Effects of age differences on Statistics Anxiety and attitude among undergraduate students

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Effects of age differences on Statistics Anxiety and attitude among undergraduate students

Abstract

Statistical anxiety is proving to be a menace in an educational setting especially for students pursuing a statistics-related course. It is exacerbated when students in non-statistical degree programs like sociology and humanities. The age of students has shown disparities in the group of students where older students have been shown to have greater anxiety than the younger students.

Effects of age differences on Statistics Anxiety and attitude among undergraduate

Apprehension to statistical data is by no means a rarity, as it falls under what psychology refers to as “statistics anxiety.” As bizarre as the term may seem, its reference to a person’s discomfort over dealing with anything about statistics attaches with it a cause of concern relating to the ability to present and interpret valuable data. However, according to Onwuegbuzie & Wilson (2003), about 80% of social and behavioural sciences students still experiences statistics anxiety. Thus, this negatively affects the student’s academic performance, including his/her psychological and physiological conditions (Zehra Ali & Iqbal, 2012). In this sense, the focus of this study is the group of students in non-statistical courses like sociology and humanities. The participants will be categorized into traditional and non-traditional students based on their age differences.

The importance of learning more about statistics anxiety relies on the essence of expanding statistics literacy, which is highly important in a democratic setting, where everyone’s input impacts the course of action (Chew & Dillon, 2014), considering the field’s instrumental contribution to the social sciences. Following fkee & Sulaiman (2014), statistical anxiety can be viewed as an obstacle to the student’s learning. Moreover, it is also found out that there is a significant relationship between intolerance of uncertainty and the age of student that highly promotes statistical anxiety (Legum et al., 2013; Williams, 2015). In the light of age demographics, according to Legum et al. (2013), there is a statistical significance between age and the levels of statistics anxiety where older students have shown greater statistical anxiety as opposed to the younger ones.Also student’s attitude towards statistics course may influence his/her performance. According to Sese’ (2015), attitude shows a strong direct analyst of performance and plays a great mediating role within the student’s academic performance. Students who have negative attitudes and unfavourable attitudes may contribute anxiety and low efficiency towards statistics (Dr. Eduljee & LeBourdais, 2015).

However, in a positive view, statistics anxiety is also a necessary provocation to help students to achieve optimal performance ((Dykerman, 2011); (Chei-Chang, Yu-Min, & Li-Tze, 2014); Macher et al., 2012).

The following hypotheses were constructed in the light of the above review:

• There is a significant association between age demographics and statistics anxiety
• Statistics anxiety heavily influences a student’s attitude towards statistical courses.

Many have come to appreciate the importance of literacy in statistics. This has resulted in an increasing number of statistical courses in various degree programs. According to Amanda (2010), close to 80% of the graduates, exhibit statistics anxiety especially in the fields of social and behavioral sciences. As a result, students usually procrastinate their statistics courses because of such fear (Chew & Dillon, 2015). Chew and Dillon (2014) asserts that empirical data provides evidence that those students who are in non-mathematical degree programs claim that statistics and related courses are the ‘most anxiety-inducing courses’ (Chew & Dillon, 2015). As the literature asserts, students in the non-statistical courses are more or less fearful of statistical courses infused in their degree programs (Chew & Dillon, 2015). This paper seeks to investigate the association between age and statistical anxiety in relation to the consequential development of attitude towards statistical courses.

Anxiety refers to worry and fear that an individual develops in anticipation of a threat even though the outcome is uncertain. In this sense, the individual exhibits fear and worry but is unable to point out the source of the anxiety (Jordan, McGladdery & Dyer, 2014). Regardless, he or she expects something harmful and even painful. The outcome of such behavior results in individuals avoiding likely sources of the anguish (Brown & Tallon, 2015). According to Amanda (2010), anxiety also includes the perception of our inability to control future events and that they might be related to several different events. As a result of the lack of control over the future events, anxiety might occur because people worry that things might happen and cause pain or stress (Bui & Alearo, 2011). Anxiety in the academics has been acknowledged extensively and has been asserted to take several forms including mathematics and statistics anxiety. According to Amanda Williams (2013), statistics anxiety has been considered to be “a multi-dimensional construct.” The six components of statistics anxiety include “worth of statistics, interpretation, test, class anxiety, computation self-concept, and the fear of asking help” (Williams, 2013). The aspect of interpretation anxiety refers to the fear and worry developed when students are faced with the task of interpreting the statistical results. Those with high scores on this aspect find statistics to be very provoking (Dykerman, 2011).

Several researchers have correlated it with age differences where they assert that older students exhibit more fear and worry when it comes to mathematical and statistical courses (Morsanyi et al., 2016). A study by Ngoc and (2011) involving 104 participants discovered that older students depicted the highest levels of anxiety as opposed to the younger ones. However, what stands out clear is that statistics anxiety depicted a direct correlation with age (Devaney, 2010). In other words, the traditional students are more anxious when compared to the non-traditional ones.

