Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0003
Charles Auerbach
In this chapter, readers are given step-by-step instructions on how to access the software necessary to use SSD for R. They are also presented with a brief overview of the capabilities of the SSD for R package. These include basic graphing functions, descriptive statistics, many effect size functions, autocorrelation, regression, statistical process control charts, hypothesis testing, and functions associated with analyzing group data. In combination, R, RStudio, and SSD for R, all of which are freely available, provide a robust way to analyze single-system research data. This chapter demonstrates how to download the necessary software and provides an overview of the visual and statistical capability available with SSD for R.
在本章中，读者将逐步了解如何访问使用SSD for R所需的软件。他们还简要概述了SSD for R软件包的功能。这些包括基本的绘图函数、描述性统计、许多效应大小函数、自相关、回归、统计过程控制图、假设检验以及与分析群体数据相关的函数。R、RStudio和SSD for R(所有这些都是免费的)结合在一起，提供了一种强大的方法来分析单系统研究数据。本章演示了如何下载必要的软件，并概述了SSD在R中的可视化和统计功能。
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Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0004
Charles Auerbach
This chapter discusses the analysis of the baseline phase. The baseline serves as the comparison for information collected during subsequent phases. It allows the researcher or practitioner to determine if the target behaviors are changing in a desirable or undesirable direction. Two different types of baselines are presented, concurrent and reconstructed. In a concurrent baseline, data are collected simultaneously, while other assessment activities are being conducted. A reconstructed baseline is an attempt to approximate naturally occurring behavior based on memories or case records. Issues related to comparing phases are discussed and illustrated, including stability of the baseline, trending data, and autocorrelation (or serial dependency). Guidance is provided on how each of these can be assessed and addressed, including the transformation of highly autocorrelated data. Examples are provided throughout to illustrate each concept.
{"title":"Analyzing Baseline Phase Data","authors":"Charles Auerbach","doi":"10.1093/oso/9780197582756.003.0004","DOIUrl":"https://doi.org/10.1093/oso/9780197582756.003.0004","url":null,"abstract":"This chapter discusses the analysis of the baseline phase. The baseline serves as the comparison for information collected during subsequent phases. It allows the researcher or practitioner to determine if the target behaviors are changing in a desirable or undesirable direction. Two different types of baselines are presented, concurrent and reconstructed. In a concurrent baseline, data are collected simultaneously, while other assessment activities are being conducted. A reconstructed baseline is an attempt to approximate naturally occurring behavior based on memories or case records. Issues related to comparing phases are discussed and illustrated, including stability of the baseline, trending data, and autocorrelation (or serial dependency). Guidance is provided on how each of these can be assessed and addressed, including the transformation of highly autocorrelated data. Examples are provided throughout to illustrate each concept.","PeriodicalId":197276,"journal":{"name":"SSD for R","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128917485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0006
C. Auerbach
This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of sampling error or chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis, that there is no difference between compared phases, and accepting the alternative one, that true differences exist. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed, and examples are provided to illustrate each. The method for transforming autocorrelated data and merging data sets is discussed further in the context of utilizing transformed data sets to test of Type 1 error.
{"title":"Statistical Tests of Type I Error","authors":"C. Auerbach","doi":"10.1093/oso/9780197582756.003.0006","DOIUrl":"https://doi.org/10.1093/oso/9780197582756.003.0006","url":null,"abstract":"This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of sampling error or chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis, that there is no difference between compared phases, and accepting the alternative one, that true differences exist. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed, and examples are provided to illustrate each. The method for transforming autocorrelated data and merging data sets is discussed further in the context of utilizing transformed data sets to test of Type 1 error.","PeriodicalId":197276,"journal":{"name":"SSD for R","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134471842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0009
Charles Auerbach
This chapter covers how to utilize RMarkdown to present SSD for R findings in a well-ordered and reproducible manner. RMarkdown is a plain text formatting syntax that makes writing research reports simple. The language provides a simple syntax that formats text such as headers, lists, boldface, and so on. This language is popular, and you will find many apps that are compatible with it. For example, combined with other packages, like SSD for R, users can easily create tables and graphics to present their research findings. Another important feature of this markdown language is that it will make your findings reproducible in that all of your files are connected. Thus, if there are changes to your data, rerunning the analysis is simple.
{"title":"Using RMarkdown to Present Your Findings","authors":"Charles Auerbach","doi":"10.1093/oso/9780197582756.003.0009","DOIUrl":"https://doi.org/10.1093/oso/9780197582756.003.0009","url":null,"abstract":"This chapter covers how to utilize RMarkdown to present SSD for R findings in a well-ordered and reproducible manner. RMarkdown is a plain text formatting syntax that makes writing research reports simple. The language provides a simple syntax that formats text such as headers, lists, boldface, and so on. This language is popular, and you will find many apps that are compatible with it. For example, combined with other packages, like SSD for R, users can easily create tables and graphics to present their research findings. Another important feature of this markdown language is that it will make your findings reproducible in that all of your files are connected. Thus, if there are changes to your data, rerunning the analysis is simple.","PeriodicalId":197276,"journal":{"name":"SSD for R","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125932057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0005
C. Auerbach
In this chapter readers will learn about methodological issues to consider in analyzing the success of the intervention and how to conduct visual analysis. The chapter begins with a discussion of descriptive statistics that can aid the visual analysis of findings by summarizing patterns of data across phases. An example data set is used to illustrate the use of specific graphs, including box plots, standard deviation band graphs, and line charts showing the mean, median, and trimmed mean that can used to compare any two phases. SSD for R provides three standard methods for computing effect size, which are discussed in detail. Additionally, four methods of evaluating effect size using non-overlap methods are examined. The use of the goal line is discussed. The chapter concludes with a discussion of autocorrelation in the intervention phase and how to consider dealing with this issue.
