The following is some information regarding the Lakaev Academic Stress Response Scale (LASRS), which I designed and published, as a means to assess Academic Stress and determine different responses to Academic Stress (e.g., cultural and gender differences). The published 21 item LASRS is currently undergoing further refinements and the results of this research will be posted later this year. The LASRS as been utilised in studies all over the world in countries such as Australia (JCU & QUT), Hong Kong, Singapore, Sri Lanka, India, United Kingdom, Canada, USA, Philippines, and Pakistan.

1. Lakaev Academic Stress
Response Scale
(LASRS)
Natasha Lakaev

2. Introduction
• Stress is pervasive in modern life, particularly among tertiary
students (Misra & McKean, 2000).
• Excessive stress diminishes the somatic and psychological functions
that are necessary for success in higher education, including
attention, cognitive flexibility and physical functioning, and their
down-regulation can paradoxically increase rather than decrease
stress (Baumeister, 1984; Gendolla, 2006).
• The Lakaev Academic Stress Response Scale (LASRS) is a self
administered scale published in 2009 (Lakaev) that measures
students’ affective, behavioural, physiological and cognitive
responses to academic stress in their attempts to maintain
homeostasis.
• The LASRS has been utilised in studies in Queensland (JCU), Hong
Kong, Singapore, Sri Lanka, India, United Kingdom, Canada, USA,
Philippines, and Pakistan.

3. Introduction (continued)
Measures
• The 21 question LASRS was developed specifically for quantifying stress in
university students in the following four stress response domains:
– Physiological, six questions 2, 9, 10, 14, 15 and 21 (e.g., “I had
headaches”);
– Cognitive, six questions 1, 3, 6, 11, 16 and 17 (e.g., “I had trouble
concentrating in class”);
– Affective, seven questions 4, 5, 7, 13, 18, 19 and 20 (e.g., “I felt
emotionally drained by university”); and
– Behavioural, two questions 8 and 12 (e.g., “I yelled at family and
friends).
• Respondents rate how much of the time they experience symptoms on a
5-point Likert-type scale with the anchors None of the Time (1), A Little of
the Time (2), Some of the Time (3), Most of the Time (4), and All of the
Time (5).

4. Background
LASRS Pilot Study (Lakaev, 2006)
• Preliminary 27 item instrument tested on 45 volunteers.
• 45 university students with pending exams and assignments completed the 27
items and Kessler-10, a measure of non-specific psychological distress (K-10+:
ABS, 2003).
• Aim of Pilot Study:
– To develop and test a new questionnaire that measures tertiary students
individual stress response across a number of theorised domains i.e.,
affective, behavioural, physiological, and cognitive stress responses.
• Findings of Pilot Study:
− Principal Components Analysis confirmed 4-factor component structure
of the questionnaire;
− Reliability analysis using leave-one-out procedure suggested the removal
of six items;
− Final 21 items yielded acceptable to excellent internal consistency; and
− This pilot study demonstrated the excellent concurrent and construct
validity, and internal consistency of a 21 item LASRS.

5. LASRS
Pilot Study
27 Item
Questionnaire
(2006)

6. Background
(continued)
Confirmatory Factor Analysis Study (Lakaev, 2008)
• The LASRS was tested on a sample of 375 University students of mixed nationality; and
• The LASRS was subjected to confirmatory factor analysis and further analyses of
validity and reliability in support of the hypothesised 4 stress response domains.
• Aim of 2008 Study:
– That the structure of the questionnaire will align with the 4 stress response
domains, and will account for a significant proportion of the variance in
participants’ stress scores.
• Findings of 2008 Study:
– A series of 2-, 3-, 4- and 5-factor extractions were conducted to compare their
accuracy at explaining the data;
– A 4-factor solution was accepted as it allowed for a succinct structure of items and
factors and accounted for 54% of the variance;
– The factor correlation matrix showed that the factors were not too highly
correlated; and
– All item loadings on the Cognitive factor were negative, suggesting that this factor
was identifying a process other than stress response as seen in the first three
factors.

