Investigating the Attitudes and Concepts of Student Life at the University of Hertfordshire - Lab Report Example

Lab Report: Investigating the Attitudes and Concepts Life at the of Hertfordshire of Hertfordshire The aim of this research was to investigate the attitudes and concepts of student life at the University of Hertfordshire. In the process, a scale was also developed from the various subscales used in analysing the qualitative data recorded in the form of likert scales. Sub-scales that did not meet the 0.7 threshold on the Cronbach’s alpha were eliminated from the subsequent analysis. The researcher used a qualitative approach (semi-structured interviews and a questionnaire) to collect useful information for the analysis. Respondents were classified based on age (below 22 years – younger, and 22 years and beyond - older) as well as using gender lines. Older and younger students were found to differ significantly in their concepts of university life (t = 2.25, p = 0.043). Equally, males and females differed significantly in their concepts of university life (t = 10.22, p = 0.004). Older and younger students did not differ significantly in terms of how positively or negatively they view university life. Similarly, there were no significant differences in terms of how positively or negatively students of opposite gender viewed university life. More males than the females (87.5% against 50.0%) believed that they had a positive attitude towards university life. The percentage (75%) of students above 21 years of age who believed they had a positive attitude towards university life was also higher than that of those below 22 years (62.5%). Introduction This research uses qualitative methods of data collection to investigate the attitudes and concepts of student life at the University of Hertfordshire. This research aims to respond to the following questions that relate to student life and student satisfaction with the services offered at the university. General Introduction to Qualitative Research Methods In order to carry out a successful research study, Kelly, Lesh and Mason (2002) underscored the need to choose a suitable research methodology – one that is guided by the best practices in the field of research. For this reason, it is necessary that the researcher employs a suitable research design. A research design is a formulation of a research, right from the exploratory stages to the more advanced stages such as experimentation, data collection, data analysis, and even data presentation (Creswell, 2003). Creswell noted that in order for a researcher to produce consistent results, it is paramount that they review literature lent through similar research questions in order to understand the general procedures involved in the research. There are three basic elements of research that guide the work of a pioneer or subsequent researchers. The first element is the philosophical assumption(s) that guide a research study. Apparently, the researcher needs to establish the framework within which to base their study. This is partly guided by the philosophical assumptions of what is considered to constitute knowledge claims in the particular venture (Creswell, 2003). For instance, in the current study, the factors that constitute student views are established through development of a framework to guide measurement of attitudes and concepts. While underlining the use of qualitative and quantitative research methods for assessing a study’s claims, Higgins (2009) observed that the difference between the two approaches is the basic foundation about which research design is based. Quantitative methods involve the use of quantitative data at some point for analysing and digging into the subject matter. More importantly, the researcher quantifies the situation to be able to measure and present it by numerical means; a means that underlines the magnitude and size of the effect being assessed. On the other hand, qualitative methods present a feel of the situation by delving into the textual data that cannot be easily coded into numerical data. There are several types of qualitative data analysis approaches. This investigation is especially concerned with the use of semi-structured interviews and questionnaires for development of a suitable scale for measuring students’ attitudes and concepts regarding university life. Semi-structured Interviews: the Concept of University Life A semi-structured interview is a method of research used in the social sciences that provides a respondent in a sample with a rigorous set of questions that do not allow deviation from a set guideline for responses (Campbell, Quincy, Osserman & Pedersen, 2013). The interviewer provides the interviewee with a framework of themes that he intends to explore (Woods, 2011). In relation to the current research, the researcher intends to investigate the following six research questions using semi-structured interviews. 