Single-case Data Analysis Research Articles

PROBLEMATIKA PENERAPAN SOAL BERBASIS NUMERASI PADA PEMBELAJARAN MATEMATIKA KELAS V SEKOLAH DASAR DI KABUPATEN LOMBOK BARAT

Numeracy skills are needed in all aspects of life. Numeracy is the ability to apply number concepts and mathematical skills in everyday life as well as being able to use mathematics practically and have an understanding of information that is expressed mathematically.This study uses a qualitative approach with a multi-case study design. Data were collected through observation, interviews and documentation. The data analysis process is done through single case data analysis and cross case data analysis. The location of the study was conducted at three elementary schools in West Lombok regency, namely SDN 3 Kuripan, SDN 2 Jembatan Kembar Timur and SDN 4 Dasan Geres. The findings from the three schools showed that teachers in constructing numeracy-based problems refer to the components of the numeracy AKM but still in the form of simple questions. In pemanfataanya, numeracy-based questions are not done routinely and according to the needs of ANBK only. The lack of workshops and references as well as the distribution of teaching materials on numeracy in schools is a significant obstacle to the use of numeracy-based problems. While the supporting factor in the form of online sites that discuss examples of numeracy problems and the form of contextualization of the problem in the form of personal, socio-cultural and scientific contexts are still minimal.

School Principal Leadership as a Vanguard for Educators and Educational Staff in Enhancing Work Motivation at SMA Assa’adah Gresik, Indonesia

Leadership is an aspirational, motivational, and creative moral force that can influence educators and education staff to change their attitudes, aligning them with the jointly established vision, mission, and objectives. The absence of leadership can result in disorder and ineffectiveness in achieving the shared vision, mission, and goals. This research aims to discover and analyze how school principals act as pioneers for the school community in enhancing motivation. This research employs a qualitative descriptive research design. Data collection techniques include interviews, participant observations, and documentation. Data analysis utilizes single-case data analysis. Data validity techniques include credibility, transferability, dependability, and confirmability. The findings of this research reveal that school principals lead by enhancing motivation in the school community through possessing a sharp, far-reaching, and transparent leadership vision, continually innovating and making improvements to enhance the efficiency and effectiveness of work and being willing to take risks and responsibilities.

Study on National Standard School Examination and School Examination in Elementary Schools

This research is descriptive qualitative research, namely a data presentation in the form of a brief, concise but clear, and easy-to-understand description. This research aims to describe and analyze the National Standardized School Exams and School Exams in elementary schools as standard assessment tools for school exams in Elementary Schools (Multi Case Study at Lawangan Daya 2 Public Elementary School and Al-Munawwaraah Islamic Elementary School, Pamekasan Regency). The data collection procedure is the interview method, with in-depth interview techniques, observation or observation, and documentation studies. The data analysis technique used uses single case data analysis with the Miles and Huberman model, namely data condensation, data display/presentation, verification and drawing conclusions. The results of the study show that the implementation of national standard school exams will encourage schools to prepare students before the exam is held.

Implementation of the Principal's Strategy in Development Talents and Interests of Students (Multi-Site Study at MTsN 3 Nganjuk and MTsN 2 Kediri City)

The research in this dissertation is motivated by the phenomenon of the development of quality educational institutions that have a good process to create high student achievement, design a good curriculum taught by qualified teachers and good products as evidenced by the students being able to compete with superior schools. inside and outside the city so that, with a good process, Madrasa are no longer underestimated and even become the community's main choice of superior schools. The research approach uses qualitative methods. Data collection techniques are participant observation, in-depth interviews and documentation. The data analysis techniques used are 1) single case data analysis, 2) cross-site analysis. From the results of this study, it can be concluded that: ( 1) The formulation of the madrasa principal's strategy in developing students' talents and interests at MTsN 3 Nganjuk and MTsN 2 Kediri City is carried out by : formulating the vision and mission of the madrasa, determining the goals of planning for the development of student talents and interests that have been carried out by the madrasa, mapping talents and interests based on abilities and fields of study . Recruitment of basic education students with implementation of strict selection assessment taking into account several assessments, namely the recruitment of the academic path through with report cards and Olympics. Non-academic selection recruitment is seen from the results of students' memorization of the Koran, sports, arts and so on . (2) The implementation of the madrasa principal's strategy in developing students' talents and interests at MTsN 3 Nganjuk and MTsN 2 Kediri City is carried out by: 1) Programs of extracurricular activities provided for all students according to their potential, interests, talents, and abilities, so that students can develop maximally. 2) Supporting activities through routine and spontaneous activities that are carried out optimally can develop students' talents and interests.