Methodology

The literature review pointed out that the two groups of students who are to be studied were the traditional and non-traditional students. Following their definition, the traditional students are those who studied mathematics or statistically related courses at the 11th year or below. The non-traditional students, on the other hand, are those who studied after the 11th year which includes the 12th year and university level. The desired sample size was 100 students but downsizing was solely for convenience purposes. A total of 50 students, 26 males and 24 females were involved in the data collection process of this study. Their ages varied from 18 years to 30 years. This is because students older than 30 were present in the university environs but most of them are not undergraduates.

This study involves assessing students to measure their levels of anxiety concerning taking statistical courses. During the data collection process, students were approached randomly regardless of their degree program.

The data was collected via a questionnaire which contains only one open-ended questions. Firstly, the student is to provide a general comment about the course expectations followed by an anxiety scale trying to score their level of anxiety when it comes to the course (Welch et al., 2015). The Likert scale is next component used to measure the attitudes of students followed by the demographic information.

 Results

		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	26 and below	30	60.0	61.2	61.2
	Above 26	19	38.0	38.8	100.0
	Total	49	98.0	100.0
Missing	Missing Values	1	2.0
Total		50	100.0

Table 1: Descriptive statistics for the age variable

		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Year 11 and Below	14	28.0	28.0	28.0
	Year 12 and Above	36	72.0	72.0	100.0
	Total	50	100.0	100.0

Table 2: The level of mathematics education

	CommentAttitude	N	Mean	Std. Deviation	Std. Error Mean
The Approximate attitude of each student towards statistics.	Positive	34	31.0588	3.77344	.64714
	Negative	16	22.1875	.83417	.20854

Table 3: Group statistics for the independent t test for the student attitude variable.

		Levene’s Test for Equality of Variances
		F	Sig.
The Approximate attitude of each student towards statistics.	Equal variances assumed	66.509	.000
	Equal variances not assumed

Table 5: The independent t test values for the attitude variable

	CommentAnxiety	N	Mean	Std. Deviation	Std. Error Mean
The Approximate levels of anxiety for each student.	High Anx	11	36.64	1.567	.472
	Low Anxi	37	28.70	3.527	.580

Table 6: Group statistics for the independent t test for the level of anxiety

		Levene’s Test for Equality of Variances
		F	Sig.
The Approximate levels of anxiety for each student.	Equal variances assumed	4.692	.036
	Equal variances not assumed

Table 7: The independent t test for the anxiety variable

		CommentAnxiety		Total
		High Anx	Low Anxi
The Students’ age.	26 and below	7	23	30
	Above 26	5	14	19
Total		12	37	49

Table 8: A crosstab showing the comparison of the students’ age and levels of anxiety

		CommentAttitude		Total
		Negative	Positive
The Students’ age.	26 and below	7	23	30
	Above 26	9	10	19
Total		16	33	49

Table 9: A crosstab showing the comparison of the students’ age and attitude

 

		CommentAttitude		Total
		Negative	Positive
The Students’ age.	26 and below	7	23	30
	Above 26	9	10	19
Total		16	33	49

Table 10: A crosstab showing the students’ level of math education and their attitudes

		CommentAnxiety		Total
		High Anx	Low Anxi
The highest level of Math studied.	Year 11 and Below	3	11	14
	Year 12	4	11	15
	University	6	15	21
Total		13	37	50

Table 11: A crosstab showing the students’ level of math education and their level of anxiety

Discussion

Following the study, the age variable was categorized into two groups, those aged 26 years and below, and those above 26 years. The former group refers to the non-traditional students whereas the latter comprised of the traditional ones. 60% of the sample was above 26 years whereas the remaining 40% were above 26 years. Also, 7 out of 30 students aged below 26 years depicted high levels of anxiety while 5 out 19 students aged above 26 years exhibited anxiety. 23 of 30 students remaining aged below 26 years showed low stress levels and 14 of 19 students above 26 years were also not anxious. 7 out 30 students aged below 26 years portrayed a negative attitude while the remaining 23 had a positive attitude. The non-traditional students were more fearful of statistics than the traditional ones. This could be attributed to the fact that the older students had learned about statistics in their recent past coupled with the fact that they also have higher levels of mathematical education (González et al., 2016). For those over 26 years, 9 out of 19 students had a negative attitude while the remaining 10 had a positive attitude. As such, more students with a positive attitude exhibited low levels of anxiety while the few who had a negative attitude had high levels of anxiety and stress.

Following the independent t-test, there were significant differences between the sample group means considering that a significant value of 0.036 was achieved which is less than 0.05. There were several groups obtained from the data extrapolated from the variables and they include high levels of anxiety, low anxiety levels, positive and negative attitude. This statistical finding could be explained by the fact that students with higher levels of anxiety developed a negative attitude towards statistics and vice versa. This study is significant and can prove to be helpful in the development and implementation of interventions for students with statistics anxiety in a bid to reduce the dislike for the non-mathematical courses taking statistics (Morsanyi et al., 2016).

Reference

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