1) Do older and younger students differ in their concepts of university life? 2) Do older and younger students differ in terms of how positively they view university life? 3) Do older and younger students differ in terms of how negatively they view university life? 4) Do males and female students differ in their concepts of University life? 5) Do male and female students differ in terms of how positively they view university life? 6) Do male and female students differ in terms of how negatively they view university life? There are several reasons why the school climate has become a significant component of research among social scientists and educationists. The main reason is that the environment has been touted as a prime facilitator for educational excellence (Thapa, Cohen, Higgins-D’Alessandro & Guffey, 2012; Higgins, Hall, Wall, Woolner & McCaughey, 2005). As such, researchers have devised various approaches of studying the environmental factors that matter to a learner’s environment at school. One such approaches is the use of semi-structured interviews (Blackmore, Bateman, O’Mara & Loughlin, 2011). In the present study, the likert scale is used as a preferred measure of student satisfaction with various services. Questionnaire: Attitudes to University Life A questionnaire is a popular research instrument that consists of an array of questions and other information seeking prompts for acquiring information from respondents in a survey (Rattray & Jones, 2007). The commonality of questionnaires stems from their efficacy and ease of use, as the respondent gets ample time to read through the questions and prepare their answers in a thoughtful manner. However, the fact that respondents at times carry them to answer at a time of their own convenience has been partly described as a leading factor for information distortion, as respondents often try to create a pattern that may not represent the truth in an effort to appear consistent (Rattray & Jones, 2007; Bird, 2009). Luckily, several data quality control techniques have been devised to get rid of misleading information when such cases are detected. In the present investigation, questionnaires were used to collect information about students’ attitudes towards university life in a bid to respond to the following two research questions: 1) Do student attitudes towards life at the university differ in line with their gender? 2) Do student attitudes towards life at the university differ in line with their age groups? The questionnaire provided a suitable choice for recording the opinions of respondents since it serves as a reference material and it enables recording of non-sequenced information. For instance, the responses offered for answering the question on students’ attitudes can be classified as nominal, since they do not follow a particular sequence/ hierarchy or order of increasing values. As such, the data collected is not experimental and it would be necessary for respondents to answer the questions themselves (Hadjazi & Omidi, 2008). The questionnaires can also be used to incorporate extra remarks from the respondents, which provides extra insights into their views (Chowa, Masa, Ramos & Ansong, 2013; Ali, Haider, Munir, Khan & Ahmed, 2013). Methods Respondents Questionnaire. This is the group of participants that completed the issued questionnaires. The main demographic details about this group are tabulated below (table 1). The average age of respondents in this category was 22.13 (SD = 4.113) years, with the youngest being 18 years and the oldest 35. Table 1. Respondents’ distribution based on gender and age. Criteria Frequency Percentage Gender Male 8 50 Female 8 50 Age 21 or younger 8 50 22 and above 8 50 Interview. This is the group of participants who participated in the interview. The sample comprised 25 individuals, and the data was used to investigate the testing scale for the questionnaire group. The sample is a subset of the questionnaire group. Table 2 below shows the gender distribution characteristics of the sample. Based on descriptive analysis, the average age of the participants was 24.8 (SD = 4.61) years, with the youngest participant being 21 years and the