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An image of China in Africa through the lens of mixed-methods

by Jarosław Jura & Kaja Kałużyńska

The increasing number of digital and digitized content sources (online versions of traditional media, news portals, various websites on myriads of topics, and, of course, social media) has started to influence empirical social research. Huge amounts of easily accessible and almost ready-to-analyze datasets seem to be a dream coming true for social researchers, especially those who prefer to work with unobtrusively-collected data.

Such large datasets ask for being analysed by mixed methods, to avoid wasting their potential by either choosing a sample or focusing on quantitatively obtained information only. Here come other tools that make the life of a contemporary researcher much more comfortable – software solutions. Of course, in the ideal situation, one could just ‘feed’ all the data to AI and wait for the results, but there are many limitations to such an approach, like usability in specific cases, its accessibility, and, of course, the researcher’s nightmare: a limited project’s budget. Moreover, in the case of smaller datasets, consisting of heterogeneous data, analysis’ results might prove unsatisfactory.

Our research project, an exploratory study on the image of China and the Chinese in Zambia and Angola, included also an analysis of textual media content, namely news articles published in these countries and mentioning China or the Chinese. We obtained a mid-sized dataset, consisting of 2477 articles; the material was very heterogeneous, because of the wide scope of topics covered by the texts and the fact that we analysed content from both English- and Portuguese-language media.

In the course of analysis, we realized that a new method would be needed to obtain the best possible results on the basis of the collected data. After a series of trial-and-error approaches, we managed to develop MIHA – Mixed Integrative Heuristic Approach. The application of this method allowed us to create an exhaustive, contextual and precise keyword dictionary for automated classification of text units as well as a set of sentiment indexes.

We have to admit, that even though we did our best to utilize all the possibilities of the software (Provalis QDA Miner and Wordstat), the dictionary creation process was a time-consuming task since it included reviewing each word of frequency higher or equal to 10 in the whole database.

Our classification, similar to the initial conceptualization of theoretical categories within the grounded theory approach, aimed to explore the most frequent contexts in which China was depicted in African e-media. Each examined word was either added to an exclusion list (words irrelevant from the point of view of the research) or assigned to a chosen – sometimes a newly created – category, together with other words of the same root and all the synonyms.

In the next step, we examined the already categorized keywords in their context to refine the categorization results, mainly by removing those keywords that appeared within the text in unexpected contexts. Most of the categories were re-coded, and some of the keywords were re-assigned in the next steps. This heuristic approach resulted in a set of categories, including ‘emotional’ ones, positive and negative, that later on were used to design sentiment indexes. Our indexes are based on a comparison of the results of quantitative and qualitative analysis and coding. They could be used as a tool for improving dictionary-based sentiment analysis by comparing the results of sentiment analysis performed on the basis of automated coding with manually-coded samples.

We believe that MIHA constitutes a conceptual approach applicable by researchers of various backgrounds in projects focused on investigating the general image presented in textual content, especially in case of mid-sized, heterogeneous data sets. We do not overlook the fact that soon, automated machine learning coding methods will constitute the main approach towards text analysis. However, since such procedures are still imperfect and context-sensitive, we presume that MIHA, consisting of a contextualized dictionary, manual coding of chosen parts of the database and index measurements, could be useful for analysis of data sets related to less common study areas (social groups, languages, geographical areas, subcultures, etc.), in which machine learning-based research would contain a low level of construct validity.

Both the dictionary-creation process and the indexes are described in detail in our paper.

Read the full article in the IJSRM here.

Announcements, featured

The winners of our ECR paper competition for 2020-21

We are pleased to announce the results of our 2020-21 IJSRM competition for papers written by early career researchers (ECRs) who, at the time of submission, were either doctoral students or in their first three years of post-doctoral employment. Our aim has been to encourage and recognise research and contributions from new scholars in current and emerging methodological debates and practice.

All entries were subject to the Journal’s usual refereeing processes and had to reach our normal publishing standard. The winners were selected by a sub-panel of members of the IJSRM Editorial Board and the Journal Editors. The panel were impressed with the very strong field of entries, and we are pleased to announce not only a winner of the ‘Best ECR Article’ but also three ‘highly commended’ runners up.

Our IJSRM Early Career Researcher Prize is awarded to Stefanie Döringer (Austrian Academy of Sciences and University of Vienna) for her article on ‘The problem-centred expert interview: Combining qualitative interviewing approaches for investigating implicit expert knowledge’. The panel of judges remarked on a ‘clearly written and illuminating account’, representing ‘a lightbulb moment that brings two previously disconnected traditions together’, and that will be ‘highly valuable as a reference for many researchers for years to come’. Stephanie’s article has already been viewed over 16,500 times.

Stephanie said, ‘It is a great honor for me that my paper is awarded with the IJSRM Early Career Researchers’ prize. The appreciative comments from the competition judges encourage me to follow my research interest further and to deepen my work with qualitative methods in social research’.

