The weekly column

Article 58, April 2001

Using Statistical Package for Social Sciences (SPSS) Analytical Solutions for Foreign Language Education

By Dr Leyla Tercanlioglu, The School of Education, Ataturk University, Turkey

Abstract

The paper discusses research on using Statistical Package for Social Sciences (SPSS) analytic solutions to discover what foreign language learners know and predict what they will do. The participants were 135 undergraduate EFL students. At the time of the data collection, students were receiving 2 hours of English Grammar Instruction a week. During the 10^th week of the fall semester, participants were asked to attend an English Grammar test and complete a questionnaire to measure their integrative and instrumental motivations. The findings of the study suggest a number of benefits for the use of SPSS in foreign language classroom.

Introduction

Foreign language teaching programs are charged with developing a deeper understanding of exactly who their students are and what types of curricula, facilities and services they need to be successful in today's diverse and competitive world. These programs have to be sure that institutional effectiveness is achieved.

In particular, the recent enormous boost in information has changed foreign language teaching institutions drastically, challenges have gained a new quality, and it is foreign language teachers especially, with their pedagogic responsibility, who have gained a special role to play. They have to meet this responsibility by familiarizing themselves with the new technology, and by developing new strategies for coping with masses of data and for assessing their value in the educational context.

It is already an accepted fact that statistical thinking will one day be as necessary as the ability to read and write. In foreign language education, as well, these methods will enable decision makers to make informed and better decisions about uncertain situations and to make useful inferences and predictions.

Statistics is a science of methods that are used to collect, analyze, present, and interpret statistical information which assists to make decisions under uncertainties. Statistical methods are used in a wide variety of occupations and help people identify, study, and solve many complex problems. Statistical data analysis provides hands-on experience to promote the use of statistical thinking and techniques to apply in order to make educated decisions in the foreign language education world.

Computers play a very important role in statistical data analysis. The statistical software package, Statistical Package for Social Sciences (SPSS), which is used in this study, offers extensive data-handling capabilities and numerous statistical analysis routines that can analyze small to very large amounts of data statistics.

The purpose of this study is:

• to promote the use of statistical thinking and techniques via a data-oriented approach

• to lead EFL teachers to apply statistical thinking in day to day language teaching activities
• to apply the concept for the purpose of continuous improvement

THE STUDY

Participants

This study was conducted at ELT Department of the School of Education, Atatürk University, Erzurum, in four classes of the third grade. A total of 135 students participated in the study. The number and the percentage of students according to classes are listed in Table 1. As indicated in Table 1, classes 1 and 3 are taught in the evening, whereas 2 and 4 are daytime classes.

Table 1

Breakdown of students according to class

As Table 2 displays, fifty four of the participants were male (%40) and eighty one were female (%60).

Table 2

Breakdown of students according to sex

Data was collected in November 2000. The instruments were administered altogether.

Measures

Instrumental and Integrative Orientation (Gardner, 1985). The scale has in total 8 items (instrumental 4; integrative 4 ) under the question "Why do students want to learn a foreign language?". Examples of statements are as follows: "Studying English is important because it will enable me to better understand English life and culture " (integrative); "Studying English is important because it will give me edge in competing with others" (instrumental). The questionnaire made use of items that were rated on a 7-point scale ranging from strong disagreement (-3) to strong agreement (+3). The scale was administered in English. The internal consistency reliability as measured by Cronbach’s alpha for integrative Orientation was .71 and for instrumental orientation .50.

Achievement measure. Grammar achievement was measured by a TOEFL test which covered 100 questions. The grammar topics fell into two broad categories, Simple Sentences and Complex Sentences. The test contained a mixture of "fill in the blank with the correct answer" questions (Ex.1.) and "identify the incorrect underlined part of a sentence" questions (Ex. 2.) .

Ex. 1. With the passing of time and the encroachment of people, the habitat of gorillas __________ to decrease.