oldest 42. Table 2. Participants’ distribution by gender. Frequency Percent Valid Percent Cumulative Percent Valid Male 4 16.0 16.0 16.0 Female 21 84.0 84.0 100.0 Total 25 100.0 100.0 Design A qualitative approach was used to collect information from participants that would help in the data analysis. To collect the data, a semi-structured interview was used. The interview was used to record information pertaining to the concept of university life. Some of the questions explored participants’ positive and negative views scores on university life. Additionally, a questionnaire was used to collect information regarding participants’ attitudes towards university life. The tool was used for recording nominal data corresponding to participants’ attitudes. Materials Interview. The interview sample was identified and the researcher organized a meeting within the university’s psychology lab to collect information about participants’ concept of university life. The meeting was organized exactly one week after the notification was issued to willing participants. Each participant responded to a set of questions as outlined in the interview script. For the sake of anonymity, each participant replied to the interview questions only in the presence of the interviewer. Questionnaires. The questionnaires were issued at the same time when interviewees were notified of the pending interview in a week’s time. As such, they had to submit the questionnaires after one week of receiving the unfilled copies. The filled copies were submitted to the university’s information department in envelopes addressed to the researcher. A 5-point likert scale was used to identify participants’ responses to individual questions pertaining to their concept of university life. Fifty questions categorized into 5 subareas (teaching, resources, social, finance, and support) were incorporated for potential answers. Procedure Interview. The participants were introduced to the study through random identification, when each potential respondent was briefed about the intended interview. Each participant was slotted 15 minutes of interview to avoid time wastage and overlapping interviews. Questionnaire. The original questionnaire was designed after consulting several pieces of literature on student life; much of which was focused on factors that necessitate improved performance at school. The five sub-items were identified through this process. The main factor considered when formulating the current questionnaire was relevance to university life, since a deviation from some levels of study (for instance, high school) was needed. Consent was obtained from the department of psychology to undertake the study and accompanying interviews when the fair copies of the questionnaire had been developed. The respondents were selected randomly from various courses, and approached for detailed overview of the need for the interviews/ study. They were also asked to confirm willingness to continue with the study, and informed of their right to withdraw from the initiative at any time. Respondents filled the questionnaires at their own time within the provided one week. Reliability analysis was performed to establish whether the different subscales conform to the standard threshold for reliability (0.7) as emphasized by Tavakol and Dennick (2011). The application of this particular reliability measure is based on the assumptions that the items meet the theoretical reliability of a scale, that the skewness of the data does not vary, and the number of response options does not wane (Gadermann, Guhn & Zumbo, 2012). Items that fail to meet the basic reliability threshold are likely to distort the results, a failure that the Cronbach’s alpha attempts to safeguard against (Tavakol & Dennick, 2011). However, special consideration was given to items whose value of alpha was greater than 0.696, in an effort to safeguard against excessive removal of subscales’ items. The actual table representing the per-item reliability analysis is provided in the appendix (Appendix 1). Based on this analysis, two items under the academic teaching scale were removed alongside three under the academic resources, one under social items, two under financial items, and three under the support services subscales; a total of eleven out of the fifty items. Consequently, the unreliable eleven items identified through this analysis were eliminated from subsequent analysis. Description of the Final Scale Table 1 below shows the various characteristics of subscales retained for the analysis. Table 3. Descriptive