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The purpose of this paper is to show the importance of secondary analysis to social sciences and to futures studies, both for research and teaching purposes.

An illustration of the main characteristics of secondary analysis, presenting it as a theory-driven activity where the definition of the research design plays a fundamental role.

This paper extends the secondary analysis approach to the study of the future. The utility of secondary analysis for futures studies is illustrated by means of the presentation of two examples developed in the field of the sociology of religion.

The results are useful for those who want to develop sound and robust approaches to the study of social change, taking into consideration the simulation of possible future scenarios.

Access to unbiased self-reported (primary) data for a normative concept like social sustainability has been a challenge for construction project management (CPM) scholars, and this difficulty has been further amplified by the ongoing COVID-19 pandemic. This paper aims to address this issue by asserting the suitability of secondary data as a methodologically sound but underutilized alternative and providing directions for secondary data-based research on social sustainability in a project setting.

By drawing on a framework for social sustainability and using “project-as-practice” approach as its point of departure, this conceptual paper identifies possibilities for utilizing multiple secondary sources in CPM research.

The paper provides a roadmap for identification of secondary sources, access to data, potential research designs and methods, limitations of and cautions in using secondary sources, and points to many novel lines of empirical enquiries to stimulate secondary data-based research on social sustainability in CPM.

Indicated secondary sources and empirical opportunities can support research efforts that aim to promote societal welfare through construction projects.

The presented guidance will assist researchers in identifying, accessing and utilizing naturalistic, secondary data for designing and conducting empirical research that cuts across social sustainability and CPM. This, in turn, will facilitate methodological pluralism and “practice turn” in such research endeavors.

In this chapter, I explore traditional notions of secondary data in qualitative research and consider the ways in which these are continually being reimagined in the digital age. I situate this discussion in respect to data typologies and, more reflexively, in relation to our need as researchers to make data real. I consider contemporary understandings of reuse in relation to secondary data, focusing particularly on qualitative interview data. Recognizing those who are already forging a path, I then suggest how we might move beyond notions of reuse and reimagine secondary data in the digital age. To illustrate these points, I highlight relevant studies drawing data from a range of online spaces, and finally summarize key considerations and challenges.

Dual diagnosis is a term that describes the co-occurrence of mental health disorders or illness and substance use or abuse disorders. Because this co-occurrence results in multiple diseases, layers of treatment are often needed to successfully create positive change in the individual. The purpose of this study is to explore factors of treatment that could facilitate improvements in functionality and quality of life for those with a dual diagnosis.

A secondary data analysis, using both quantitative and qualitative data, was completed. Secondary analysis is an empirical exercise that applies the same basic research principles as studies using primary data and has steps to be followed, including the evaluative and procedural steps commonly associated with secondary data analysis. Documentation data from the intensive mobile psychosocial assertive community treatment program was gathered for this analysis; this program was used because of the intensive and community-based services provided to patients with a dual diagnosis.

The major findings from this secondary analysis suggested that significant barriers included “denial” (e.g. evasion, suspension or avoidance of internal awareness) of diagnoses, complicated treatment and other barriers related to housing. Ultimately, these findings provided greater insight into potential effective treatment interventions for people living with a dual diagnosis.

This study adds to the growing body of literature showing that patient-centered care allows for more effective treatment and ultimately, improved health outcomes.

The purpose of this paper is to identify the dividend policy determinants of Sri Lankan firms and why they pay dividends.

The study uses several quantitative approaches to investigate dividend determinants using market (secondary) data of 190 Sri Lankan firms and 1,330 firm-year observations. Dividend determinants are also identified using survey (primary) data from 141 of the 190 firms. Triangulation is then used to facilitate validation of the data through cross-verification from two data sources.

Analysis of the market data reveals that firm size, industry impact, corporate governance, free cash flow, earnings, past dividends, profitability, investment opportunities, net working capital, concentrated ownership structure and investor preference represent the most important dividend determinants. Survey data confirm these findings. The evidence supports the pecking order, signaling, free cash flow, catering and outcome theories using both secondary and primary data and the bird-in-the-hand theory using survey data.

The findings are useful not only for corporate decision makers in establishing an appropriate dividend policy but also for shareholders in making investment decisions. Because the current study is limited to Sri Lanka, future researchers should study the same phenomenon in other countries using the triangulation approach.

This study provides a hybrid approach to dividend policy research by using both primary and secondary data in a single study. It is the first dividend study in Sri Lanka to use a triangulation approach.

The purpose of this paper is to explore why autistic people and their caregivers choose interventions other than applied behavior analysis (ABA), and how their decision impacts them over their lifespan. The focus group was divided into those who pursued augmentative and alternative communication (AAC)-based supports, received ABA, selected other interventions or received no intervention at all. The reported posttraumatic stress symptoms (PTSS) of ABA recipients were compared to non-ABA recipients in order to evaluate the long-term impacts of all intervention types. Using a mixed-method thematic analysis, optional comments submitted alongside a quantitative online survey were reviewed for emergent themes. These comments augmented the survey Likert scores with a qualitative impression of the diverse intervention-related attitudes among participants. Investigating the lived experiences of autism intervention recipients illuminated the scope of the long-term impacts of each intervention that was chosen. Overall, autistics who received no intervention fared best, based on the lowest reported PTSS. These findings may inform the potential redesign of autism interventions based on the firsthand reported experiences and opinions of autistics.

