Mark Rubin's Research

Questionable Metascience Practices

noreply@blogger.com (Mark Rubin) — Mon, 24 Apr 2023 23:57:00 +0000

In this new article, I consider questionable research practices in the field of metascience. A questionable metascience practice (QMP) is a research practice, assumption, or perspective that's been questioned by several commentators as being potentially problematic for metascience and/or the science reform movement. I discuss 10 QMPs that relate to criticism, replication, bias, generalization, and the characterization of science. My aim is not to cast aspersions on the field of metascience but to encourage a deeper consideration of its more questionable research practices, assumptions, and perspectives.

10 QMPS

(1) Rejecting or ignoring self-criticism: Rejecting or ignoring criticisms of metascience and/or science reform

(2) Fast ‘n’ bropen scientific criticism: A quick, superficial, dismissive, and/or mocking style of scientific criticism

(3) Overplaying the role of replication: Assuming that replication is essential to science, and that it indexes “the truth”

(4) Unspecified replication rate targets: Assuming a replication rate is “too low” without specifying an “acceptable” rate

(5) Metabias: An unacknowledged bias towards explaining the replication crisis in terms of researcher bias

(6) A bias reduction assumption: Focusing on selective reporting as the primary form of researcher bias and assuming that it can be reduced without increasing other forms of bias

(7) Devaluing exploratory results: Devaluing an exploratory result as being more “tentative” than a confirmatory result without considering other relevant issues (e.g., quality of associated theory, methods, analyses, transparency)

(8) Presuming QRPs are problematic: Presuming that questionable research practices (e.g., HARKing) are always problematic research practices

(9) A focus on knowledge accumulation: Conceiving knowledge accumulation as the primary objective of science without considering (a) the role of specified ignorance or (b) different objectives in other philosophies of science

(10) Homogenizing science: Focusing on specific approaches (e.g., quantitative methods; replicable effects) as “the scientific method”

A Caveat

In my article, I stress that only some metascientists engage in some QMPs some of the time, and that these QMPs may not always be problematic. Research is required to estimate the prevalence and impact of QMPs. In the meantime, I think that QMPs should be viewed as invitations to ask questions about how we go about doing better metascience.

Further Information

The Article

Rubin, M. (2023). Questionable metascience practices. Journal of Trial and Error. https://doi.org/10.36850/mr4

Journal of Trial and Error Special Issue

My article is part of a special issue in the Journal of Trial and Error: “Consequences of the scientific reform movement: Is the scientific reform movement headed in the right direction?” https://journal.trialanderror.org/pub/callscientificreform/release/4

My Other Work in This Area

For my other work in the area of metascience, please see: https://sites.google.com/site/markrubinsocialpsychresearch/replication-crisis

Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests

noreply@blogger.com (Mark Rubin) — Sat, 27 Aug 2022 00:21:00 +0000

Researchers often distinguish between:

(1) Exploratory hypothesis tests - unplanned tests of post hoc hypotheses that may be based on the current results, and

(2) Confirmatory hypothesis tests - planned tests of a priori hypotheses that are independent from the current results

This distinction is supposed to be useful because exploratory results are assumed to be more “tentative” and “open to bias” than confirmatory results. In this recent paper, we challenge this assumption and argue that exploratory results can be more compelling than confirmatory results.

Our article has three parts. In the first part, we demonstrate that the same data can be used to generate and test a hypothesis in a transparently valid manner. We agree that circular reasoning can invalidate some exploratory hypothesis tests. However, circular reasoning can be identified by checking the contents of the reasoning without knowing the timing of that reasoning (i.e., a priori or post hoc).

Figure 1. An illustration of two ways in which exploratory data analyses may provide legitimate support for post hoc hypotheses

In the second part of our article, we argue that exploratory hypothesis tests can have several evidential advantages over confirmatory tests and, consequently, they have the potential to deliver more compelling research conclusions. In particular, exploratory hypothesis tests:

✅ avoid researcher commitment and prophecy biases

✅ reduce the motive for data fraud

✅ are more appropriate following unplanned deviations

✅ facilitate inference to the best explanation

✅ allow peer reviewers to contribute to exploratory analyses

Finally, in the third part of our article, we consider several potential *disadvantages* of exploratory hypothesis tests and conclude that these potential disadvantages may not be problematic. In particular, exploratory hypotheses tests are not necessarily disadvantaged due to:

✅overfitting

✅bias

✅HARKing

✅unacceptable research practices

And they:

✅are usually necessary

✅can be falsified

✅can predict anything but may suffer an evaluative cost in doing so

To be clear, our claim is not that exploratory hypothesis tests are always more compelling than confirmatory tests or even that they are typically more compelling. Our claim is only that exploratory tests can be more compelling in specific research situations. More generally, we encourage researchers to evaluate specific tests and results on a case-by-case basis rather than to follow simplistic heuristics such as “exploratory results are more tentative,” which represents a form of methodolatory.