statistics for the subscales and the overall scale. N Mean Std. Deviation Alpha Teaching 25 28.6800 3.02379 0.639 Resources 25 21.6000 2.67706 0.612 Social 25 26.9200 2.91433 0.665 Finance 25 23.6000 2.44949 0.639 Support 25 20.8400 2.65644 0.608 Overall 25 121.6400 6.77545 0.219 Valid N (listwise) 25 Based on table 3 above, the mean score for the subscale “academic teaching” is 28.68 (SD = 3.02), “academic resources subscale has a mean of 21.6 (SD = 2.68), social services subscale has a mean of 26.92 (SD = 2.91), finance subscale has a mean of 23.60 (SD = 2.45), and support services subscale has a mean of 20.84 (SD = 2.66). The “total” scale has a mean of 121.64 (SD = 6.76). Table 4. Report of scale norms – based on gender. Gender Teaching Resources Social Finance Support Overall Male Mean 27.2500 22.0000 27.5000 23.5000 22.2500 122.5000 N 4 4 4 4 4 4 Std. Deviation 3.50000 2.16025 1.29099 3.00000 2.62996 7.04746 Female Mean 28.9524 21.5238 26.8095 23.6190 20.5714 121.4762 N 21 21 21 21 21 21 Std. Deviation 2.94068 2.80391 3.14037 2.41819 2.63764 6.88926 Total Mean 28.6800 21.6000 26.9200 23.6000 20.8400 121.6400 N 25 25 25 25 25 25 Std. Deviation 3.02379 2.67706 2.91433 2.44949 2.65644 6.77545 From table 4 we learn that females (mean = 28.95, SD = 2.94) had a higher average score on the academic teaching subscale than males (mean = 27.25, SD = 3.5). Females also scored higher on the finance subscale (mean = 23.62, SD = 2.42). Across other subscales, males scored slightly higher than females; academic resources (males: mean = 22.00, SD = 2.16; females: mean = 21.52, SD = 2.80), social items (males: mean = 27.50, SD = 1.29; females: mean = 26.80, SD = 3.14), and support services (males: mean = 22.25, SD = 2.63; females: mean = 20.57, SD = 2.64). Overall, males (mean = 122.50, SD = 7.05) scored higher than females (mean = 121.48, SD = 6.89). Results Descriptive Analysis Descriptive analysis was performed to establish the underlying features of the data. The results are summarized below. From table 2 above, 4 (comprising 16%) of the participants were male while 21 (84%) were females. As demonstrated in table 4, two (8%) of the participants were living on campus (De Haviland), and 4 (16%) were living off campus (Hatfield). The majority (19 – comprising 76% of the sample) were living in their homes. Apart from criminology and economics that had 2 (8%) participants each, all other participants were drawn each from a different field of study. Table 5. Participants’ distribution by accommodation. Frequency Percent Valid Percent Cumulative Percent Valid On Campus (De Haviland) 2 8.0 8.0 8.0 Off Campus (Hatfield) 4 16.0 16.0 24.0 Off Campus (Home) 19 76.0 76.0 100.0 Total 25 100.0 100.0 For the sample that was interviewed, the number of males (8 = 50%) was equal to the number of females (table 1). The respondents’ ages were divided into either 21 or lesser years and 22 or more years. Based on this criterion, 8 (50%) of the respondents fell in the group below 22 while the remaining 8 were at least 22 years. These results are displayed in table 5 below. Table 5. Distribution of respondents based on gender and age. The average age for the respondents was 22.13 (SD = 4.11) with the lowest and highest ages recorded being 18 and 35 years respectively. Testing the Hypotheses – Inferential Statistics The research aimed to answer the questions posed in the introductory section of this paper. Accordingly, the six hypotheses emanating from these research questions have been tested in this section. H1: Older and younger students do not differ significantly in their concept of university life. Table 6. Average scores for age groups on the concept of university life score. Age Category N Mean Std. Deviation Std. Error Mean Summation Concept 21 or younger 8 12.8750 7.97205 2.81855 22+ 8 24.0000 11.46423 4.05322 Table 7. Independent samples test for concept of university life score based on age groups. Levenes Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error 95% CI Lower Upper Sum. Concept Equal variances 0.853 0.371 -2.253 14 0.041 -11.125 4.937 -21.713 -0.5365 Unequal variances -2.253 12.5 0.043 -11.125 4.937 -21.835 -0.4148 Based on tables 6 and 7 above, the total concept “score” for individuals in the upper age group (mean = 24.0, SD = 11.46) was significantly higher (t = -2.253, p = 0.043) than that of the under 22s (mean = 12.875, SD = 7.97). H2: There is no significant difference between older and younger students’ positive views of university life. Table 8. Average scores for age groups on positive