Our highly commended runners (in alphabetical order) are: Riccardo Ladini (University of Milan): ‘Assessing general attentiveness to online panel surveys: The use of instructional manipulation checks’ Órla Meadhbh Murray (Imperial College London): ‘Text, Process, Discourse: Doing Feminist Text Analysis in Institutional Ethnography’ Kate Summers (London School of Economic and Political Science): ‘For the greater good? Ethical reflections on interviewing the ‘rich’ and ‘poor’ in qualitative research

Many congratulations to Stephanie, and also to Kate, Órla and Riccardo.

Announcements

Changes to our Journal metrics – a more rounded picture?

Journal Impact Factors – based on citations of articles published in the journal concerned, have been used as a proxy for the prestige of a journal in comparison with others in its field. And promotion committee considerations can be based on whether an academic has had articles published in high impact factor outlets. But this means of assessing the value of journals, the quality of articles published in them, and by extension the standing of the authors of published pieces, has been subject to criticism. These concerns run from questioning the reliability of the measurement and ranking, through encouragement to editors and authors to game the system, to condemnation of a neo-liberalised audit culture in academia. Some publishers and platforms, such as PLOS, have decided not to display Impact Factors.

Our Journal’s publisher, Routledge/Taylor & Francis, is now starting to shift away from the Impact Factor as a key indicator of quality, replacing it with a ‘basket’ of metrics in an effort to provide a more rounded views of the various ways in which a journal and articles published within it may have scholarly, policy and social ‘impact’.

The metrics being posted on the publishers’ IJSRM page for 2020 are:

  • an Impact Factor of 3.061 for the year, and one of 4.508 over a 5-year period
  • a new Journal Citation Indicator of 2.15, ranking 13/254 in the category of Social Sciences, Interdisciplinary, and
  • a CiteScore of 5.0, ranking 16/260 in all Social

Whether or not such moves deal with the concerns about measurement reliability, gaming the system, and evaluating of academics, is a moot point.

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Using objects to help facilitate qualitative interviews

by Signe Ravn

Doing empirical research on imagined futures is a methodological challenge. As scholars have argued, generating rich insights into how such futures might look can be difficult as participants may produce somewhat generic or stereotypical accounts of what the future might hold or even refuse to engage in such tasks (which of course provides other insights). Over the past decade, these challenges have led many qualitative researchers to explore different forms of creative, arts-based and/or participatory methods to approach the topic in new ways. In some cases, these approaches have been productive, and in other cases they lead to new questions about how to then interpret the findings. And sometimes they don’t really generate more concrete insights after all.

In my longitudinal research on the everyday lives and imagined futures of young women with interrupted formal schooling, I also used various creative methods to break away from the traditional interview format and to seek to approach the ways in which participants imagined their futures from multiple different perspectives. This approach was inspired by Jennifer Mason’s work on facet methodology. In my recent paper for the International Journal of Social Research Methodology I explore one creative method that proved particularly fruitful, that is, an object-based method. In brief, this method was deployed in the third interview with my participants (after one year) and involved asking participants to bring ‘one thing (like a gift, some clothing, a thing you once bought, or something else) that reminds you of your past and a thing that you relate to your future’. Only one participant asked for a clarification of what these items could be, while the remainder were happy to do this task, and some even said right away that they knew exactly what to bring. On the day of the interview, some participants did say that deciding on a ‘future’ thing had been difficult, but nevertheless they all had chosen something. Towards the end of the interview I asked about their ‘things’ and we spoke about each object in turn, exploring why they had brought a particular object, how it related to their past/future, and whether and how this was something they used in their day-to-day lives.

Reflecting on the interviews I was wondering what made this particular exercise helpful for exploring and speaking about ‘futures’. Other scholars have successfully drawn on objects to study memories, but none have turned their attention to the potential of objects for studying futures. In the paper I argue that what makes the object-method productive is to do with materiality. More specifically, I argue that what makes this method unique is the combination of ‘materiality as method’ as well as the ‘materiality of the method’, and that this double materiality at play is what is producing elaborate future narratives. In other words, via the materiality of the objects, specific imagined futures become ‘within reach’ for participants, with the object serving as an anchor for these future narratives. The method suggests a temporal complexity as well: the future objects come to represent futures that the participants have already taken steps towards; they are ‘futures-already-in-the-making. Drawing on Jose Esteban Munoz, we can consider them ‘futures in the present’, that is, futures that already exist, perhaps just in glimpses, in the present.

To make this argument I draw on both narrative research, material culture studies and qualitative research methodology. One key source of inspiration was Liz Moor and Emma Uprichard’s work on material approaches to empirical research, where the authors argue for paying greater attention to the ‘latent messages’ of methods and data, for instance in the form of sensory and emotional responses but also, as I point to in the paper, the messages conveyed by a dirty and bent P plate and a carefully crafted name tag.   Due to limitations of space, the published paper focuses on the ‘future’ objects and the future narratives generated through these, and only briefly mentions the ‘past’ object that participants also brought to the interview. This is due to the paper’s ambition to highlight the potentials of using object methods, and a focus on materiality more generally, in research on futures. However, for a full analysis of the insights gained through this method, both in terms of the settled and unsettled future narratives and the normative dimensions shaping which objects became ‘proper’ objects for the interview situation, both ‘past’ and ‘future’ objects should be analysed together.