Our paper builds on some of my previous work on preregistration and HARKing. And please check out Szollosi and Donkin’s (2021) paper on “the misguided distinction between exploratory and confirmatory research.”

For more info, please see our open access article:

Rubin, M., & Donkin, C. (2022). Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests. Philosophical Psychology. https://doi.org/10.1080/09515089.2022.2113771

Two-Sided Significance Tests

noreply@blogger.com (Mark Rubin) — Mon, 04 Apr 2022 02:29:00 +0000

In this paper (Rubin, 2022), I make two related points: (1) researchers should halve two-sided p values if they wish to use them to make directional claims, and (2) researchers should not halve their alpha level if they're using two one-sided tests to test two directional null hypotheses.

(1) Researchers should halve two-sided p values when making directional claims

Researchers sometimes conduct two-sided significance tests and then use the resulting two-sided p values to make directional claims. I argue that this approach is inappropriate because two-sided p values refer to non-directional hypotheses, rather than directional hypotheses.

So, for example, if you conduct a two-sided t test and obtain a significant two-sided p value, then your significant result refers to a non-directional null hypothesis (e.g., "men have the same self-esteem as women”), and you should make a corresponding non-directional claim (e.g., "men and women have significantly different self-esteem"). If you wish to make a directional claim (e.g., "men have significantly higher self-esteem than women"), then you should halve your two-sided p value to obtain a one-side p value.

This first point is important because, if you use a two-sided p value to make a decision about a directional null hypothesis, then (a) your evidence will be weaker than it should be (i.e., your p value will be too large), and (b) your Type II error rate will be higher than necessary. For the same view, please see Georgi Georgiev’s onesided.org website here.

(2) Researchers should not halve their alpha level when using two one-sided tests

I also argue that, if you use two one-sided tests to test two directional null hypotheses, then it's not necessary to adjust your alpha level to compensate for multiple testing, because your decision about rejecting each directional hypothesis is based on a single test result, rather than multiple test results.

For example, imagine that you use a one-sided test to test the directional null hypothesis that “men have the same or lower self-esteem than women.” In this case, there's no need to lower your alpha level (e.g., from .050 to .025), because your Type I error rate only refers to a single test of a single null hypothesis. It doesn't refer to either (a) the other directional null hypothesis (i.e., “men have the same or higher self-esteem than women”) or (b) the non-directional null hypothesis (i.e., “men have the same self-esteem as women).” Consequently, no alpha adjustment is required. For similar views, please see Georgi Georgiev's piece here and my paper on multiple testing here.

For further information, please see:

Rubin, M. (2022). That’s not a two-sided test! It’s two one-sided tests! Significance, 19(2), 50-53. Publisher’s version Self-archived version

When to Adjust Alpha During Multiple Testing

noreply@blogger.com (Mark Rubin) — Wed, 07 Jul 2021 01:22:00 +0000

In this new paper (Rubin, 2021), I consider when researchers should adjust their alpha level (significance threshold) during multiple testing and multiple comparisons. I consider three types of multiple testing (disjunction, conjunction, and individual), and I argue that an alpha adjustment is only required for one of these three types.

There’s No Need to Adjust Alpha During Individual Testing

I argue that an alpha adjustment is not necessary when researchers undertake a single test of an individual null hypothesis, even when many such tests are conducted within the same study.

For example, in the jelly beans study below, it’s perfectly acceptable to claim that there’s “a link between green jelly beans and acne” using an unadjusted alpha level of .05 given that this claim is based on a single test of the hypothesis that green jelly beans cause acne rather than multiple tests of this hypothesis.

Retrieved from https://xkcd.com/882/

For a list of quotes from others that are consistent with my position on individual testing, please see Appendix B here.

To be clear, I’m not saying that an alpha adjustment is never necessary. It is necessary when at least one significant result would be sufficient to support a joint hypothesis that’s composed of several constituent hypotheses that each undergo testing (i.e., disjunction testing). For example, an alpha adjustment would be necessary to conclude that “jelly beans of one or more colours cause acne” because, in this case, a single significant result for at least one of the 20 colours of jelly beans would be sufficient to support this claim, and so a familywise error rate is relevant.