evaluation scores on university life. Age Category N Mean Std. Deviation Std. Error Mean Positive Evaluation 21 or younger 8 3.2500 1.03510 .36596 22+ 8 3.2500 1.03510 .36596 Table 9. Independent samples test for positive evaluation based on age groups. Levenes Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error 95% CI Lower Upper Positive Evaluation Equal variances 0.00 1.000 0.00 14 1.000 0.000 0.5176 -1.11003 1.11003 Unequal variances 0.00 14 1.000 0.000 0.5176 -1.110 1.110 The results presented in tables 8 and 9 above indicate the students’ positive evaluations of university life based on age. The two age groups had equal average positive evaluation scores (mean = 3.25, SD = 1.0351). As such, the scores are not different (t = 0, p = 1.00). H3: There is no significant difference between older and younger students’ negative views of university life. Table 10. Average scores for age groups on negative evaluation scores on university life. Age Category N Mean Std. Deviation Std. Error Mean Negative Evaluation 21 or younger 8 2.0000 1.06904 .37796 22+ 8 1.7500 1.28174 .45316 Table 11. Independent samples test for negative evaluation based on age groups. Levenes Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error 95% CI Lower Upper Negative Evaluation Equal variances 0.500 0.491 0.424 14 0.678 0.25000 0.5901 -1.0156 1.5156 Unequal variances 0.424 13.56 0.678 0.25000 0.5901 -1.0195 1.5195 From tables 10 and 11 above, students 21 years and below had a higher negative evaluation score for school life (mean = 2.00, SD = 1.069) than the older group – 22 years and above (mean = 1.75, SD = 1.282). However, the difference between the two average scores is not statistically significant at the designated level of significance (t = 0.424, p = 0.678). H4: Male and female students do not differ significantly in their concept of university life. Table 12. Average scores for gender groups on the concept of university life score. GENDER N Mean Std. Deviation Std. Error Mean Sum Concept male 8 26.0000 10.41976 3.68394 female 8 10.8750 5.13914 1.81696 Table 13. Independent samples test for concept of university life score based on gender. Levenes Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error 95% CI Lower Upper Sum Concept Equal variances 1.409 0.255 3.682 14 0.002 15.125 4.1077 6.3150 23.9350 Unequal variances 3.682 10.22 0.004 15.125 4.1077 5.9987 24.2513 Tables 12 and 13 above indicate students’ concept scores based on their gender. Males had a higher total concept score (mean = 26.00, SD = 10.42) than females in the sample (mean = 10.875, SD = 5.14). The males’ score was significantly higher than that of females (t = 3.682, p = 0.004). H5: There is no significant difference between male and female students’ positive views on university life. Table 14. Average scores for gender groups - positive evaluation on university life. GENDER N Mean Std. Deviation Std. Error Mean Positive Evaluation male 8 3.6250 1.06066 0.37500 female 8 2.8750 0.83452 0.29505 Table 15. Independent samples test for positive evaluation based on gender. Levenes Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error 95% CI Lower Upper Positive Evaluation Equal variances 0.843 0.374 1.57 14 0.138 0.75000 0.4772 -0.27340 1.7734 Unequal variances 1.57 13.27 0.140 0.75000 0.4772 -0.27874 1.7787 From the figures in tables 14 and 15, males had a higher average positive evaluation of university life (mean = 3.625, SD = 1.06) than females (mean = 2.875, SD = 0.8345). However, this difference is not statistically significant (t = 1.57, p = 0.140). H6: There is no significant difference between male and female students’ negative views on university life. Table 16. Average scores for gender groups on negative evaluation scores on university life. GENDER N Mean Std. Deviation Std. Error Mean Negative Evaluation male 8 1.75 1.28174 .45316 female 8 2.00 1.06904 .37796 Table 17. Independent samples test for negative evaluation based on gender. Levenes Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error 95% CI Lower Upper Negative Evaluation Equal variances 0.500 0.491 -0.424 14 0.678 -0.25000 0.5901 -1.5156 1.0156 Equal variances -0.424 13.56 0.678 -0.25000 0.5901 -1.5195 1.0195 From table 16 above, females had a higher negative evaluation score for university life (mean = 2.00, SD = 1.07) than males (mean = 1.75, SD = 1.28). Based on table 17, the average negative evaluation scores between males and females are not statistically significant (t = -0.424, p = 0.678). H7: Younger and older students do not differ significantly in terms of