7. LASRS
2008 Study
21 Item
Questionnaire
(handout)
Published Article:
Lakaev, N. (2009).
Validation of an
Australian academic
stress questionnaire.
Australian Journal of
Guidance and
Counselling 19(1), 56-
70 (ISSN 1037-2911).

8. Background (continued)
Rasch Model Analysis of LASRS (Lakaev, 2012)
• The Rasch measurement theory is a relatively modern psychometric approach for the
development and validation of instruments. It has emerged as a powerful tool for
examining the performance of an instrument in depth, allowing both the instrument as
whole and individual items to be assessed (Kaipper et al., 2010).
• The Rasch model is also helpful in providing potential solutions for mis-performing
instruments (Kaipper et al., 2010).
•Rasch analysis (mathematical modelling) was applied to the total LASRS and its four
subscales (Physical, Behavioural, Affective, and Cognitive stress) to assess the fit of each
LASRS question (item) to their designated subscale. This included a detailed assessment of
the response format, item fit, dimensionality and targeting.
• The suitability of using all items of the LASRS as a measure of general psychological
distress was also explored.
• The data collected from the 2008 CFA analysis was used in this study.
• Results highlighted areas where refinement of the LASRS may be achieved to be able to
use the scale more confidently in clinical and educational settings.

9. • Recommendations from the 2012 Rasch model analysis were
reviewed and the following changes made to the 21 item LASRS:
− Rewording of three items; and
− Adding of 2 new Behavioural items, 2 Physiological items and 1
Affective item.
• The new 26 item LASRS will be assessed via the collection of new
data and application of the Rasch model.
• 2000 students from University campuses will be approached in
public areas and libraries to complete the LASRS along with a
demographic questionnaire.
• The new Rasch model results and refined LASRS will be published in
late 2016.
Future Refinement of LASRS

10. • The following link takes you to the 2009 published article of
the 21 item LASRS:
http://journals.cambridge.org/action/displayAbstract?
fromPage=online&aid=8497682
• For copies of the LASRS scale and permission to use it in
research or studies please email:
natashapsychology@outlook.com
Access to Current LASRS

11. References
• Andrich, D. (1978). Rating formulation for ordered response categories. Psychometrika, 43, 561-573.
• Aron, A., Aron, E. N., & Coups, E. J. (2006). Statistics for psychology (4th ed.). Upper Saddle River, NJ: Pearson
Education Inc.
• Australian Bureau of Statistics. (2003). 4817.0.55.001 Information paper - use of the Kessler
• Kaipper, M. B., Chachamovich, E., Hidalgo, M., Torres, I., & Caumo, W. (2010). Evaluation of the structure of
Brazilian State-Trait Anxiety Inventory using a Rasch psychometric approach. Journal of Psychosomatic
Research, 68, 223-233.
• Lakaev, N. (2006). Development of a stress response inventory for university students. Manuscript. Queensland
University of Technology. Carseldine.
• Lakaev, N. (2008). Different responses to tertiary academic stress: A cross-cultural study. Postgraduate Diploma
in Psychology, Bond University, Varsity Lakes.
• Lakaev, N. (2009). Validation of an Australian academic stress questionnaire. Australian Journal of Guidance
and Counselling, 19(1), 56-70.
• Masters, G. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
• Pallant, J. F., & Tenant, A. (2007). An introduction to the Rasch measurement model: An example using the
Hospital Anxiety and Depression Scale (HADS). British Journal of Clinical Psychology, 46, 1-18.
• Shea, T. L., Tenant, A., & Pallant, J. F. (2009). Rasch model analysis of the Depression, Anxiety and Stress
Scales (DASS). BMC Psychiatry, 9(21).
• Smith, E. V. (2002). Detecting and evaluation the impact of multidimensionality using item fit statistics and
principal components analysis of residuals. Journal of Applied Measurement, 3, 205-231.
• Tennant, A., & Pallant, J. F. (2006). Unidimensionality matters. Rasch Measurement Transactions, 20(1), 1048-
1051.
• Wang, W.-C., & Chen, C.-T. (2005). Item parameter recovery, standard error estimate and fit statistics of the
WINSTEPS program for the family of Rasch models. Educational and Psychological Measurement, 65, 376-404.