METHODS

METHODS

Describing Methods

This section is written if the manuscript derives from research. It is sufficient to write "Methods", instead of "Research Methods". If the manuscript is written is a literature review, then this part does not need to be written specifically but included in the introduction section. However, if the research focuses on text and/or documents using content analysis and critical discourse analysis approach for example, then the description can be put in this section.

One type of research requiring an explanation of methods is the field research/studies; one that requires field data collection. Several essential things to be included in the methods are below.

Table 10 Things to Write about at the beginning of the Methods Section

Things to write

Example

Research approach

Quantitative, qualitative

Design or research model

Developmental research, action research, research & development (R & D), experimental, comparative, evaluative, etc.

Scope and range

Small (school, family, village), local, regional, cross-regional, large, and national scale

Research duration

Short term (e.g., from May 5 to July 1, 2016), long term (2 years, from 2015 to 2016),

Research site

SD Negeri 1 Selojari, Klambu, Grobogan Regency, Central Java; Mangunsari Village, Gunungpati District, Semarang City, Central Java

Informant (and respondents in a quantitative study since their responses are taken by researchers)

History teachers in grade VII of Junior High School; Mangunsari Village community figures;

Population and sample (for quantitative research)

The population is all students of SD Negeri 1 Selojari, and 50 students were taken randomly as the sample (Convey the sample collection technique. Avoid inserting too many formulas)

Data collection technique

Interview, observation, literature review, including the target and type of data gathered.

Analysis of data validity

Using triangulation technique (qualitative) and statistical equation (quantitative), but do not too focus on the formula

Data analysis technique

Using a critical perspective of social theories, ideological critic (qualitative), statistical formula (quantitative) such as t-test and the like

Novice authors are sometimes lacking in writing those essential elements in the methods section. Thus, the following is an example of writing an explanation of the adopted methods.

Table 11 The Example of a Short Paragraph in the Methods Section

Qualitative

This is a small-scale qualitative study that took place in Public Elementary School/Sekolah Dasar Negeri (SDN) Selojari 1, Selojari Village, Klambu Sub-district, Grobogan Regency. The research duration was relatively short; 6 months from March to August 2016. The research informant consisted of several teachers and VI-grade students. There were five students as informants who were selected based on their social background ... etc.

Quantitative

This is a small-scale quantitative study that took place in Public Elementary School/Sekolah Dasar Negeri (SDN) Selojari 1, Selojari Village, Klambe Sub-district, Grobogan Regency. The research duration was relatively short; 6 months from March to August 2016. The research respondent consisted of several teachers and VI-grade students. The respondents were selected randomly based on ... formula. Finally, the researchers obtained ...

The data collection technique has to be explicitly elucidated, for instance, interview, observation, etc. However, there is no need to explain the concept and theory of the adopted techniques. Furthermore, the data validity formula for a quantitative study is written simultaneously. On the other hand, the accuracy and accountability data for a qualitative study is supported by Triangulation techniques. As for data analysis, the theoretical perspective could be written immediately in qualitative research, while short formula and the reason for using it have to be explained in quantitative research.

Table 12 The Example of a Long Paragraph in the Methods Section

Data for the study were collected through a self-reported online survey. Participants were 1103 undergraduate students in five large General Education courses at a public university in the northeastern United States. In class, course instructors (that included the researchers) directed students to the survey by forwarding an invitation email from the researchers and offered extra credit for participation: 717 usable responses were collected (a 65.0% response rate). The average age of the respondents was 19.5 years. Of all the participants, 71% were female, and 21% were male. The median number of semesters spent at the University was 4, and the average credit load was approximately 17 credits during the academic semester in which these data were collected. Respondents reported spending approximately 7.52 hours (standard deviation = 6.9 hours) per week on total academic reading time (3.74 hours total during weekdays and 7.52 hours total per week). That is, students were spending less than 30 minutes per week per course credit or approximately 1.5 hours per week for a 3-credit course. The study collected data on the major of students. However, that information is not reported or analyzed here. Students were not compensated for responding to the survey.

The example in Table 12 was excerpted from an article by Sharma, van Hoof, and Ramsay (2017)  entitled “The influence of time on the decision that students make about their academic reading”, Active learning in Higher Education, 20(10), 81-82. The following table provides a more specific example in writing data analysis as a part of the methods section taken from the same reference, page 82.

Table 13 Outlining Data Analysis in the Methods Section

Responses to Likert-type questions (Boone and Boone, 2012) were individually analyzed and not combined with other responses to create composite scores. Data were analyzed using a combination of statistical techniques, and demographic variables were analyzed using descriptive statistics.