Reliable visual analysis of single-case data: A comparison of rating, ranking, and pairwise methods

The most common method of single-case data analysis is visual analysis, but interrater reliability among visual raters tends to be poor. A new paradigm of visual analysis is presented and tested with the goal of addressing this persistent limitation. In traditional visual analysis, graphs are viewed and rated one by one. However, in the ranking and pairwise comparison methods introduced here, graphs are compared to each other and sorted from least to most evidence of intervention effectiveness. Four visual raters scored a set of 30 previously published single-case graphs using a traditional rating method as well as the ranking and pairwise methods. As in previous studies of visual analysis, the raters failed to achieve acceptable interrater reliability with the traditional rating approach (α = 0.641). However, interrater reliability increased to satisfactory levels when graphs were scored with ranking (α = 0.847) and pairwise comparison (α = 0.860). Visual analysis scores based on the pairwise method were also used to evaluate the performance of three single-case effect size statistics.

Validity and judgment bias in visual analysis of single-case data

Analysis of data from single-case intervention studies commonly involves visual analysis. Previous research indicates that visual analysis may suffer from low reliability and unpromising error rates. We investigated the reliability and validity of visual analysis and explored to what extent data trends affect judgments. We administered a within-subject experiment in which 186 teacher-education students visually analyzed specifically constructed single-case graphs that included either an intervention effect, a trend effect, both effects, or no effect. Participants identified intervention effects in 75% of the graphs, regardless of the existence of a trend. Type I error rates were low (5%) in graphs without a trend but increased fivefold (25%) for graphs with a trend. Inter- and intra-rater reliability was low, particularly when a trend was present in the data.

LECTURER PROFESSIONAL DEVELOPMENT PROGRAM STRATEGIES AT PUBLIC STATE UNIVERSITY

The purpose of this study was to describe the lecturer professional development program strategies. The data was collected by conducting interviews, observations, and documentation. The informants in this study were the member of university senate, rector, vice rector, the head and secretary of quality assurance institutions, the head and secretary of research and community service institution. The data analysis was done through a single case data analysis. The result of this study showed that the strategies used by state university in lecturer development program focusing on the noble values of the university's long-term strategic plans. There were authorized institutions, established groups of expertise field, guidebooks for lecturers’ management, and the development philosophy. The strategies in developing the lecturer professionalism on education and teaching were done by sending them for further study a doctoral degree, giving assistance, conducting pedagogical workshop, foreign language workshop, in-service training, and integrated workshop. On the research field, qualitative and quantitative research workshop were conducted. Creating orientation on research development, research scheme, classification and funding. On the community service field, lecturer professional development program was done by debriefing and forming a community-based service.

Machine Learning to Analyze Single-Case Data: A Proof of Concept

Visual analysis is the most commonly used method for interpreting data from single-case designs, but levels of interrater agreement remain a concern. Although structured aids to visual analysis such as the dual-criteria (DC) method may increase interrater agreement, the accuracy of the analyses may still benefit from improvements. Thus, the purpose of our study was to (a) examine correspondence between visual analysis and models derived from different machine learning algorithms, and (b) compare the accuracy, Type I error rate and power of each of our models with those produced by the DC method. We trained our models on a previously published dataset and then conducted analyses on both nonsimulated and simulated graphs. All our models derived from machine learning algorithms matched the interpretation of the visual analysts more frequently than the DC method. Furthermore, the machine learning algorithms outperformed the DC method on accuracy, Type I error rate, and power. Our results support the somewhat unorthodox proposition that behavior analysts may use machine learning algorithms to supplement their visual analysis of single-case data, but more research is needed to examine the potential benefits and drawbacks of such an approach.

Handling missing data in randomization tests for single-case experiments: A simulation study.

Single-case experiments have become increasingly popular in psychological and educational research. However, the analysis of single-case data is often complicated by the frequent occurrence of missing or incomplete data. If missingness or incompleteness cannot be avoided, it becomes important to know which strategies are optimal, because the presence of missing data or inadequate datahandling strategies may lead to experiments no longer "meeting standards" set by, for example, the What Works Clearinghouse. For the examination and comparison of strategies to handle missing data, we simulated complete datasets for ABAB phase designs, randomized block designs, and multiple-baseline designs. We introduced different levels of missingness in the simulated datasets by randomly deleting 10%, 30%, and 50% of the data. We evaluated the type I error rate and statistical power of a randomization test for the null hypothesis that there was no treatment effect under these different levels of missingness, using different strategies for handling missing data: (1) randomizing a missing-data marker and calculating all reference statistics only for the available data points, (2) estimating the missing data points by single imputation using the state space representation of a time series model, and (3) multiple imputation based on regressing the available data points on preceding and succeeding data points. The results are conclusive for the conditions simulated: The randomized-marker method outperforms the other two methods in terms of statistical power in a randomization test, while keeping the type I error rate under control.