Studywise Error Rates are Not Usually Relevant

I also argue against the automatic (mindless) use of what I call studywise error rates – the familywise error rate that is associated with all of the hypotheses that are tested in a study. I argue that researchers should only be interested in studywise error rates if they are interested in testing the associated joint studywise hypotheses, and researchers are not usually interested in testing studywise hypotheses because they rarely have any theoretical relevance. As I explain in my paper, “in many cases, the joint studywise hypothesis has no relevance to researchers’ specific research questions, because its constituent hypotheses refer to comparisons and variables that have no theoretical or practical basis for joint consideration.”

For example, imagine that a researcher conducts a study in which they test gender, age, and nationality differences in alcohol use. Do they need to adjust their alpha level to account for their multiple testing? I argue “no” unless they want to test a studywise hypothesis that, for example: “Either (a) men drink more than women, (b) young people drink more than older people, or (c) the English drink more than Italians.” If the researcher does not want to test this potentially atheoretical joint hypothesis, then they should not be interested in controlling the associated familywise error rate, and instead they should consider each individual hypothesis separately. As I explain in my paper, “researchers should not be concerned about erroneous answers to questions that they are not asking.”

For a list of quotes that support my position on studywise error rates, please see Appendix A here.

My paper is a follow up to my 2017 paper that considers p values in exploratory analyses.

For further information, please see:

Rubin, M. (2021). When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing. Synthese. https://doi.org/10.1007/s11229-021-03276-4 Open Access

“Repeated sampling from the same population?” A critique of Neyman and Pearson’s responses to Fisher

noreply@blogger.com (Mark Rubin) — Sun, 27 Sep 2020 03:49:00 +0000

In a new paper in the European Journal for Philosophy of Science, I consider Fisher's criticism that the Neyman-Pearson approach to hypothesis testing relies on the assumption of “repeated sampling from the same population” (Rubin, 2020). This criticism is problematic for the Neyman-Pearson approach because it implies that test users need to know, for sure, what counts as the same or equivalent population as their current population. If they don't know what counts as the same or equivalent population, then they can't specify a procedure that would be able to repeatedly sample from this population, rather than from other non-equivalent populations, and without this specification Neyman-Pearson long run error rates become meaningless.

I argue that, by definition, researchers do not know for sure what are the relevant and irrelevant features of their current populations. For example, in a psychology study, is the population “1st year undergraduate psychology students” or, more narrowly, “Australian 1st year undergraduate psychology students” or, more broadly, “psychology undergraduate students” or, even more broadly, “young people,” etc.? Researchers can make educated guesses about the relevant and irrelevant aspects of their population. However, they must concede that those guesses may be wrong. Consequently, if a researcher imagines a long run of repeated sampling, then they must imagine that they would make incorrect decisions about their null hypothesis due to not only Type I errors and Type II errors, but also Type III errors - errors caused by accidentally sampling from populations that are substantively different to their underspecified alternative and null populations. As Dennis et al. (2019) recently explained, "the 'Type 3' error of basing inferences on an inadequate model family is widely acknowledged to be a serious (if not fatal) scientific drawback of the Neyman-Pearson framework."

To be clear, the Neyman-Pearson approach does consider Type III errors. However, it considers them outside of each long run of repeated sampling. It does not allow Type III errors to occur inside a long run of repeated sampling, where the sampling must always be from a correctly specified family of "admissible" populations (Neyman, 1977, p. 106; Neyman & Pearson, 1933, p. 294). In my paper, I argue that researchers are unable to imagine a long run of repeated sampling from the same or equivalent populations as their current population because they are unclear about the relevant and irrelevant characteristics of their current population. Consequently, they are unable to rule out Type III errors within their imagined long run.

Following Fisher, I contrast scientific researchers with quality controllers in industrial production settings. Unlike researchers, quality controllers have clear knowledge about the relevant and irrelevant characteristics of their populations. For example, they are given a clear and unequivocal definition of Batch 57 on a production line, and they don't consider re-conceptualizing Batch 57 as including or excluding other features. They also know which aspects of their testing procedure are relevant and irrelevant, and they are provided with precise quality control standards that allow them to know, for sure, their smallest effect size of interest. Consequently, the Neyman-Pearson approach is suitable for quality controllers because quality controllers can imagine a testing process that repeatedly draws random samples from the same population over and over again. In contrast, the Neyman-Pearson approach is not appropriate in scientific investigations because researchers do not have a clear understanding of the relevant and irrelevant aspects of their populations, their tests, or the smallest effect size that represents their population. Indeed, they are "researchers" because they are "researching" these things. Hence, it is researchers' self-declared ignorance and doubt about the nature of their populations that renders Neyman-Pearson long run error rates scientifically meaningless.

