3.1 Data Type and Sources
We collected primary data from university students in Ghana via a survey questionnaire after obtaining their informed consent to participate in the study and the publication of their data. The protocol for data collection was approved by the KNUST Research Ethics Committee and the research was conducted in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki.
The survey questionnaire, which had three sections, was developed based on information gathered from the literature. The first section collected demographic data such as age, gender, programme of study, college, faculty, level of study, place of residence, among others from the students. According to Fernandez, et al (2016), “demographic data helps paint a more accurate picture of the people you are trying to understand”, and why they are making a certain choice. The second section collected data on the literacy levels of the students in relation to green buildings and technologies. To determine this, three questions were asked. The first question sought to measure the students’ awareness of green buildings on a nominal scale (Dalati, 2018) by asking about whether they have heard about it before. This question required a discrete response; that is, a ‘Yes’ or a ‘No’.
While a ‘Yes’ may be a good indicator of awareness, it says little about the respondents’ level of knowledge about green buildings, which might differ across them. The second question offered an opportunity to the students to self-assess their level of green building knowledge based on a modified version of the US National Institute of Health’s Competencies Proficiency 5-point rating scale from having “no knowledge” to being an “expert” in green buildings as described in Table 1. This is a form of ordinal rating scale which shows the options in an ordered manner (Gadermann, Guhn and Zumbo, 2012). Our modification was in two forms. First, we introduced another level to capture all those who have ‘no knowledge’ about green buildings. Second, we merged ‘novice’ and ‘fundamental awareness’ levels into one level and labelled it ‘novice with fundamental awareness’.
Table 1
Green Building Literacy Measurement Scale
Scale
|
Green Building Literacy Level
|
Description
|
1
|
No knowledge
|
No knowledge about it
|
2
|
Novice with Fundamental awareness (basic knowledge and limited experience)
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You have a common knowledge or an understanding of basic techniques and concepts. You have the level of experience gained in a classroom and/or experimental scenarios or as a trainee on-the-job. You are expected to need help when performing this skill.
|
3
|
Intermediate (practical application)
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You are able to successfully complete tasks in this competency as requested. Help from an expert may be required from time to time, but you can usually perform the skill independently.
|
4
|
Advanced (applied theory)
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You can perform the actions associated with this skill without assistance. You are certainly recognized within your immediate organization as "a person to ask" when difficult questions arise regarding this skill.
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5
|
Expert (recognised authority)
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You are known as an expert in this area. You can provide guidance, troubleshoot and answer questions related to this area of expertise and the field where the skill is used.
|
Source: Adopted from US National Institute of Health’s Competencies Proficiency
Since these ratings are self-assessed, there is the risk that some individuals might overrate or underrate their knowledge of green buildings. Therefore, the knowledge of respondents about green buildings was objectively tested by the third question. Based on Cole (2019), respondents were given nine true statements about green buildings (see Appendix C) to rate on a 5-point Likert scale: 1 = Strongly Disagree; 2 = Disagree; 3 = Don’t Know; 4 = Agree; 5 = Strongly Agree. As a psychometric response scale, the Likert scale is used primarily to obtain participant’s preferences or degree of awareness with a statement or sets of statements (Bertram, 2007).
The third section gathered data on the housing choices of the students. Respondents were asked to give an opinion based on a nominal scale (“1” = Yes and “0” = No) in terms of whether their previous and current housing units satisfied the true statements about sustainable buildings used in the Likert scale measurements. They were also asked if they would prefer a building with those sustainable characteristics in the future.
The questions were pretested by using five Teaching and Research Assistants at the Department of Land Economy of the Kwame Nkrumah University of Science and Technology in Ghana to ensure their validity and reliability. Their responses helped to streamline the questionnaires and the sample space. In particular, the survey was initially targeted at university students, graduates, and workers but was subsequently limited to only students since only estimates for the student population were available. The rationale for choosing the students is justified in two ways. First, because youth education and engagement on climate mitigation strategies has been insufficiently addressed across the world (Narksompong and Limjirakan, 2015). Second, they offer an opportunity to know and understand how university education is contributing to the creation of awareness of climate change mitigation strategies and green buildings in particular among future leaders – youth. Besides, this group of people are most likely to have had the experience of making a choice as to which building to rent during their studies in the university.
3.2 Sample And Sampling Method
The population of the study consisted of all students of universities in Ghana. Data for the 2013–2017 period from the National Council for Tertiary Education (NCTE) indicates a 9.4% compound growth in population. This rate was then used to project the 2020 student population of 183,498 (as presented in Table 2), based on which the study sample size was estimated.
Table 2
University Enrolment in Ghana
Year
|
2013
|
2016
|
2017
|
2020*
|
Student Enrolment
|
128,118
|
155,402
|
167,736
|
183,498
|
Source: NCTE (2013, 2016, 2017).