Single-Case Data Analysis: A Practitioner Guide for Accurate and Reliable Decisions.

Practitioners frequently use single-case data for decision-making related to behavioral programming and progress monitoring. Visual analysis is an important and primary tool for reporting results of graphed single-case data because it provides immediate, contextualized information. Criticisms exist concerning the objectivity and reliability of the visual analysis process. When practitioners are equipped with knowledge about single-case designs, including threats and safeguards to internal validity, they can make technically accurate conclusions and reliable data-based decisions with relative ease. This paper summarizes single-case experimental design and considerations for professionals to improve the accuracy and reliability of judgments made from single-case data. This paper can also help practitioners to appropriately incorporate single-case research design applications in their practice.

Linear Trend in Single-Case Visual and Quantitative Analyses.

The frequently used visual analysis of single-case data focuses on data aspects such as level, trend, variability, overlap, immediacy of effect, and consistency of data patterns; most of these aspects are also commonly quantified besides inspecting them visually. The present text focuses on trend, because even linear trend can be operatively defined in several different ways, while there are also different approaches for controlling for baseline trend. We recommend using a quantitative criterion for choosing a trend line fitting technique and comparing baseline and intervention slopes, instead of detrending. We implement our proposal in a free web-based application created specifically for following the What Works Clearinghouse Standards recommendations for visual analysis. This application is especially destined to applied researchers and provides graphical representation of the data, visual aids, and quantifications of the difference between phases in terms of level, trend, and overlap, as well as two quantifications of the immediate effect. An evaluation of the consistency of effects across replications of the AB sequence is also provided. For methodologists and statisticians, we include formulas and examples of the different straight line fitting and detrending techniques to improve the reproducibility of results and simulations.

The impact of ordinate scaling on the visual analysis of single-case data

Visual analysis is the primary method for detecting the presence of treatment effects in graphically displayed single-case data and it is often referred to as the “gold standard.” Although researchers have developed standards for the application of visual analysis (e.g., Horner et al., 2005), over- and underestimation of effect size magnitude is not uncommon among analysts. Several characteristics have been identified as potential contributors to these errors; however, researchers have largely focused on characteristics of the data itself (e.g., autocorrelation), paying less attention to characteristics of the graphic display which are largely in control of the analyst (e.g., ordinate scaling). The current study investigated the impact that differences in ordinate scaling, a graphic display characteristic, had on experts' accuracy in judgments regarding the magnitude of effect present in single-case percentage data. 32 participants were asked to evaluate eight ABAB data sets (2 each presenting null, small, moderate, and large effects) along with three iterations of each (32 graphs in total) in which only the ordinate scale was manipulated. Results suggest that raters are less accurate in their detection of treatment effects as the ordinate scale is constricted. Additionally, raters were more likely to overestimate the size of a treatment effect when the ordinate scale was constricted.

Probability and Visual Aids for Assessing Intervention Effectiveness in Single-Case Designs: A Field Test.

Single-case data analysis still relies heavily on visual inspection, and, at the same time, it is not clear to what extent the results of different quantitative procedures converge in identifying an intervention effect and its magnitude when applied to the same data; this is the type of evidence provided here for two procedures. One of the procedures, included due to the importance of providing objective criteria to visual analysts, is a visual aid fitting and projecting split-middle trend while taking into account data variability. The other procedure converts several different metrics into probabilities making their results comparable. In the present study, we expore to what extend these two procedures coincide in the magnitude of intervention effect taking place in a set of studies stemming from a recent meta-analysis. The procedures concur to a greater extent with the values of the indices computed and with each other and, to a lesser extent, with our own visual analysis. For distinguishing smaller from larger effects, the probability-based approach seems somewhat better suited. Moreover, the results of the field test suggest that the latter is a reasonably good mechanism for translating different metrics into similar labels. User friendly R code is provided for promoting the use of the visual aid, together with a quantification based on nonoverlap and the label provided by the probability approach.

