THE INVENTORY FOR THE STUDY OF SCIENTIFIC ACTIVITY MOTIVATION

Разина Т.В.

ORCID: 0000-0002-0723-7479, Кандидат психологических наук, Филиал Московского психолого-социального университета в г. Ярославле

МЕТОДИКА ДЛЯ ИССЛЕДОВАНИЯ МОТИВАЦИИ НАУЧНОЙ ДЕЯТЕЛЬНОСТИ

Аннотация

Представлена методика, предназначенная для диагностики мотивации научной деятельности. Приведены результаты психометрических проверок. Общий объем выборки составил 781 научных сотрудников, работающих в вузах и научных центрах.

Ключевые слова: методика, мотивация, научная деятельность.

Razina T.V.

ORCID: 0000-0002-0723-7479, PhD in Psychology, Moscow Psychology and Social University, Yaroslavl Branch

THE INVENTORY FOR THE STUDY OF SCIENTIFIC ACTIVITY MOTIVATION

Abstract

Here we present inventory intended for diagnostics of motivation of the scientific activity of adult researchers. The results of psychometric testing of inventory are presented. The total sample size was 781 subjects: researches from universities and research centers.

Keywords: motivation, inventory, scientific activity.

 

Background

Despite the fact that there exist publications that attempt to uncover the nature of scientific activity [1, 2] and create psychological conditions for its effective implementation [4, 6], research on the psychological analysis of scientific activity is still very insufficient, and accordingly, there are difficulties in identifying its specific motives. Therefore, the number of specialized techniques aiming at scientific motivation assessment is very limited. S.M. Glynn et al. developed Science Motivation Questionnaire II (SMQ) aimed at diagnosing five groups of motivations, i.e. Intrinsic, Career, Self-determination, Self-efficacy and Grade motivation [3].

In this paper we present our Motivation of Scientific Activity Inventory (MSAI). As its base, MSAI has three main principles:

1. Psychological analysis of scientific activity.
2. Specificity, i.e. it’s suitability for scientists.
3. A comprehensive coverage of all possible motives of scientific activity.

Our analysis of the scientific motives described in different research studies leads us to conclude that almost any human motive can have a motivating potential in scientific activity. Accordingly, the number of scientific motives is potentially unlimited. We understand the motivation of scientific activity as a system that combines all the potential motives into 10 subsystems (represented as scales in our technique).

In our publication, we define intrinsic motivation as the pleasure, derived by a person from scientific research, as well as from its anticipation, the interest in the process and the result, the feeling of self-realization in science.

         The extrinsic motivation in our view combines a variety of motives predominantly including pursuit of a career and certain social factors, such as desire for a stable social condition, scientific degree, title, position, a stable financial position, social obligations, convenience or a certain inertia of habit, ambition and pride.

We treat the cognitive motivation as an incentive aimed at obtaining fundamentally new knowledge in science or related field, based on the emotion of "pure" (i.e. not linked to practical use) interest; an influx of any new knowledge provokes a new wave of cognitive activity.

We define achievement motivation as the desire to achieve maximum end results, solve complex non-trivial scientific problems, look for new solutions to technical problems that previously seemed insolvable, all this in shortest terms.

We determine safety motivation in scientific activity as the desire to avoid the negative impact of organizational factors (dismissals, obstructions in the course of doing one’s doctoral thesis, censure on the part of significant colleagues, etc.), to reach a relatively stable social and scientific status, get a guarantee of his/her integrity in the scientific establishment, earn the reputation of a man/woman who makes no mistakes, either in science or administrative work.

We define rivalry motivation in science as the desire for championship in science in conjunction with the neutralization of rivals in reaching the goal. Rivalry motivation may occur at the interpersonal level, at the level of scientific centers, research schools, as well as at the level of states and countries.

We define axiological motivation as the stimulating effect of value orientations and personality ideals in scientific activity. In potention, any human or scientific value (humanism, beauty, truth, justice, etc.) or a combination of them could form the basis for such a motivation.

We define reflection motivation in science as self-motivation, self-control, goal-setting, self-determination in scientific activity, i.e. self-stimulation of the scientist to scientific work.

Anti-motivation can be described as a motivation of overcoming or “counter-motivation". Scientific activity is chosen as profession, sometimes for life, in order to overcome a range of internal conditions, and prove to oneself and others one’s scientific independence and self-fulfillment.

According to our conception, indirect motivation includes all the motives that are not directly related to scientific activities, does not stimulate it directly, in fact, it is external, non-scientific motivation, aiming at achieving external goals through research activities.

