both the web and the browser. SurfLogger is an automated data logging tool, free, open-source, and cross-platform (can be used in Windows, Linux, Mac and many other operating systems), and easy to modify. SurfLogger does not depend on other software, such as Internet Explorer, and does not need installation.

SURFLOGGER

Description

SurfLogger is written in Python, a scripting language, and the GUI (Graphical User Interface) is created with wxPython, which is a Python bundle of wxWidget. SurfLogger can record a variety of user actions with the web pages and the browsers. SurfLogger produces two files, logfile.txt and urlfile.txt. Logfile.txt stores action IDs (natural numbers assigned to each action, used to track the record to the responding actions), the time for each actions, interaction with the browsers (such as, clicking on the Back, Forward, Home, etc. buttons), and mouse coordination when clicking. The time record could be used to compute the time of completion for each task. The number of button press on the browsers could be used as a measure of effort in carrying out the task. SurfLogger also captures the images of each screen when the web page refreshes. Marking the mouse coordination on the screen captures could tell us what links the users clicked at. Urlfile.txt stores action IDs and URLs (Uniform Resource Locator). Action IDs are used to synchronize the record in logfile.txt and urlfile.txt. URL record is stored in a separate file because the abundant information it can provides. I will give an example about how to extract information from urlfile.txt in case study section of this paper.

SurfLogger also calls external software to record the whole process of user actions. Currently, I used Michael Urman’s Screen Recorder named cankiri as my external software for recording, because it is also written in python and shares the same spirit of open source. With video record, the researchers could know more about users’ actions. If quality of recording is emphasized, SurfLogger could easily switch to call other recording software, and only one line of the code has to change to refer to the path of the external software.

Log File Format

The records are stored in two files, the logfile.txt and urlfile.txt. Every variable takes up one line, which begins with variable name, followed by variable value. The variable name is self-explanatory. In the logfile.txt (see Figure 1), a record set for one action includes the mouse coordination, browser action (clicking on Back, Forward Home or other button on the browser), time for the action, and action ID. In the urlfile.txt (see Figure 2), each set of record contains action ID and URL. Adjacent record sets are separated by a blank line. The format of log file is designed to be human-readable and easily read for analysis software.

ID: 3

TIME: 04 Apr 2008 11:50:04

Mouse Coordination: 125 52

Browser Action: Back

Figure 1. Records in logfile.txt

ID3

URLhttp://www.citeulike.org/user/testMaterial/article/2624476

Figure 2. Records in urlfile.txt

Case study

To demonstrate how SurfLogger could benefit web-based research, I will briefly explain the usability analysis of IGroup as a case study (Wang, Jing, He, and Yang, 2007). IGroup is an image search engine, presenting the results in semantic clusters. To test whether IGroup can increase search efficiency compared to MSN, we developed the predecessor of SurfLogger, which functioned similarly like SurfLogger, but less flexible. We developed a measure of Search Effort to compare IGroup and MSN objectively. Search Effort was defined as the number of query input, and number of links and cluster names clicked by the users. Query input, links and cluster names clicked were extracted from URLs recorded by our automated logging tool. A sample URL recorded in this study was listed as follows:

Wednesday, August 30, 2006 3:06:54 PM

http://msra-vss50-b/igroup2/search.aspx?q=Disney#g,14,1,-1

The characters in bold, “Disney”, “14”, and “1” were the input query, ID of cluster name, and result page. The information could be extracted from the URL by simple text processing. For code of data reduction, URL extraction and source code of SurfLogger please refer to my project page of SurfLogger.

RELATED WORK

Although a large number of researchers are interested in web-based study, there are not many well-developed tools. Reeder, Pirolli and Card (2000) developed a great tool called WebLogger, which can collect extensive data, including user input from keyboard and mouse, user actions on the interface elements of IE, and URLs. Choo, Detlor and Turnbull (1999) also developed a similar tool named WebTracker. But both WebLogger and WebTracker can be used only in Windows platform, and relied on the explorer software of IE or Netscape’s Navigator. After the explorers upgraded, the codes of WebLogger and WebTracker have to update too, in order to function normally.

The LogSquare, sold by ManGold Inc., can record keyboard entries, web page actions, mouse clicks, user comments and coding etc. However, despite the price of LogSquare, it can not offer researchers the flexibilities in data collecting and analysis. IT companies also wrote some tools for their usability test. But these tools are usually not full-fledged, and not available to the common researchers (Wang, Jing, He, and Yang, 2007).