In my article, I also consider Pearson (1947) and Neyman's (1977) responses to Fisher's "repeated sampling" criticism, focusing in particular on how it affected their conceptualization of the alpha level (nominal Type I error rate).

Pearson (1947) proposed that the alpha level can be considered in relation to a "hypothetical repetition" of the same test. However, as discussed above, this interpretation is only appropriate when test users are sure about all of the equivalent and non-equivalent aspects of their testing method and population. By definition, test users who adopt the role of "researcher" are not 100% sure about these things. As Fisher (1956, p. 78) explained, for researchers, “the population in question is hypothetical,…it could be defined in many ways, and…the first to come to mind may be quite misleading.”

Echoing Neyman and Pearson (1933), Pearson (1947) also suggested that the alpha level can be interpreted as a personal "rule" that guides researchers’ behavior during hypothesis testing across substantively different populations. However, this interpretation fails to acknowledge that a researcher may break their personal rule and use different alpha levels in different testing situations. As Fisher (1956, p. 42) put it, "no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses."

Addressing the rule-breaking problem, Neyman (1977) agreed with Fisher (1956) that the same researcher could use different alpha levels in different circumstances. However, he proposed that a researcher's average alpha level could be viewed as an indicator of their typical Type I error rate. So, for example, if Jane has an average alpha level of .050, and Tina has an average alpha level of .005, then we know that Jane will make more Type I errors than Tina during the course of her research career. Critically, however, these two researchers’ average alpha levels tell us nothing about the Type I error rates of specific tests of specific hypotheses, and in that sense they are scientifically irrelevant. Hence, although Tina may have an average alpha of .005 over the course of her career, her alpha level for Test 1 of Hypothesis A may be .10, .001, or any other value. As scientists, rather than metascientists, we should be more interested in the nominal Type I error rate of Test 1 of Hypothesis A than in the typical Type I error rate of Tina.

I conclude that neither Neyman nor Pearson adequately rebutted Fisher’s “repeated sampling” criticism, and that their alternative interpretations of alpha levels are lacking. I then briefly outline Fisher’s own significance testing approach and consider how it avoids his "repeated sampling" criticism.

For further information, please see:

Rubin, M. (2020). “Repeated sampling from the same population?” A critique of Neyman and Pearson’s responses to Fisher. European Journal for Philosophy of Science, 10, Article 42, 1-15. https://doi.org/10.1007/s13194-020-00309-6 *Publisher's open access view only version* *Author’s version*

I also discuss the differences between the Fisherian and Neyman-Pearson approaches to hypothesis testing here.

Does Preregistration Improve the Interpretablity and Credibility of Research Findings?

noreply@blogger.com (Mark Rubin) — Sun, 20 Sep 2020 00:43:00 +0000

Preregistration entails researchers registering their planned research hypotheses, methods, and analyses in a time-stamped document before they undertake their data collection and analyses. This document is then made available with the published research report in order to allow readers to identify discrepancies between what the researchers originally planned to do and what they actually ended up doing. In a recent article (Rubin, 2020), I question whether this historical transparency facilitates judgments of credibility over and above what I call the contemporary transparency that is provided by (a) clear rationales for current hypotheses and analytical approaches, (b) public access to research data, materials, and code, and (c) demonstrations of the robustness of research conclusions to alternative interpretations and analytical approaches.

My article covers issues such as HARKing, multiple testing, p-hacking, forking paths, exploratory analyses, optional stopping, researchers’ biases, selective reporting, test severity, publication bias, and replication rates. I argue for a nuanced approach to these issues. In particular, I argue that only some of these issues are problematic, and only under some conditions. I also argue that, when they are problematic, these issues can be identified via contemporary transparency per se.

I conclude that preregistration’s historical transparency does not facilitate judgments about the credibility of research findings when researchers provide contemporary transparency. Of course, in many cases, researchers do not provide a sufficient degree of contemporary transparency (e.g., no open research data or materials), and in these cases preregistration’s historical transparency may provide some useful information.