whether they consider themselves to have positive attitude to university life. Table 18. Cross-tabulation of age categories against positive attitude towards university life. Positive attitude Total yes yes & no no answer Age Category 21 or younger Count 5 2 1 8 % within age category 62.5% 25.0% 12.5% 100.0% 22+ Count 6 1 1 8 % within age category 75.0% 12.5% 12.5% 100.0% Total Count 11 3 2 16 % within age category 68.8% 18.8% 12.5% 100.0% Students were further rated in terms of their positive attitudes towards university life. According to figures presented in table 18, 5 (62.5%) of respondents below 22 years believed they had a positive attitude against 6 (75.0%) of those at least 22 years of age, making up a total of 68.8% of the entire sample. Two respondents (25.0%) below 22 years of age had both positive and negative attitudes towards student life, compared to 1 (12.5%) from the higher age bracket; comprising 18.8% of the entire sample. One respondent (12.5%) had no reply to the question on attitude for both age groups. H8: Male and female students do not differ significantly in terms of whether they consider themselves to have positive attitude to university life; comprising 12.5% of the sample. Table 19. Cross-tabulation of gender against positive attitude on university life. Positive attitude Total yes yes and no no answer GENDER male Count 7 0 1 8 % within GENDER 87.5% 0.0% 12.5% 100.0% female Count 4 3 1 8 % within GENDER 50.0% 37.5% 12.5% 100.0% Total Count 11 3 2 16 % within GENDER 68.8% 18.8% 12.5% 100.0% Rated based on gender (table 19), 7 (87.5%) of the males had a positive attitude towards school life, as compared to 4 (50.0%) of the females. As such, 68.8% of the entire sample had a positive attitude towards university life. No males harboured contrasting opinions (both yes and no), as compared to the 3 (37.5%) females who did. One male and one female student did not provide replies to the question, implying that 12.5% of the sample offered no responses to the question. Discussion The scale developed owes its reliability to its ability to near-replicate the results of previous studies into student attitudes and concepts of the life they lead as students. Apparently, several findings in this research have aligned themselves with those worked out in several previous studies. For instance, differences in student attitudes across the gender divide has been discussed by Chowa et al. (2013) and Hadzaji and Omidi (2008), albeit in different contexts. This owes its credence from the quality development procedures used in developing the measurement scale. At the same time, some findings contradict those found in other research materials. For instance, younger learners have been known to have closely equal scores on the school-life attitude scale (Hedjazi & Omidi, 2008), while the present study found out that the older students have higher scores. Apparently, female learners appeared to have higher scores on the scale for assessing negative concepts towards the university; conversely, they had lower scores in the measure for positive concepts towards university life. These two sets of results are complementary, and they can be escalated to imply partial dissatisfaction of females with life at the university. To these results, it is important focus attention on the reasons why there are such inter-gender differences. Future analysis should focus on the extent to which age determines students’ perception of university life. If the factors identified as being more important considerations for satisfaction of students at the universities can be ranked, that would help the management in prioritizing its capital investments, including monetary and human resource increments. To the extent that this research is limited to utilizing the five factors on a reliability basis lends to its inadequacy in addressing the question of how much more important are any of the factors compared to the others. One notable weakness with the scale developed in this study is the lack of total adherence to replicating previous research findings. However, the fact that the data collected for the study was subjected to rigorous tests for reliability clearly indicates that the scale can be relied on for future studies. However, extra issues, such as validity (which was not assessed in the present case) need to be addressed further. The data did not need validation as being normally distributed as the t-tests are not affected by the distribution of the data. By considering the above highlighted weaknesses and recommendations, it is possible to develop a more consistent scale in future. References Ali, S., Haider, Z., Munir, F., Khan, H. & Ahmed, A. (2013). Factors contributing to the students academic performance: A case study of Islamia University sub-campus. American Journal of Educational Research. 