(A) continuing
(B) which continue
(C) continues
(D) that it has continued

Ex. 2.

The internal consistency reliability as measured by Cronbach’s alpha for the test was .8075.

Discussion

In this study, four issues will be examined using SPSS:

• Calculating totals
• Sex influences
• Class influences
• Relations to instrumental and integrative motivations

Calculating totals. Statistical data analysis divides the methods for analyzing data into two categories: exploratory methods and confirmatory methods. Exploratory methods are used to discover what the data seems to be saying by using simple arithmetic and easy-to-draw pictures to summarize data. Confirmatory methods use ideas from probability theory in the attempt to answer specific questions.

To calculate students' total grammar scores, we can use a simple exploratory method - Online Analytical Processing Cubes (OLAP). OLAP provides a sum of total scores and sum of sub-score for each student in alphabetical order. Appendix 1 shows that the lowest score is 40, the highest is 87 and the average score is 62,53. The OLAP Cubes also provides number of cases, mean, median, grouped median, standard error of the mean, minimum, maximum, range, variable value of the first category of the grouping variable, variable value of the last category of the grouping variable, standard deviation, variance, kurtosis, standard error of kurtosis, skewness, standard error of skewness, percentage of total cases, percentage of total sum, percentage of total cases within grouping variables, percentage of total sum within grouping variables, geometric mean, and harmonic mean.

The score percentage might be useful in various ways. It is possible to see the lowest and highest score, how many students scored them and the frequency of its occurrence. For example, Table 3 displays that 13 students (9.6 %) score under 50 and were found weak in grammar.

Table 3

Percentage of scores

If a graphic is needed, the Frequency analyses section also provides charts. Figure 1 shows the distribution of student scores and reveals that most of the students are between 55 and 70.

Figure 1

The histogram of the distribution of student scores

Furthermore, using SPSS enables teachers see the student preferences regarding question types. As noted before the test contained "fill in the blank" and "identify the incorrect underlined part of a sentence" questions. As Figure 2 shows the students are slightly more successful in "fill in the blank with the correct answer" (M= 0.66)questions than "identify the incorrect underlined part of a sentence"(M=0.58). That is, although the same grammar topics were examined, students in our sample failed to reply when the question was phrased in the second type. Empirical research is needed to explain this trend.

Figure 2

Question types

Sex Differences

It might be useful to see if the "mean" of a set of scores male and female students have differ. To measure the average mean score differences attained by different sexes (Table 4), we performed Compare Means analysis. As the table demonstrates, male students were higher. Furthermore, the highest score (87) was attained by a male student.

Table 4

Means of scores by sex

Note. M=Mean; SD=Standard Deviation

Furthermore as explained in the Measures section, Grammar questions were divided into two groups "Simple" and "Complex sentences". We wanted to see if males or females are more successful in complex sentences. Table 5 reveals that male students are more successful in both types and both genders are successful in simple sentence type.

Table 5

Means of male/female student responses to Simple and Complex sentences

It is evident that students prefer Simple Sentences. Recent motivation studies generally assume that there is a form of motivation which is defined as "a tendency to feel successful when work is easy". Additional research is needed to confirm this pattern and to examine their implications for students foreign language learning achievement.

The grammar test contained questions on different topics of grammar. We may therefore consider how a mean analysis could be used to see how means change.

Table 6

Means of male/female student responses to different grammar topics

As Table 6 indicates, students have highest score in connectors and lowest in word-order. Except for infinitive-gerund boys have a higher average in all topics.

Class Differences

We had four classes; two day-time, two evening. It would be useful to see the mean difference between day-time (2 and 4) and evening groups (1 and 3). Analysis of variance, or ANOVA, is an appropriate method of testing the null hypothesis that several group means are equal in the population, by comparing the sample variance estimated from the group means to that estimated within the groups (Table 7).

Table 7

Anovas relating class and score

Table 7 does not display a significant correlation. We might use mean differences as well. The results of mean differences between scores and classes are reported in the form of a graph (Figure 3).