Multiple linear regression and ordinal logistic regression methods were used to conduct the appropriate analyses. Variables were chosen for multiple linear regressions only if underlying measures were on interval or ratio scales (Spanos, 1999). All other variables were analyzed using ordinal logistic regression, suitable for interval scale data (Agresti, 1990). Multiple regression calculations and a series of ordered logit regression analyses were conducted to analyze the data. These analyses all used "completion of reading course texts that students had been asked to read" as the dependent variable and various independent variables that captured the students' opinions about their reading of texts, incentives to read, the timing of reading, and perceived reading support issues.

 

Describing the Research Instrument

The research instrument is immensely vital and required as a research manual especially for a quantitative study. It is a guide for researchers in collecting data and analyzing it appropriately. The research instrument contains criteria or indicators based on certain theories. For example, in a study that seeks to determine informants' assessment toward certain policies, researchers can use a scaling technique in which each informant can select a specific score describing his assessment. The criteria and indicators can be manifested in the form of statements or questions that will be able to extract data accurately.

The types of instruments, criteria, indicators, and even the theoretical basis need to be described in the methods section, the aim of which is to provide a clear picture for readers about the theoretical-scientific basis of the research carried out. Describing the instruments used is a way of convincing readers that the research is clear and has a strong theoretical basis. Below are some examples of instrument descriptions.

Table 14 The Example of Methods Section in a Qualitative Research

In this qualitative study, we have combined different approaches. This choice reflects the complexity of the topic, as our wish is to explore different perspectives, and possibly draw a picture of an essential part of ECTE work-based education at OAUC. The work is based on a text-analysis of policy documents and program plans, 10 focus-group interviews with teachers, students, coordinators, and staff management, 4 observations of field classes, and 1 questionnaire answered anonymously by 23 students in the fourth term. All teachers involved in our work-based education and all students in the two selected classes were invited to partake in the study. The empirical material consists of a random sample of students (in two different classes) and teachers. Members of staff management in kindergartens were selected because of their positions in kindergartens of the students interviewed.

In this article, we present results from the questionnaire and two focus-group interviews, one with students and one with staff managers.

The questionnaire consisted of 13 questions relating to 4 main themes: Knowledge, learning environments, roles, and loops of learning. Interviews and answers have been transcribed and analyzed using category analysis (Strauss & Corbin, 1990). The answers were grouped by topics generated through individual readings and common discussions of analysis. In this analysis, we have concentrated on students' experiences and possible emerging patterns. The aim has been to study differences and patterns in some students' experiences and opinions as regards the workplace as a learning environment. As a relatively small qualitative study, it has clear limitations. However, we find it useful to shed light on a fundamental issue in work-based learning – not only in ECTE but in education in general: The common challenge of the educational institution and workplace to support students' learning at work.

Table 14 is a part of the method explanation taken from an article by Kaarby and Lindboe (2016) entitled "The Workplace as Learning Environment in Early Childhood Teacher Education: An Investigation of Work-Based Education" published in Higher Education Pedagogies, 1 ( 1), 107-108. In this example, there has been provided a complete description of the research approach, the instruments used, the theoretical basis of the analysis, and the technical operational analysis.

Table 15 The Example of Instrument Description for a Quantitative Research

The survey was created on the Survey Monkey© platform. Researchers representing expertise in psychology, education, teaching scholarship, and teaching and learning technologies developed the survey. Two main constructs guided the development of the survey: (1) self-rationing of time and (2) academic and nonacademic activities. Completion of course reading texts were used as a proxy for the amount of reading that students claimed to have done and was measured on a 4-point Likert-type qualitative response scale (never, rarely, most of the time, and always). Time allocated to reading was measured by the number of reading hours per week and the respondents' self-reported time spent on reading texts per week, during weekdays, on Saturday or Sunday. Responses were summed and reported as total academic reading time in hours per week. How efficiently they used their time was measured by self-reported responses to seven different questions that addressed the timing of their reading (Sappington et al., 2002) and whether students thought they managed their time well (Britton and Tesser, 1991). The allocation of their time was defined as a descriptive construct rather than an objective outcome. Since distractions impact decision-making among individuals, the survey also included questions on the students' level of involvement in other academic and nonacademic activities. Students were asked about the time they spent on academic and nonacademic activities (Lotkowski et al., 2004; Pascarella et al., 2004; Reason et al., 2006). Other variables of interest included demographic information about the respondents such as age, gender, nationality, and grade point average (GPA). These variables were included based on evidence from research that shows age and gender (Parault and Williams, 2010; Zhang and Ma, 2011) are associated with reading behavior, and GPA as a proxy of achievement is associated with cognitive task performance including reading (Trainin and Swanson, 2005).

The above Table 15 shows an example of instrument description taken from an article by Sharma, van Hoof, dan Ramsay (2017)  entitled “The Influence of Time on the Decision that Students Make about Their Academic Reading”, Active learning in Higher Education, 20(10), 82.