Participants

A total of 781 subjects participated in the psychometric testing. The average age was 42.6 years, the average experience in research work was 18.9 years. Total men – 48.5%, women – 51.5%. Among them, without degree – 28.9% (including undergraduates, postgraduates, engineers (R&D). Total of persons with a candidate's degree – 61.2%, persons with a doctor's degree – 9.9%. Respondents by workplace: academic research institutes staff – 34.5%, higher education institutions – 59.6%, commercial R&D organizations – 5.9%.

Method

Originally, each motivational subsystem was tied to 10 inventory items reflecting its most typical manifestations in scientific activity. These manifestations had been identified in our preliminary empirical studies, and also based on motivation studies of other researchers. For some items statements of famous scientists were used. 75 percent were ‘direct’ items, the rest being ‘reverse’ ones. Each item should have been evaluated on a 7-point response scale: 1-Absolutely wrong; 2-Wrong; 3-Rather wrong; 4-I don't know; 5-Rather true; 6-True; 7- Absolutely true.

Exploratory factor analysis is designed for situations where the relationships between the observed and latent variables are uncertain. We used exploratory factor analysis to examine how to relate to questions of inventory with certain theoretical factors (subsystems). All of the items  met the criterion of loading at least 0.35 on their respective factor [5].

The difficulty of the task was determined by calculating the percent of the subjects who chose the answer corresponding to the uncertainty category ("I don't know"). If more than 25% of subjects fell into this category, the task was considered subjectively difficult and non informative and discarded. In some cases, item difficulty was discarded in favor of discrimination, and the item was included into the final version of MSAI. Full version of the MSAI includes 70 questions (7 for each scale).

The most of the scales show satisfactory retest reliability values (r ≥ 0.7), indicating a high stability of MSAI test results over time. The only exceptions are the scales of extrinsic motivation, achievement motivation, and anti-motivation, all of them showing a somewhat weaker relationship between the dimensions; however, these coefficients also have high p-values.

Split-half test reliability was verified, both for the overall test score (overall level of MSA). Spearman-Brown formula was used (r=0.83), as well as Rulon formula (r=0.82). For internal consistency i.e. MSAI intertasks consistency measurements, Alpha coefficient for multiple-response items was used (0.82, p ≤ 0.01).

The results of the empirical validity check of MSAI confirm our theoretical assumptions and the empirical findings obtained previously by other researchers. Data on Achievement Motivation, Value, Intrinsic and Cognitive Motivation Scales do positively correlate with productivity measures (with low values, but high significance). Hence the empirical validity of MSAI may be considered satisfactory.

On the criterion of factor structure repeatability, MSAI also shows consistent results, which allows to consider it a valid diagnostic tool. Here, good reliability is demonstrated by Intrinsic, Cognitive and Indirect Motivation Scales. The rest of the scales in one or two cases also show satisfactory variance results. Only Extrinsic and Reflection Motivation Scales never showed dispersion values exceeding 0.6. Perhaps this problem will be overcome by the use of larger arrays of empirical data, also the specificity of the motivational phenomena underlying these scales, can not be excluded. Thus, MSAI showed a satisfactory level of validity and reliability, and can be used as a diagnostic tool for research purposes.

The normalization was carried out using a STEN score scale.

Conclusions

The quantitative results of the psychometric tests show that MSAI can be used as a quality measurement tool for research purposes. MSAI compensates for the lack of psychodiagnostical instruments designed to diagnose the specific motivation of a specific sphere of human activity.

References

1. Clement J. J. Creative model construction in scientists and students : The role of imagery, analogy, and mental simulation. – Dordrecht : Springer Science + Business Media B.V. – 2008. – 601 р.
2. Frigotto M. L., Riccaboni M. A few special cases: scientific creativity and network dynamics in the field of rare diseases // Scientometrics. – 2011. – Vol.89. – Issue 1. – P. 397–420.
3. Glynn S.M., Brickman P., Armstrong N., Taasoobshirazi G. Science motivation questionnaire II: validation with science majors and nonscience majors // Journal of research in science teaching. – 2011. – Vol.48. – №10. – P. 1159–1176.
4. Nonaka I., Toyama R., Konno N. SECI, Ba and leadership: a unified model of dynamic knowledge creation // Long range planning. – 2000. – Vol.33. – P. 5–34.
5. Tabachnick B.G., Fidell L.S. Using multivariate statistics. – 6-th edition, Boston: Pearson, 2013. – 983 р.
6. Wierzbicki A. P. Creative environments: Issues of creativity support for the knowledge civilization age / ed. by Andrzej P. Wierzbicki, Yoshiteru Nakamori. − Berlin ; Heidelberg : Springer, 2007. – 509 p.