The Measurement of Motivation with Science Student

Motivation is an inner force that activates and provides direction to our thought, feelings and actions. Two main characteristics of motivation are goal directed behavior and persistence. Motivated people persistently work for the goal until it is achieved. This paper explores the nature of motivation in the context of learning and seeks to relate it to self-efficacy, self- concept, confidence and self-esteem. Motivation is presented as a ‘second order’ variable be- ing very much dependent on attitudes as well as perceived goals, needs and value. Ways of assessing motivation are considered and the typical use of questionnaire approaches is criticized heavily. These can measure what a person perceives but the perceptions may or may not correspond to reality. Indeed, the entire mathematical basis of data handling with questionnaires is questioned. A typical questionnaire is then used with a large sample of 600 1st and 2nd year science intermediate students, drawn from the province of the Punjab in Pakistan and the data obtained examined statistically. Correlations between the responses patterns in all 30 Likert-type questions were examined using Kendall’s tau-b while Principal Components Analysis, using varimax rotation, looked at the questionnaire overall as well as sub-groups of questions. Correlation values were found to be very low, suggesting no factor structure and, indeed, the factor analysis showed that there is no factor structure with the questionnaire used with this large population. Chi-Square, as a ‘contingency test’, was applied to compare the distributions of responses, gender separated. Gender differences were found only in a minority of questions. It is argued that motivation is highly multi-variate and that no simple factor structure is to be expected. It is also argued that, with ordinal data, following no prescribed pattern of distribution, only non-parametric statistics are appropriate. The traditional approaches are statistically incorrect and, as a result, will often miss key insights.