The Single-Case Data Analysis Package: Analysing Single-Case Experiments with R Software

The Single-Case Data Analysis Package: Analysing Single-Case Experiments with R Software

Analysis of single-case data: Randomisation tests for measures of effect size

Single-case experiments can be used to evaluate the effect of an intervention or treatment for a single entity. The internal validity and statistical conclusion validity of single-case experiments can be improved by incorporating randomisation in their design. In this article, we explain how to design randomised single-case phase and alternation studies as well as randomised simultaneous and sequential replication studies, and how to conduct randomisation tests for these designs. Advantages and limitations of randomisation tests are discussed. In order to not only determine the (non)randomness of an intervention effect, but also the magnitude of this effect, we propose to use an effect size index as a test statistic for the randomisation test. We illustrate this combination for the design and analysis of an ABAB phase study, using a free software package.

A comparison of mean phase difference and generalized least squares for analyzing single-case data

The present study focuses on single-case data analysis specifically on two procedures for quantifying differences between baseline and treatment measurements. The first technique tested is based on generalized least square regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns including both independent and serially related measurements arising from different underlying processes. Heterogeneity in autocorrelation and data variability was also included, as well as different types of trend, and slope and level changes. The results suggest that the two techniques perform adequately for a wide range of conditions and that researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.

Robust regression for single-case data analysis: How can it help?

This study examined the degree to which outliers were present in a convenience sample of published single-case research. Using a procedure for analyzing single-case data Allison & Gorman (Behaviour Research and Therapy, 31, 621-631, 1993), this study compared the effect of outliers using ordinary least squares (OLS) regression to a robust regression method and attempted to answer four questions: (1) To what degree does outlier detection vary from OLS to robust regression? (2) How much do effect sizes differ from OLS to robust regression? (3) Are the differences produced by robust regression in more or less agreement with visual judgments of treatment effectiveness? (4) What is a typical range of effect sizes for robust regression versus OLS regression for data from "effective interventions"? Results suggest that outliers are common in single-case data. The effects of outliers in single-case data are explored, and the implications for researchers and practitioners using single-case designs are discussed.

Percentage of nonoverlapping corrected data

In the present study, we proposed a modification in one of the most frequently applied effect-size procedures in single-case data analysis: the percentage of nonoverlapping data. In contrast with other techniques, the calculus and interpretation of this procedure are straightforward and can be easily complemented by visual inspection of the graphed data. Although the percentage of nonoverlapping data has been found to perform reasonably well in N = 1 data, the magnitude of effect estimates that it yields can be distorted by trend and autocorrelation. Therefore, the data-correction procedure focuses on removing the baseline trend from data prior to estimating the change produced in the behavior as a result of intervention. A simulation study was carried out in order to compare the original and the modified procedures in several experimental conditions. The results suggest that the new proposal is unaffected by trend and autocorrelation and that it can be used in case of unstable baselines and sequentially related measurements.

Single Case Design Visual Analysis Procedures for Use in Practice Evaluation

Visual procedures are the most commonly used in the analysis of single case design data. However, a number of problems with visual analysis methods have been identified in previous research, and research and development on visual analysis methods has been recommended. If single case design methods are to be used, as once hoped, by social work practitioners, then visual analysis methods should be easy and quick to use as well as methodologically defensible. This article presents visual analysis methods for use by social work practitioners. First, there is a presentation of a method for representing trend. Next there is a presentation of methods for representing variability, background variability, and overlap and discussion of their use in the visual analysis of single case data. Finally, there are two illustrative data analyses in which these methods are used. The mathematical and statistical bases for these methods are provided in two appendices. The methods presented are quick and easy to use and require nothing more than a pencil or pen and a straight edge for use by practitioners.

Meta-analysis for single-case consultation outcomes: Applications to research and practice

The application of meta-analysis holds much appeal for single-case consultation outcome research. We review a meta-analytic method for using within-study treatment effect sizes in reporting consultation outcomes. The strengths and limitations of traditional group design meta-analysis are examined. Various methods for analyzing single-case outcomes are discussed briefly, followed by an examination of the use of meta-analysis in single-case reviews across independent studies. Within-study meta-analytic results are presented that were derived from treatments implemented in consultations in natural settings. To conclude the article, an illustration is offered of a single-case data analysis display that incorporates meta-analytic results along with other indices of treatment outcome. Recommendations are provided for using meta-analytic methods to evaluate outcomes of single-case consultation treatment.