在本章中，读者将了解在分析干预的成功以及如何进行可视化分析时要考虑的方法问题。本章以描述性统计的讨论开始，描述性统计可以通过总结各个阶段的数据模式来帮助对结果进行可视化分析。示例数据集用于说明特定图形的使用，包括箱形图、标准差带图和显示平均值、中位数和修剪平均值的折线图，可用于比较任何两个阶段。SSD for R提供了三种计算效应大小的标准方法，并对其进行了详细讨论。此外，研究了四种使用非重叠方法评估效应大小的方法。讨论了球门线的使用。本章最后讨论了干预阶段的自相关以及如何考虑处理这一问题。
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Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0010
Charles Auerbach
This chapter was designed to provide readers a broad understanding of factors that should be considered when attempting to implement research in a practice environment. Common obstacles to conducting research in practice settings are discussed in this chapter and include administrative factors, work demands placed on practitioners, the availability of research knowledge and skills, and the research tradition of some professions. To address these, recommendations have been developed to remediate these barriers. These involve building support and demand for practice research by increasing its value to stakeholders, the development of and/or accessibility to research skills, and providing the infrastructure necessary to conduct practice research. The importance of including practitioners in the process of building research capacity is discussed. In order to increase the chances of success, capacity building must be collaborative. All activities should include representation from all employee groups that will either participate in the research process or be consumers of research.
{"title":"Building Support for Practice Research","authors":"Charles Auerbach","doi":"10.1093/oso/9780197582756.003.0010","DOIUrl":"https://doi.org/10.1093/oso/9780197582756.003.0010","url":null,"abstract":"This chapter was designed to provide readers a broad understanding of factors that should be considered when attempting to implement research in a practice environment. Common obstacles to conducting research in practice settings are discussed in this chapter and include administrative factors, work demands placed on practitioners, the availability of research knowledge and skills, and the research tradition of some professions. To address these, recommendations have been developed to remediate these barriers. These involve building support and demand for practice research by increasing its value to stakeholders, the development of and/or accessibility to research skills, and providing the infrastructure necessary to conduct practice research. The importance of including practitioners in the process of building research capacity is discussed. In order to increase the chances of success, capacity building must be collaborative. All activities should include representation from all employee groups that will either participate in the research process or be consumers of research.","PeriodicalId":197276,"journal":{"name":"SSD for R","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131818625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0002
Charles Auerbach
This chapter covers how to measure target behaviors and use common software to record and edit client data. Readers are then shown how to import data into R and use the SSD for R functions to analyze their data. The first part of this chapter focuses on the type of data that is most appropriate to record and some common issues related to collecting these. Four different types of measurement are covered, each of which has its own strengths and weaknesses. These include direct behavioral observations, standardized scales, individual rating scales, and logs. When selecting one or more methods of measuring a target behavior, readers will want to consider the specific needs of their clients, the identified problem, and the practice or research situation. The second part of this chapter demonstrates how to use Excel or other spreadsheet programs to quickly and effectively record this data.
{"title":"Getting Your Data Into SSDforR","authors":"Charles Auerbach","doi":"10.1093/oso/9780197582756.003.0002","DOIUrl":"https://doi.org/10.1093/oso/9780197582756.003.0002","url":null,"abstract":"This chapter covers how to measure target behaviors and use common software to record and edit client data. Readers are then shown how to import data into R and use the SSD for R functions to analyze their data. The first part of this chapter focuses on the type of data that is most appropriate to record and some common issues related to collecting these. Four different types of measurement are covered, each of which has its own strengths and weaknesses. These include direct behavioral observations, standardized scales, individual rating scales, and logs. When selecting one or more methods of measuring a target behavior, readers will want to consider the specific needs of their clients, the identified problem, and the practice or research situation. The second part of this chapter demonstrates how to use Excel or other spreadsheet programs to quickly and effectively record this data.","PeriodicalId":197276,"journal":{"name":"SSD for R","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133202546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0007
C. Auerbach
This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis and accepting the alternative one. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed. The method for transforming autocorrelated data and merging data sets is discussed. Once new data sets are created using the Append() function, they can be tested for Type I error using the techniques discussed in the chapter.
{"title":"Analyzing Group Data","authors":"C. Auerbach","doi":"10.1093/oso/9780197582756.003.0007","DOIUrl":"https://doi.org/10.1093/oso/9780197582756.003.0007","url":null,"abstract":"This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis and accepting the alternative one. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed. The method for transforming autocorrelated data and merging data sets is discussed. Once new data sets are created using the Append() function, they can be tested for Type I error using the techniques discussed in the chapter.","PeriodicalId":197276,"journal":{"name":"SSD for R","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131027477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.1093/oso/9780197582756.003.0008
Charles Auerbach
Meta-analytic techniques can be used to aggregate evaluation results across studies. In the case of single-subject research designs, we could combine findings from evaluations with 5, 10 or 20 clients to determine, on average, how effective an intervention is. This is a more complex and sophisticated way of understanding differences across studies than reporting those changes qualitatively or simply reporting the individual effect sizes for each study. In this chapter, the authors discuss why meta-analysis is important to consider in single-subject research, particularly in the context of building research evidence. They then demonstrate how to do this using SSD for R functions. Building upon effect sizes, introduced in Chapter 4, the authors illustrate the conditions under which it is appropriate to use traditional effect sizes to conduct meta-analyses, how to introduce intervening variables, and how to evaluate statistical output. Additionally, the authors discuss and illustrate the computation and interpretation of a mean Non-Overlap of All Pairs in situations which traditional effect sizes cannot be used.
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