Besides the above mentioned automated logging tools, researchers also used some compensatory recording methods. Catledge and Pitkow (1995) studies user interface by capturing client-side browsing event with NCSA’s XMosaic. Byrne and his colleague (1999) used videotape recording to study web-browsing behaviors. However, these methods are not only time-consuming, but also provided limited data about users’ behaviors.

CONCLUSION

SurfLogger is a useful tool for collecting data for web-based researches. With its great features of automated data logging, free, open-source, cross-platform, and no dependence on other browsers, SurfLogger can free many researchers from the financial and time cost in data collecting. SurfLogger is expected to contribute more to the increasing interest in web-based researches.

ACKNOWLEDGEMENTS

I appreciate Dr. Wai-Tat Fu of University of Illinois, Urbana/Champaign for suggestion and revision. I would also like to thank Jeff Grimmett and Michael Urman for sharing their work, thank Robin Dunn and other members of the Python/wxPython communities for information and help.

REFERENCES

Byrne, M.D., John, B.E., Wehrle, N.S., and Crow, D.C. (1999). The Tangled Web We Wove: A Taskonomy of WWW Use. In Proceedings of CHI '99 (Pittsburgh PA, May, 1999), ACM Press, 544-551.

Cankiri. http://www.tortall.net/mu/wiki/Cankiri

Catledge, L.D. and Pitkow, J.E. (1995). Characterizing browsing strategies in the World Wide Web. In Computer Networks and ISDN Systems 27: 1065-1073.

Choo, C.W., Detlor, B., and Turnbull, D. Working. (1999). The web: An Empirical Model of Web Use. HICSS’33 (Hawaii International Conference on Systems Science). Available at http://choo.fis.utoronto.ca/FIS/ResPub/HICSS/

Eighmey, J., & McCord, L. (1998). Adding value in the information age: Uses and gratifications of sites on the World Wide Web, Journal of Business Research
41(3):187-194.

Igroup: http://igroup.msra.cn/

Jing, F. Wang, C., Yao, Y. , Deng, K. , Zhang, L., & Ma, W.Y. (2006). IGroup: A web image search engine with semantic clustering of search results. International Multimedia Conference: Proceedings of the 14th annual ACM international conference on Multimedia, Santa Barbara, CA, USA.

LogSquare. http://www.mangold.de/LogSquare.16.0.html.

Pitkow, J.E. (1998). Summary of WWW Characterizations. In Proceedings of the Seventh International WWW Conference, Brisbane, Australia. Also available at http://www7.scu.edu.au/programme/fullpapers/1877/com1877.htm.

Reeder, R., Pirolli, P., and Card, S. (2001). WebEyeMapper and Weblogger: Tools for analyzing eye tracking data collected in web-use studies.

Reeder, R., Pirolli, P., and Card, S. (2000). WebLogger: A data collection tool for web-use studies. Technical report number UIR-R-2000-06 online at http://www.parc.xerox.com/istl/projects/uir/pubs/default.html.

Wang, S., Jing, F., He, J., Yang, J. (2007), IGroup: Presenting Web Image Search Results in Semantic Clusters. Proc. SIG CHI’2007, ACM Press.

Python. http://www.python.org/

wxPython. http://www.wxpython.org/



use of this blog

This blog will be used to communicate with my colleague and spread the knowledge of Human Factors and Engineering Psychology. I will publish my researches and writings in HF and Engineering Psychology at this blog. Some of my other training and interest, such as, developmental psychology, attachment, computer science, artificial intelligence, driving, economics and music, may also appear in this site.
I am very happy to discuss with you if you are interested in my writings. Please correspond to hejibo@gmail.com. Thank you for your interest!

about this blogger: He, Jibo

Mr. Jibo He
Graduate Research Assistant

jibohe2@illinois.edu
217-244-4461 (phone)
217-244-8647 (fax)

Address
University of Illinois
3414 Beckman Institute
405 N. Mathews
Urbana, IL 61801

Education
B.S., Department of Psychology, Peking University, 2007
B.S., China Center for Economic Research, Peking University, 2007

Special Interests
Visual cognition, attention, usability, user centered design, human-machine interaction

Selected Articles

Jing, F., Wang, S., He, J., Du, Q., Zhang, L. (2007), Long Query Suggestion List: Prioritized or Organized. Proc. 12th HCI International.

Wang, S., Jing, F., He, J., Yang, J. (2007), IGroup: Presenting Web Image Search Results in Semantic Clusters. Proc. SIG CHI’2007, ACM Press.