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      Analysing N-of-1 observational data in health psychology and behavioural medicine: a 10-step SPSS tutorial for beginners

      research-article
      a , b , c
      Health Psychology and Behavioral Medicine
      Routledge
      N-of-1, single-case study, observational, statistical analysis, dynamic regression modelling

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          ABSTRACT

          Background: N-of-1 observational studies can be used to describe natural intra-individual changes in health-related behaviours or symptoms over time, to test behavioural theories and to develop highly personalised health interventions. To date, N-of-1 observational methods have been under-used in health psychology and behavioural medicine. One reason for this may be the perceived complexity of statistical analysis of N-of-1 data.

          Objective: This tutorial paper describes a 10-step procedure for the analysis of N-of-1 observational data using dynamic regression modelling in SPSS for researchers, students and clinicians who are new to this area. The 10-step procedure is illustrated using real data from an N-of-1 observational study exploring the relationship between pain and physical activity.

          Conclusion: The availability of a user-friendly and robust statistical technique for the analysis of N-of-1 data using SPSS may foster increased awareness, knowledge and skills and establish N-of-1 designs as a useful methodological tool in health psychology and behavioural medicine.

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          Most cited references43

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          The theory of planned behavior

          Icek Ajzen (1991)
          Organizational Behavior and Human Decision Processes, 50(2), 179-211
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            Missing data: our view of the state of the art.

            Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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              Ecological Momentary Assessment

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                Author and article information

                Journal
                Health Psychol Behav Med
                Health Psychol Behav Med
                Health Psychology and Behavioral Medicine
                Routledge
                2164-2850
                22 January 2020
                2020
                : 8
                : 1
                : 32-54
                Affiliations
                [a ]Centre for Clinical Research, The University of Queensland , Brisbane, Australia
                [b ]Institute of Applied Health Sciences, University of Aberdeen , Aberdeen, United Kingdom
                [c ]School of Psychology, University of Aberdeen , Aberdeen, United Kingdom
                Author notes
                [CONTACT ] Suzanne McDonald suzanne.mcdonald@ 123456uq.edu.au Centre for Clinical Research, The University of Queensland , Building 71/918, Royal Brisbane & Women's Hospital Campus, Herston, 4029Queensland

                Supplemental data for this article can be accessed https://doi.org/10.1080/21642850.2019.1711096

                Author information
                https://orcid.org/0000-0002-1745-4987
                https://orcid.org/0000-0002-3599-7299
                https://orcid.org/0000-0003-3357-4011
                Article
                1711096
                10.1080/21642850.2019.1711096
                8114402
                34040861
                eac81e54-96a0-4814-93b6-6740cd51e183
                © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 12, Tables: 0, Equations: 0, References: 45, Pages: 23
                Categories
                Research Article
                Articles

                n-of-1,single-case study,observational,statistical analysis,dynamic regression modelling

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