1(8): 283-289. Bird, D. K. (2009). The use of questionnaires for acquiring information public perception of natural hazards and risk mitigation – A review of current knowledge and practice. Natural Hazards and Earth System Sciences. 9: 1307-1325. Blackmore, J., Bateman, D., O’Mara, J. & Loughlin, J. (2011). The connections between learning spaces and learning outcomes: People and learning spaces? Centre for Research in Educational Features and Innovation: Deakin University. Campbell, J. L., Quincy, C., Osserman, J. & Pedersen, O. K. (2013). Coding in-depth semi-structured interviews: Problems of unitization and intercoder reliability and agreement. Sociological Methods and Research. 42(3): 294-320. Chowa, G. A. N., Masa, R. D., Ramos, Y. & Ansong, D. (2013). How do student and school characteristics influence youth academic performance in Ghana? A hierarchical linear modelling of baseline data from the Youngsave Ghana Experiment. Washington University in St. Louis. Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches (2nd Ed.). California: Sage Publications. Gadermann, A. M., Guhn, M. & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research & Evaluation. 17(3): 1-13. Hedjazi, Y. & Omidi, M. (2008). Factors affecting the academic success of agricultural students at University of Tehran, Iran. Journal of Agricultural Science and Technology. 10: 205-214. Higgins, G. E. (2009). Quantitative versus qualitative methods: Understanding why quantitative methods are predominant in criminology and criminal justice. Journal of Theoretical and Philosophical Criminology. 1(1): 23-37. Higgins, S., Hall, E., Wall, K., Woolner, P. & McCaughey, C. (2005). The impact of school environments: A literature review. Newcastle: University of Newscastle. Kelly, E., Lesh, R. & Mason, G. (2002). Understanding and explicating the design experiment methodology. Journal of the ESRC Teaching and Learning Research Programme Research Capacity Building Network. Issue 3. ISSN: 1475-4193. Lloyd-Jones, G. (2003). Design and control issues in qualitative case study research. International Journal of Qualitative Methods. 2(2): 1-19. Rattray, J. & Jones, M. C. (2007). Essential elements of questionnaire design and development. Journal of Clinical Nursing. 16: 234-243. Tavakol, M. & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education. 2: 53-55. Thapa, A., Cohen, J., Higgins-D’Alessandro, A. & Guffey, S. (2012). School climate research summary: August 2012. New York: National School Climate Center. Woods, M. (2011). Interviewing for research and analysing qualitative data: An overview. Wellington: Massey University. Appendix 1 Corrected Item-Total Correlation Cronbachs Alpha academic teaching item 01 .311 .703 academic teaching item 02 -.037 .714 academic teaching item 03 .142 .707 academic teaching item 04 .295 .702 academic teaching item 05 .348 .695 academic teaching item 06 .037 .710 academic teaching item 07 .117 .708 academic teaching item 08 .284 .700 academic teaching item 09 .312 .701 academic teaching item 10 .521 .690 Academic resources item 01 .178 .706 Academic resources item 02 .407 .692 Academic resources item 03 .309 .697 Academic resources item 04 .348 .695 Academic resources item 05 .292 .699 Academic resources item 06 .134 .708 Academic resources item 07 .477 .696 Academic resources item 08 .320 .699 Academic resources item 09 .153 .707 Social item 01 .084 .710 Social item 02 .401 .693 Social item 03 .110 .709 Social item 04 .249 .703 Social item 05 .008 .714 Social item 06 .042 .712 Social item 07 -.113 .720 Social item 08 .192 .705 Social item 09 .017 .712 Social item 10 -.168 .732 finance item 01 .012 .710 finance item 02 -.033 .719 finance item 03 -.044 .718 finance item 04 .218 .703 finance item 05 .422 .690 finance item 06 .001 .713 finance item 07 .153 .707 finance item 08 -.151 .721 finance item 09 .291 .699 finance item 10 .013 .715 support services item 01 .273 .701 support services item 02 .420 .690 support services item 03 .262 .700 support services item 04 .324 .699 support services item 05 .285 .700 support services item 06 .275 .703 support services item 07 .003 .711 support services item 08 .533 .687 support services item 09 .055 .712 support services item 10 -.013 .713 Academic resources item 10 .309 .699 Read More