Multivariate Tests for Time Series Models

Cromwell, Jeff; Hannan, Michael; Labys, Walter C.; Terraza, Michel

doi:10.4135/9781412985239

Cited by 92 publications

(64 citation statements)

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“…Second, in a time-series model, time lag issue may occur [12]. For example, students who anticipated an assessment in week 10 would start preparing in week 9.…”

Section: Limitationsmentioning

confidence: 99%

Unravelling the dynamics of instructional practice

Nguyen

Rienties

Toetenel

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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Substantial progress has been made in understanding how teachers design for learning. However, there remains a paucity of evidence of the actual students' response towards leaning designs. Learning analytics has the power to provide just-in-time support, especially when predictive analytics is married with the way teachers have designed their course, or so-called a learning design. This study investigates how learning designs are configured over time and their impact on student activities by analyzing longitudinal data of 38 modules with a total of 43,099 registered students over 30 weeks at the Open University UK, using social network analysis and panel data analysis. Our analysis unpacked dynamic configurations of learning designs between modules over time, which allows teachers to reflect on their practice in order to anticipate problems and make informed interventions. Furthermore, by controlling for the heterogeneity between modules, our results indicated that learning designs were able to explain up to 60% of the variability in student online activities, which reinforced the importance of pedagogical context in learning analytics.

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“…Second, in a time-series model, time lag issue may occur [12]. For example, students who anticipated an assessment in week 10 would start preparing in week 9.…”

Section: Limitationsmentioning

confidence: 99%

Unravelling the dynamics of instructional practice

Nguyen

Rienties

Toetenel

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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“…The basic approach compares an observed trend to a theoretically meaningful trend as well as to a competing or contradicting trend. The specifics of the analyses will not be outlined here, but the reader is directed to Cromwell, Labys, Hannan, and Terraza (1994), , or Ostrom (1990) for further detail on time-series analyses.…”

Section: Analyzing Case Study Datamentioning

confidence: 99%

Qualitative Data Collection and Analysis

Bachiochi¹,

Weiner²

2004

Handbook of Research Methods in Industrial and Organizational Psychology

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IntroductionThe rich legacy of qualitative research has yielded a wide variety of approaches and methods (grounded theory, ethnography, action research, focus groups, interviews, case studies, etc.). This chapter will summarize specific steps from the traditions of grounded theory, ethnography, and other schools of thought that have emerged from the fields of sociology, anthropology, and psychology. Our specific focus will be on the methods we believe are most useful and frequently used in organizations. These methods can be combined to create an approach tailored to the research question and situation. In fact, triangulation of results from multiple approaches can yield more reliable and valid conclusions (Tucker, Powell, and Meyer, 1995) and is particularly important when results will be used for decisions that may have significant risk attached (e.g., attraction and retention strategies, implementation of a new performance appraisal system). Because systematic approaches can enhance the reliability and validity of conclusions drawn from qualitative research, we hope to strike a balance between standardization and the flexibility that has been the hallmark of many qualitative researchers and theorists (e.g., Glaser and Strauss, 1967).In this chapter, guidelines for choosing when qualitative research may be most appropriate and recommendations for how to collect and analyze qualitative research in organizations will be provided. Our purpose is not to provide an exhaustive description of different qualitative research approaches, because there are already several definitive sources for each technique. We direct the reader to Lee (1999) or Symon and Cassell (1998) for overviews of organizational applications of multiple methods, to Creswell (1998) or Maxwell (1996 for a more general review of qualitative research, and to Miles and Huberman (1994) for data analysis techniques. While we will be faithful to these original sources, which are often oriented to the academic researcher, we will also 162 Bachiochi, Weiner provide a perspective intended for the organizational researcher. Thus, unique situations and constraints on research in organizations will be covered as the specific steps and techniques are summarized. To facilitate this process, the topic of employee retention and attrition will provide a running context for our recommendations.The different approaches available also involve varying levels of participant involvement, which can be a significant factor in selecting a method. In this chapter, the approaches will be discussed starting with the highest level of participant involvement. For instance, focus groups require active involvement of the participants in the data collection process. Observational studies may vary widely in the level of involvement and potentially the degree to which participants are aware of the research being conducted. Case studies, especially if they rely heavily on written or archival records, could require very little participant involvement. Finally, some of the overarching is...

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“…It is represented from the sign (.) that is more than sign (+) and (-) [10]. Whereas, we can use q-q plot to check the normality of the error.…”

Section: Figure 2 Macf Plot Of Error Inflation Rate In 3 Locationsmentioning

confidence: 99%

“…After finding the significant model of GSTAR, The last step is checking the white noise and normality of the error [10]. MACF plot can be used to identify the assumption of white noise.…”

Section: Diagnostic Checkmentioning

confidence: 99%