* Project figure
The Kish Formula (Kish, 1965) below was used to determine the sample size:
ss=\(\frac{{Z}^{2}(1-p)p}{{c}^{2}}\)
where Z is the Z-score (i.e. 1.96 for 95% confidence level), p is the percentage picking a choice, expressed as decimal (0.5 used for sample size needed), c is the confidence interval, expressed as decimal (i.e. 0.05). A total of 384.16 students was estimated as an appropriate sample size. However, a total of 736 responses were received, resulting in a 191.6% response rate.
3.3 Empirical Strategy And Data Analysis
The analysis is structured in three levels. First, we use descriptive statistics to provide a basic understanding of the data collected. Then the Chi square test was used at a confidence level of 95% to measure the relationship between green building literacy levels of the respondents and their demographic characteristics. This is important because the Chi square tests the independence and relationship of two variables (Franke, Ho, and Christie, 2012). This was followed by an assessment of the determinants of green building literacy levels. In the first instance, the Logistic regression technique is used to predict respondents’ awareness of green buildings based on whether they had previously lived in a house with sustainable features and their demographic attributes.
The Logistic regression technique is used to predict the probability of an event occurring with dichotomous response (Tolles and Meurer, 2016). Berry and Bove (1997) used the Logistic regression technique to predict a customer’s likelihood to purchase a product. The Logistic regression technique uses the Odds ratio in its estimation (Kleinbaum, et al., 2008), which measures the probability that an event will occur divided by the probability that the event will not happen. For this study, the Odds ratio is the probability that a respondent has previously lived a building with sustainable features. The Odds are estimated as follows:
Odds = P (case)/P(non-case)
= P(X)/1 – P(X)
= [exp(-XTB)]-1
Where P(X) is the probability of success (case) and 1 – P(X) is the probability of failure (non-case). An odds ratio of one show that the odds of a success outcome is equally likely as the odds of a failure. A value less than one denotes that success is not likely and a value greater than one indicates a high likelihood to prevail. The following model was estimated:
p(Green Buildings Awareness = 1) \({ = 1/1+e-(\beta }_{0}+\beta 1X1+\beta 2X2+\cdots \beta kXk)\)… (1)
Where p(Awareness of green building = 1) is the probability of respondent being aware of green buildings, \({\beta }_{0}\) is the constant, 𝑋1,𝑋2,... 𝑋𝑘 are predictors (i.e. respondents’ previous housing type, age, gender, education, employment status, income level, and area of study. See Table 1 for detailed description of variables) that may influence the choice of a house with sustainable features; 𝛽1, 𝛽2, ..., 𝛽𝑘 are odds ratios; and 𝜀 is an error term. Two similar models with the same independent variables but using continuous data (i.e. the respondents’ green building knowledge level and their green building test scores) were also estimated using the Multiple regression technique as follows:
Green building knowledge level \({ = \beta }_{0}+\beta 1X1+\beta 2X2+\cdots \beta kXk\)+ 𝜀 … (2)
Green building test scores \({ = \beta }_{0}+\beta 1X1+\beta 2X2+\cdots \beta kXk\)+ 𝜀 …… (3)
We then reverted to the Logistic regression model (4) below to predict the probability of respondents’ current housing having sustainable features and their preference for a house with sustainable features in the future based on their current green building literacy levels (X8, X9 and X10 in Table 3).
p(housing with sustainable features = 1) \({ = 1/1+e-(\beta }_{0}+\beta 1Greenbuildingliteracy 1+ \beta 2X2+\cdots \beta kXk)\)…(4)
Table 3 provides the details of the dependent and independent variables used in the regressions and how they are measured.
Table 3
Measurement and Description of Variables
Variable
|
Description
|
Measurement
|
Housing with sustainable features
|
Current housing choice
|
“1” = If current house has some or all the nine sustainable features presented in the Likert scale and “0” = If current house lacks them.
|
Future housing preference
|
“1” = If respondent would prefer a house with sustainable features in the future and “0” = If not.
|
X1
|
Previous housing choice
|
“1” = If previous house had some or all of the nine sustainable features and “0” = If previous house had none of the nine sustainable features
|
X2
|
Age
|
The age of respondents measured as the midpoint of the age groups.
|
X3
|
Gender
|
“1” = male and “0” = female
|
X4
|
Education
|
“1” = undergraduate and “0” = postgraduate
|
X5
|
Employment status
|
“1” = employed and “0” = unemployed
|
X6
|
Income
|
The midpoint of respondent’s month income
|
X7
|
Study area
|
“1” = If respondent studies a built environment subject and “0” = If respondent does not study a built environment subject
|
X8
|
Green buildings awareness
|
“1” = If respondent has heard about green buildings and “0” = if otherwise
|
X9
|
Green building knowledge level
|
"1" = No knowledge about it; "2" = Novice with fundamental awareness; “3" = Intermediate level; "4" = Advanced level; "5" = Expert level
|
X10
|
Green building test score
|
The average score from nine true statements about sustainable building based on a 5-point Likert scale: 1 = Strongly Disagree; 2 = Disagree; 3 = Don’t Know; 4 = Agree; 4 = Strongly Agree. See Appendix C for the Likert scale items.
|