However, I argue that historical transparency is a relatively narrow, researcher-centric form of transparency because it focuses attention on the predictions made by specific researchers at a specific point in time. In contrast, contemporary transparency allows research data to be considered from multiple, unplanned, theoretical and analytical perspectives while maintaining a high degree of research credibility. Hence, I suggest that the open science movement should push more towards contemporary transparency and less towards historical transparency.

For further information, please see:

Rubin, M. (2020). Does preregistration improve the credibility of research findings? The Quantitative Methods in Psychology, 16(4), 376–390. https://doi.org/10.20982/tqmp.16.4.p376

For more information about my work in this area, please see: https://sites.google.com/site/markrubinsocialpsychresearch/replication-crisis

What Type of Type I Error?

noreply@blogger.com (Mark Rubin) — Fri, 13 Mar 2020 03:26:00 +0000

In a recent paper (Rubin, 2021), I consider two types of replication in relation to two types of Type I error probability. First, I consider the distinction between exact and direct replications. Exact replications duplicate all aspects of a study that could potentially affect the original result. In contrast, direct replications duplicate only those aspects of the study that are thought to be theoretically essential to reproduce the original result.

Second, I consider two types of Type I error probability. The Neyman-Pearson Type I error rate refers to the maximum frequency of incorrectly rejecting a null hypothesis if a test was to be repeatedly reconducted on a series of different random samples that are all drawn from the exact same null population. Hence, the Neyman-Pearson Type I error rate refers to a long run of exact replications. In contrast, the Fisherian Type I error probability is the probability of incorrectly rejecting a null hypothesis in relation to a hypothetical population that reflects the relevant characteristics of the particular sample under consideration. Hence, the Fisherian Type I error rate refers to a one-off sample rather than a series of samples that are drawn during a long run of exact replications.

I argue that social science deals with units of analysis (people, social groups, and social systems) that change over time. As the Greek philosopher Heraclitus put it: “No man ever steps in the same river twice, for it’s not the same river and he’s not the same man.” Rivers and men are what Schmidt (2009, p. 92) called "irreversible units" in that they are complex time-sensitive systems that accumulate history. The scientific investigation of these irreversible units cannot proceed on the assumption that exact replications are possible. Consequently, the Neyman-Pearson Type I error rate is irrelevant in social science, because it relies on a concept of exact replication that cannot take place in the case of people, social groups, and social systems. Why should social scientists be interested in an error rate for an impossible process of resampling from the same fixed and unchanging population?

"No [wo]man ever steps in the same river twice, for it’s not the same river and [s]he’s not the same [wo]man" ( Heraclitus)

I argue that the Fisherian Type I error probability is more appropriate in social science because it refers to one-off samples from hypothetical populations. In this case, researchers recognise that every sample comes from a potentially different population. Hence, researchers can apply the Fisherian Type I error probability to each sample-specific provisional decision that they make about rejecting the same substantive null hypothesis in a series of direct replications.

I conclude that the replication crisis may be partly (not wholly) due to researchers’ unrealistic expectations about replicability based on their consideration of the Neyman-Pearson Type I error rate across a long run of exact replications.

For further information, please see:

Rubin, M. (2021). What type of Type I error? Contrasting the Neyman-Pearson and Fisherian approaches in the context of exact and direct replications. Synthese, 198, 5809–5834. *Publisher’s version* *Self-archived version*

Do p Values Lose their Meaning in Exploratory Analyses?

noreply@blogger.com (Mark Rubin) — Thu, 12 Mar 2020 04:39:00 +0000

In Rubin (2017), I consider the idea that p values lose their meaning (become invalid) in exploratory analyses (i.e., non-preregistered analyses). I argue that this view is correct if researchers aim to control a familywise error rate that includes all of the hypotheses that they have tested, or could have tested, in their study (i.e., a universal, experimentwise, or studywise error rate). In this case, it is not possible to compute the required familywise error rate because the number of post hoc hypotheses that have been tested, or could have been tested, during exploratory analyses in the study is unknown. However, I argue that researchers are rarely interested in a studywise error rate because they are rarely interested in testing the joint studywise hypothesis to which this error rate refers.

