**4. Data analysis**

This section analyzes the quantitative and qualitative data collected from the questionnaires, interviews, and the online meetings. Quantitative data provides a great value to study by providing meaningful results from a large data set [34]. Qualitative data focused on meanings rather than on quantifiable phenomena. It includes rich descriptions of the data rather than measurements of specific variables [35]. Furthermore, it involves the identification, examination, and interpretation of patterns and themes in textual data and determines how these patterns and themes help to answer the research questions [34].

## **4.1. Questionnaire**

during seminars that we presented in three institutions in the Kurdistan region of Iraq: the University of Duhok, the University of Zakho, and Duhok Polytechnic University. After those seminars, we interviewed some of the participants, who were academics from those universities. Finally, we conducted an online meeting between a selected group of academics from the

Quantitative data were collected via the questionnaire. A series of presentations were made in

three universities in the Kurdistan region, Iraq, according to the following schedule:

**1.** December 14, 2015, the University of Zakho, Faculty of Engineering and Science.

**4.** January 12, 2016, Duhok Polytechnic University, Faculty of Engineering and Science.

were handed out to the participants. Most of the participants agreed to respond.

After each presentation, the questionnaire forms, which were validated through peer review,

Kurdistan region of Iraq and an outside expert, using video conference.

**Figure 3.** Techniques used for collecting quantitative and qualitative data.

**2.** December 23, 2015, the University of Duhok, Faculty of Engineering.

**3.** January 6, 2016, the University of Duhok, Faculty of Science.

*3.4.1. Quantitative data*

88 Global Voices in Higher Education

The questionnaire aimed to evaluate how online labs can assist teachers and students and highlights how a community of practice around online labs can increase collaborative and cooperative work among researchers,<sup>5</sup> especially in engineering and science disciplines. In general, the data are classified into two kinds: nominal data and interval data [34].

### *4.1.1. Characterizing the sample*

In order to understand the demographical background of participants (i.e. occupation, gender, language, age, program taken, Internet use experience, and internet use frequency), a descriptive analysis of the data was performed, as shown in **Figure 4**. From this analysis, the sample can be characterized as being primarily teaching staff (from electronics and computer engineering to physics), male, Kurdish, above 30 years old, and regarding the Internet usage, 89% have used it for more than 3 years on a regular basis.

<sup>3</sup> http://uoz.edu.krd/news.php?NID=ODY=4DXtDr2x

<sup>4</sup> https://scholar.google.com/citations?user=vAonlVMAAAAJ&hl=en

<sup>5</sup> https://drive.google.com/file/d/0BykHovfSV4CCSk9peGw5X0ZOWDQ/view

**Figure 4.** Analyzing the nominal data.

#### *4.1.2. Analyzing interval data*

To classify the continuous data, the questionnaire showed standardized differences between values. We transferred the questionnaires into a spreadsheet by putting each question number as a column heading and one row for each person's answers, as shown in **Table 1**. The scale was strongly agree (4), agree (3), disagree (2), and strong disagree (1) [36, 37]. This four-point scale (i.e. an even scale) forces people to choose a side, without a middle point [36, 38]. It gives a certain tendency of answer, hence increasing the reliability [39]. In addition, within using four points, the result can reasonably perceive the tendency [40].

#### **4.2. Interview**

Regarding the academics' questions, these had already been discussed and had been replied to in their office about online labs. Interestingly, the academics agreed that online labs can be useful to science, technology, engineering, and mathematics (STEM) fields for supporting hands-on labs. In their comments, they indicated online labs technology can be very interesting to use in higher educational and curricula. In addition, they pointed out that online labs should become available resources for engineering and science disciplines. The academics answers are shown in the results section.



**Table 1.** Analyzing the interval data.

#### **4.3. Online meeting discussion**

During the presentation, the participants wrote questions related to online labs technology and a community of practice to the respondent. Several questions were passed and answered by Professor Alves. These questions were related to online labs, collaborative, and cooperative work among researchers, a community of practice, cost of online labs use, and so on. These questions and answers are also shown in the results section.