Read the full article in the IJSRM here.

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Measuring Measures during a Pandemic

by Paul Romanowich & Qian Chen,

The spring 2020 semester started like many others before – frantically preparing class materials, finalizing research proposals, and trying to squeeze in one last getaway trip. However, by mid-March 2020 that normalcy had fallen by the wayside. Like it or not, classes were now all remote, disrupting both data collection and plans for any meaningful travel during the summer. But what about that data that was collected? Was it any good, considering what our participants were experiencing? Not surprisingly, little research has focused on the impact major environmental disruptions have on data reliability, given how rare and unpredictable those disruptions are (have you ever experienced a pandemic before 2020?!?). However, we were fortunate to be collecting repeated-measure impulsivity data throughout the spring 2020 semester. Thus, this research note focuses on whether data obtained in the immediate aftermath of the beginning of the COVID-19 pandemic is reliable, from a test-retest perspective.

Our original research question centered around whether decreasing one aspect of impulsivity, delay discounting, would have a positive effect on test scores for Electrical and Computer Engineering students. Like many personality traits, delay discounting rates have been shown to be relatively stable via test-retest data (i.e., trait-like). However, there is also a growing literature that episodic future thinking (EFT) can decrease delay discounting rates, and as a result decrease important impulse-related health behaviors (e.g., smoking, alcohol consumption, obesity). Thus, delay discounting also shows state-like properties. We hypothesized that decreasing delay discounting rates via EFT would also decrease impulse-related academic behaviors (e.g., procrastination), resulting in better quiz and test scores. To accurately measure temporal aspects of delay discounting, EFT, and class performance students completed up to 8 short (27-items) delay discounting tasks from January to May 2020. Multiple EFT trainings significantly decreased delay discounting rates relative to a control group (standardized episodic thinking – SET). However, the impact of EFT on academic performance was more modest.

Although the data did not support our original hypothesis, we did still have repeated-measure delay discounting data throughout the semester, which included data from March 2020 when classes were switched from in-person to fully remote. This repeated-measure data set up a series of Pearson correlations throughout the semester between delay discounting rates at two points in time (e.g., delay discounting rates at the beginning of the semester in January 2020 and end of the semester in May 2020). Importantly, students in the EFT group completed a delay discounting task on March 22, 2020 – 11 days after the official announcement that all classes would be fully remote for the remainder of the semester. In terms of test-retest reliability, the data collected on March 22, 2020 stood out as not like the other. Whereas delay discounting task test-retest reliability was high throughout the semester (supporting previous studies), most correlations using the March 22, 2020 data was nonsignificant, suggesting poor test-retest reliability. Thus, it appeared that the COVID-19 pandemic had significantly, but only temporarily, decreased test-retest reliability for delay discounting rates.

The EFT data also afforded us a way to look at changes more qualitatively in behavior before and after March 22, 2020. As a part of the EFT trainings, students came up with three plausible positive events that could happen in the next month, 6 months, and one year. We coded these events as either having COVID-19 content or not for all students. Predictably, events containing COVID-19 content did not appear until March 22, 2020. However, this event content changed as the semester progressed. On March 22, 2020, most (6 of 7 events) of the content was for the 1-month event. By May 7, 2020 only two students included COVID-19 content, and this was for the 6-month event. Thus, students were more concerned with COVID-19 in March 2020 and as a closer temporal disturbance, relative to May 2020. Perhaps this focus on COVID-19 in the near future disrupted delay discounting rates. We can’t be sure from this data, but the idea is intriguing.

Although this research note was not a rigorously controlled experiment to explicitly examine test-retest reliability for delay discounting, there are still some important points to take from the obtained data. First, it does appear that large environmental disruptions in participants life can significantly change test-retest reliability on standardized measures. Social and behavioral science researchers should be aware of this when interpreting their data. It may also be worthwhile to include a brief measure for significant life events that may be occurring concurrently with their participation in the task. Second, the change in test-retest reliability we observed was only temporary. This is actually good news for researchers, in that even significant environmental disruptions seem to have a minimal impact on test-retest reliability one month later. Perhaps we are more resilient as a species than we typically give ourselves credit for. Lastly, we have no doubt that other social and behavioral science researchers collected similar repeated-measure data throughout the spring 2020 semester. One way to be more confident that our results are not an outlier is through replication. Although we can’t (and don’t want to!) replay the beginning of the COVID-19 pandemic, researchers around the world could profitably begin to combine their data for specific well-validated measures to examine how this large environmental disruption may have systematically affected their results. The same could be done for other large environmental events, such as earthquakes or wars. The end result would be a better understanding of how these environmental disruptions impact those measurement tools that we base many of our theories and treatments off of.

Read the full article in IJSRM here.