For example, imagine that a researcher conducted a study in which they explored the associations between body weight and (1) gender, (2) age, (3) ethnicity, and (4) social class. This researcher is unlikely to be interested in a studywise null hypothesis that can be rejected following a significant result for any of their four tests, because this joint null hypothesis is unlikely to relate to any meaningful theory. Which theory proposes that gender, age, ethnicity, and social class all predict body weight for the same theoretical reason? And, if the researcher is not interested in making a decision about the studywise null hypothesis, then there is no need for them to lower the alpha level (α; the significance threshold) for each of their four tests (e.g., from α = .050 to α = .050/4 or .0125) in order to maintain the Type I error rate for their decision about the studywise hypothesis at α = .050. Instead, the researcher can test each of the four different associations individually (i.e., each at α = .050) in order to make a separate, independent claim about each of four theoretically independent hypotheses (e.g., "male participants weighed more than female participants, p = .021"). By analogy, a woman who takes a pregnancy test does not need to worry about the familywise error rate that either her pregnancy test, her fire alarm, or her email spam filter will yield a false positive result because the associated joint hypothesis is nonsensical.

Sometimes it doesn't make sense to combine different
hypotheses as part of the same family!

Researchers should only be concerned about the familywise error rate of a set of tests when that set refers to the same theoretically meaningful joint hypothesis. For example, a researcher who undertakes exploratory analyses should be concerned about the familywise error rate for the hypothesis that men weigh more than women if they use four different measures of weight, and they are prepared to accept a single significant difference on any of those four measures as grounds for rejecting the associated joint null hypothesis. In this case, they should reduce their alpha level for each constituent test (e.g., to α/4) in order to maintain their nominal Type I error rate for the joint hypothesis at α. Based on this reasoning, I argue that p values do not lose their meaning in exploratory analyses because (a) researchers are not usually interested in the studywise error rate, and (b) they are able to transparently and verifiably specify and control the familywise error rates for any theoretically meaningful post hoc joint hypotheses about which they make claims.

I also recommend that researchers undertake a few basic open sciences practices during exploratory analyses in order to alleviate concerns about potential p-hacking: (1) List all of the variables in the research study. (2) Undertake a sensitivity analysis to demonstrate that the research results are robust to alternative analytical approaches. (3) Make the research data and materials publicly available to allow readers to check whether the results for any relevant measures have been omitted from the research report.

For further information, please see:

Rubin, M. (2017). Do p values lose their meaning in exploratory analyses? It depends how you define the familywise error rate. Review of General Psychology, 21, 269-275. *Publisher’s version* *Self-archived version*

The Costs of HARKing: Does it Matter if Researchers Engage in Undisclosed Hypothesizing After the Results are Known?

noreply@blogger.com (Mark Rubin) — Wed, 11 Mar 2020 05:33:00 +0000

While no-one's looking, a Texas sharpshooter fires his gun at a barn wall, walks up to his bullet holes, and paints targets around them. When his friends arrive, he points at the targets and claims he’s a good shot (de Groot, 2014; Rubin, 2017b). In 1998, Norbert Kerr discussed an analogous situation in which researchers engage in undisclosed hypothesizing after the results are known or HARKing. In this case, researchers conduct statistical tests, observe their results (bullet holes), and then construct post hoc hypotheses (paint targets) to fit these results. In their research reports, they then pretend that their post hoc hypotheses are actually a priori hypotheses. This questionable research practice is thought to have contributed to the replication crisis in science (e.g., Shrout & Rodgers, 2018), and it provides part of the rationale for researchers to publicly preregister their hypotheses before they conduct their analyses (Wagenmakers et al., 2012). In a recent BJPS article (Rubin, 2019), I discuss the concept of HARKing from a philosophical standpoint and then undertake a critical analysis of Kerr’s 12 potential costs of HARKing.

Source: Dirk-Jan Hoek. https://www.flickr.com/photos/23868780@N00/7374874302

I begin my article by noting that scientists do not make absolute, dichotomous judgements about theories and hypotheses being “true” or “false.” Instead, they make relative judgements about theories and hypotheses being more or less true that other theories and hypotheses in accounting for certain phenomena. These judgements can be described as estimates of relative verisimilitude (Cevolani & Festa, 2018).