The aim of this study was to conduct research that is both question-driven and data-driven to aid in the analysis of existing data (Van Helden, 2013). In the research question-driven approach, the independent variables were the intervention type and duration of exposure relative to lifespan; the dependent variables were the PTSS severity score and binary indicator of meeting PTSS criteria. The analyses that were conducted included linear regression analyses of severity score on intervention type and duration, and χ² tests for independence of the probabilities of PTSS criterion satisfaction and intervention type. This experiment was designed to test the data-driven hypothesis that the prevalence and severity of PTSS are dependent on the type of autism intervention and duration of exposure. After reviewing the primary data set, the data-driven inquiry determined that the sample for secondary analysis should be categorized by communication-based vs non-communication-based intervention type in order to best complement the limitations and strengths of the published findings from the primary analysis.

Autistics who received no intervention had a 59 percent lower likelihood of meeting the PTSS criteria when compared to their ABA peers, and they remained 99.6 percent stable in their reported symptoms throughout their lifespan (R²=0.004). ABA recipients were 1.74 times more likely to meet the PTSS criteria when compared to their AAC peers. Within the 23 percent who selected an intervention other than ABA, consisting of psychotherapy, mental health, son-rise and other varying interventions, 63 percent were asymptomatic. This suggests that the combined benefits of communication-based interventions over behaviorism-influenced ABA practices may contribute to enhanced quality of life. Although not generalizable beyond the scope of this study, it is indicated from the data that autistics who received no intervention at all fared best over their lifetimes.

The obvious advantage of a secondary analysis is to uncover key findings that may have been overlooked in the preliminary study. Omitted variables in the preliminary data leave the researcher naive to crucially significant findings, which may be mitigated by subsequent testing in follow-up studies (Cheng and Phillips, 2014, p. 374). Frequency tables and cross-tabulations of all variables included in the primary analysis were reproduced. The secondary analysis of existing data was conducted from the design variables used in the original study and applied in the secondary analyses to generate less biased estimates (Lohr, 2010; Graubard and Korn, 1996). Inclusion criteria for each intervention group, PTSS scores and exposure duration, were inherited from the primary analysis, to allow for strategic judgment about the coding of the core variables pertaining to AAC and PTSS. The data sample from 460 respondents was reduced to a non-ABA group of n=330. An external statistician scored each respondent, and interrater reliability was assessed using Cohen’s κ coefficient (κ=1).

Including the autistic voice in the long-term planning of childhood interventions is essential to those attempting to meet the needs of the individuals, their families and communities. Both parents and autistic participant quotes were obtained directly from the optional comments to reveal why parents quit or persisted with an autism intervention.

Practitioners and intervention service providers must consider this feedback from those who are directly impacted by the intervention style, frequency or intensity. The need for such work is confirmed in the recent literature as well, such as community-based participatory research (Raymaker, 2016). Autistics should be recognized as experts in their own experience (Milton, 2014). Community–academic partnerships are necessary to investigate the needs of the autistic population (Meza et al., 2016).

Most autistic people do not consider autism to be a mental illness nor a behavior disorder. It is imperative to recognize that when injurious behavior persists, and disturbance in mood, cognition, sleep pattern and focus are exacerbated, the symptoms are unrelated to autism and closely align to the diagnostic criteria for posttraumatic stress disorder (PTSD). When PTSD is underdiagnosed and untreated, the autistic individual may experience hyperarousal and become triggered by otherwise agreeable stimuli. Since autism interventions are typically structured around high contact, prolonged hours and 1:1 engagement, the nature of the intervention must be re-evaluated as a potentially traumatic event for an autistic person in the hyperarousal state. Any interventions which trigger more than it helps should be avoided and discontinued when PTSS emerge.

Designing technologies for active and healthy ageing (AHA) requires a subtle understanding of end users (primary stakeholders) and healthcare professionals (secondary stakeholders). Often, their perspectives can be heterogeneous and contradictory. Identifying and negotiating them may be a challenge for designers. The purpose of this paper is to present our approach to understanding and negotiating contradictory stakeholder perspectives when designing AHA technologies for older adults.

The authors conducted an exploratory interview study with 15 community-dwelling older adults and 11 healthcare stakeholders, including doctors, health insurance agencies, policymakers and caregivers. The authors analyzed the interview material and negotiated contradictory perspectives.

Three major issues among stakeholders emerged: perspectives on AHA; perceived benefits and drawbacks of AHA technologies; and concerns about data privacy, control and trust.

The results show the heterogeneity and contradictions in stakeholder perspectives on AHA technologies and how these perspectives may be negotiated. This could help understand and facilitate long-term use of AHA technologies among older adults.

This study alerts researchers to contradictory perspectives among older people and healthcare stakeholders and the importance of involving them in the design of AHA technologies.

Data, either in primary or secondary form, represent the core strength of quantitative research. However, there is significant difference between collected data and the final researchable data. The data collection is driven by objectives of the research. The data also could be in various formats at different sources. The collected data in its original form may contain systematic and random errors. Such errors need to be cleaned from the data which is termed as data cleaning process.

The present chapter discusses about the different methodologies and steps that may be helpful for fine tuning the data into researchable format. The discussions are instantiated with the applications of methodologies on a set of financial data of companies listed in Bombay Stock Exchange. Various steps involved in transformation of collected data to researchable data are presented. A schematic model including data collection, data cleaning, working with variables, outlier treatment, testing the assumption of statistical test, normality, and heteroscedasticity is presented for the benefit of research scholars. Beyond this generic model, this paper focuses exclusively on financial data of listed companies in the Bombay Stock Exchange. The challenges involved in various sources, data gathering and other pre-analysis stages are also considered. This is also applicable for research based on secondary data sources in other fields as well.