Figure 3

Class differences

Figure 3 reveals that Classes 2 and 4- day-time classes- have highest mean of scores. Since 1975, participation rates in primary and secondary education have been constantly increasing and an increasing number of applicants want to enter a higher education programs. Turkey is faced with the challenge of reconciling quantity with quality in higher education. To create additional capacity, for the last several years several departments at Ataturk University have been offering an evening program. This small scale study reveals that, although the academic staff and material used are the same, the evening program is strikingly less successful. It seems it is crucial to understand why.

Relations between instrumental and integrative motivations

Integrative and instrumental motivation are important constructs which deal with individuals’ reasons for doing or not doing different activities. Integrative motivation refers to an internal state that drives the individual to engage in an activity, being motivated and curious to do an activity for its own sake. Instrumental motivation refers to being motivated in an activity as a means to an end, such as receiving a reward, meeting course requirements or pleasing others. Numerous studies have been conducted on students on these two distinctions of motivation, and there is evidence that both may exert influence on the quality of learning outcomes (Wigfield and Guthrie, 1997).

In this study, we want to understand if the score of the students is related to integrative or instrumental reason to learn a foreign language. Since Pearson’s Correlation Coefficient is a measure of linear association, it is an appropriate statistics for measuring this relation.

Table 8

Correlation between score-integrative motivation and score-instrumental motivation

Correlation coefficient=r

p*<0.05 (2-tailed). p**<0.01 (2-tailed).

As Table 8 shows there is relation between the score of the students and their integrative reasons of 0.04 (p<0.05). The results revealed that students' instrumental reasons for learning a foreign language are more correlated with their scores (p< 0.01). To estimate the linear relationship between students' score and their integrative and instrumental motivation Regression Analysis was performed. Figure 4 shows the relation between students' integrative motivation and their score, and Figure 5 shows the relation between students' instrumental motivation and their score. In both cases, as the students integrative and instrumental motivation increases, their scores increase, as well. The results of the study indicate that students' instrumental motivation is very strong. Lepper and Hodell (1989) suggested that classrooms should avoid undermining integrative motivation with too many instrumental motivation incentives. Instrumental incentives should be avoided because such students prefer to demonstrate a high level of ability to please teachers/parents than to deepen their understanding. Therefore, decision makers have to seek ways that will lead to a long-term increase in integrative motivations for learning a foreign language.

Figure 4

Regression Analysis of relationship between students’ integrative motivation and score

 Figure 5

Regression Analysis of relationship between students’ instrumental motivation and score

Conclusion

To conclude, a critical aspect of managing any educational institution is planning for the future. Good judgment, intuition, and an awareness of the current state may give administrators a rough idea or "feeling" of what is likely to happen in the future. However, converting that feeling into a figure that can be used effectively is difficult. Statistical data analysis may help foreign language teachers forecast and predict future aspects of a educational operation. The most successful foreign language teachers and decision makers are the ones who can understand the information and use it effectively.

The results of this small scale study, with data collected to illustrate how SPSS might be useful, imply that vast amounts of statistical information are available in foreign language education because of continual improvements in computer technology. To compete successfully, foreign language educators must understand and use information effectively.

REFERENCES

Lepper , M. R., & Hodell, M. (1989). Intrinsic motivation in the classroom. In C. Ames & R. Ames (Eds.), Research on Motivation in Educationn: Vol 3. Goals and Cognitions (pp. 73-105). New York Acedemic Press.

Gardner, R. C. (1985). The Attitudes/Motivation Test Battery. Technical Report. University of Western Ontario.

Wigfield, A. & Guthrie, J. T. (1997). Motivation for reading: An overview. Educational Psychologist, 32, 57-58.

About the author

Dr Leyla Tercanlioglu is an assistant professor at ELT Department, the School of Education, Ataturk University, Turkey. Her interests include reading in a foreign language research and computer assisted language learning.

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