I then note that a HARKer is obliged to provide a theoretical rationale for their secretly post hoc hypothesis in the Introduction section of their research report. Despite being secretly post hoc, this theoretical rationale provides a result-independent basis for an initial estimate of the relative verisimilitude of the HARKed hypothesis. (The rationale is "result-independent" because it doesn't formally refer to the current result. If it did, then the rationale's post hoc status would no longer be a secret!) The current result can then provide a second, epistemically independent basis for adjusting this initial estimate of verisimilitude upwards or downards (for a similar view, see Lewandowsky, 2019; Oberauer & Lewandowsky, 2019). Hence, readers can estimate the relative verisimilitude of a HARKed hypothesis (a) without taking the current result into account and (b) after taking the current result into account, even if they have been misled about when the researcher deduced the hypothesis. Consequently, readers can undertake a valid updating of the estimated relative verisimilitude of the hypothesis even though, unbeknowst to them, it has been HARKed. Importantly, there's no “double-counting” (Mayo, 2008), “circular reasoning” (Nosek et al., 2018), or violation of the use novelty principle here (Worrall, 1985, 2014), because the current result has not been used in the formal theoretical rationale for the HARKed hypothesis. Consequently, it's legitimate to use the current result to change (increase or decrease) the initial estimate of the relative verisimilitude of that hypothesis.

To translate this reasoning to the Texas sharpshooter analogy, it's necessary to distinguish HARKing from p-hacking. If our sharpshooter painted a new target around his stray bullet hole but retained his substantive claim that he's “a good shot,” then he'd be similar to a researcher who conducted multiple statistical tests and then selectively reported only those results that supported their original a priori substantive hypothesis. Frequentist researchers would call this researcher a “p-hacker” rather than a HARKer (Rubin, 2017b, p. 325; Simmons et al., 2011). To be a HARKer, researchers must also change their original a priori hypothesis or create a totally new one. Hence, a more appropriate analogy is to consider a sharpshooter who changes both their statistical hypothesis (i.e., paints a new target around their stray bullet hole) and their broader substantive hypothesis (their claim). Let's call her Jane!

Jane initially believes “I’m a good shot” (H1). However, after missing the target that she was aiming for (T1), she secretly paints a new target (T2) around her bullet hole and declares to her friends: "I'm a good shot, but I can't adjust for windy conditions. I aimed at T1, but there was a 30 mph easterly cross-wind. So, I knew I'd probably hit T2 instead." In this case, Jane has generated a new, post hoc hypothesis (H2) and passed it off as an a priori hypothesis. Note that, unlike our original Texas sharpshooter, Jane isn't being deceptive about her procedure here (i.e., what she actually did): It's true that she aimed her gun at T1. She's only being deceptive about the a priori status of H2, which she secretly developed after she missed T1 (i.e., she's HARKing). Importantly, however, Jane's deception doesn't prevent her friends from making a valid initial estimate of the verisimilitude of her HARKed hypothesis and then updating this estimate based on the location of her bullet hole:

"We know that Jane's always trained indoors. So, it makes sense that she hasn't learned to adjust for windy conditions. We also know that (a) Jane was aiming at T1, and (b) there was a 30 mph easterly cross-wind. Our calculations show that, if someone was a good shot, and they were aiming at T1, but they didn't adjust for an easterly 30 mph cross-wind, then their bullet would hit T2's location. So, our initial estimated verismilitude for H2 is relatively high. The evidence shows that Jane's bullet did, in fact, hit T2. Consequently, we can tentatively increase our support for H2: Jane appears to be a good shot who can't adjust for windy conditions. Of course, we'd also want to test H2 again by asking Jane to hit targets on both windy and non-windy days!"

We can predict the location of the sharpshooter's bullet hole on the basis of her (secretly HARKed) hypothesis that she is a good shot but cannot adjust for windy conditions. We can then use the location of the bullet hole to increase or decrease our estimated relative verisimilitude for this prediction. Source: https://pixabay.com/photos/woman-rifle-shoot-gun-weapon-2577104/

The second part of my paper provides a critical analysis of Kerr’s (1998) 12 costs of HARKing. For further information, please see:

Rubin, M. (2022). The costs of HARKing. The British Journal for the Philosophy of Science, 73. https://doi.org/10.1093/bjps/axz050 *Publisher’s free access* *Self-archived version*

References

Cevolani, G., & Festa, R. (2018). A partial consequence account of truthlikeness. Synthese. http://dx.doi.org/10.1007/s11229-018-01947-3
de Groot, A. D. (2014). The meaning of “significance” for different types of research (E. J. Wagenmakers, D. Borsboom, J. Verhagen, R. Kievit, M. Bakker, A. Cramer, . . . H. L. J. van der Maas). Acta Psychologica, 148, 188–194. http://dx.doi.org/10.1016/j.actpsy.2014.02.001
Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2, 196-217. http://dx.doi.org/10.1207/s15327957pspr0203_4
Lewandowsky, S. (2019). Avoiding Nimitz Hill with more than a little red book: Summing up #PSprereg. https://featuredcontent.psychonomic.org/avoiding-nimitz-hill-with-more-than-a-little-red-book-summing-up-psprereg/
Mayo, D. G. (2008). How to discount double-counting when it counts: Some clarifications. The British Journal for the Philosophy of Science, 59, 857–879.
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115, 2600-2606. http://dx.doi.org/10.1073/pnas.1708274114
Oberauer, K., & Lewandowsky, S., (2019). Addressing the theory crisis in psychology. Psychonomic Bulletin & Review. http://dx.doi.org/10.3758/s13423-019-01645-2
Rubin, M. (2017a). An evaluation of four solutions to the forking paths problem: Adjusted alpha, preregistration, sensitivity analyses, and abandoning the Neyman-Pearson approach. Review of General Psychology, 21, 321-329. http://dx.doi.org/10.1037/gpr0000135 *Self-archived version*
Rubin, M. (2017b). When does HARKing hurt? Identifying when different types of undisclosed post hoc hypothesizing harm scientific progress. Review of General Psychology, 21, 308-320. http://dx.doi.org/10.1037/gpr0000128 *Self-archived version*
Shrout, P. E., & Rodgers, J. L. (2018). Psychology, science, and knowledge construction: Broadening perspectives from the replication crisis. Annual Review of Psychology, 69, 487-510. http://dx.doi.org/10.1146/annurev-psych-122216-011845
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359–1366. http://dx.doi.org/10.1177/0956797611417632
Wagenmakers, E. J., Wetzels, R., Borsboom, D., van der Maas, H. L., & Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspectives on Psychological Science, 7, 632-638. http://dx.doi.org/10.1177/1745691612463078
Worrall, J. (1985). Scientific discovery and theory-confirmation. In J. C. Pitt (Ed.), Change and progress in modern science: Papers related to and arising from the Fourth International Conference on History and Philosophy of Science (pp. 301–331). Dordrecht, the Netherlands: Reidel. http://dx.doi.org/10.1007/978-94-009-6525-6_11
Worrall, J. (2014). Prediction and accommodation revisited. Studies in History and Philosophy of Science, 45, 54–61. http://dx.doi.org/10.1016/j.shpsa.2013.10.001

The Effects of Sexism on Women Miners' Mental Health and Job Satisfaction

noreply@blogger.com (Mark Rubin) — Mon, 12 Jun 2017 23:32:00 +0000

In the mining industry, women make up only 19.4% of the workers in Canada, 16.4% in Australia, and 13.3% in the USA (Catalyst, 2015). In the present research, we investigated women’s experiences of sexism in this male-dominated industry and how these experiences related to women’s mental health and job satisfaction.

We surveyed 263 women miners from an Australian-based mining company that has operations in Australia, Africa, South America, and South East Asia. Participants responded to items about sexism, sense of belonging, mental health, and job satisfaction.

Our research focused on two types of sexism: organizational sexism and interpersonal sexism. Organizational sexism refers to structural inequalities in an organization that are connected with opportunities for promotion and career progression, job stability, training, pay, competence, work-life balance, and performance standards. We found that women miners who felt relatively disadvantaged on these dimensions reported poorer mental health and job satisfaction. Hence, a potential strategy to improve women miners’ mental health and job satisfaction may be to reduce their perceived and actual disadvantage on these dimensions. This might be achieved through a combination of structural changes in the workplace (e.g., more opportunities for women miners’ career progression) and/or greater transparency in the gender-based similarities on these dimensions (e.g., publication of workforce statistics demonstrating equality of pay).

Interpersonal sexism refers to inappropriate images of women in the workplace, sexual harassment, and sexist comments. Like organizational sexism, interpersonal sexism was negatively related to mental health and job satisfaction. Interpersonal sexism is more ingrained in wider intergender relations in society, and addressing interpersonal sexism effectively is likely to require a partnership between employers and (male and female) employees.

A third variable that was associated with women miners' mental health and job satisfaction was sense of belonging in the industry. This variable mediated the effects of organizational sexism on job satisfaction. Hence, an additional approach towards improving women miners’ job satisfaction may be to increase their sense of belonging. An increased sense of belonging may be achieved by promoting community events both within the female group of miners (i.e., as a group of “women miners”) and within the industry as whole (i.e., women identifying as “miners”).

We also found some interesting cross-country differences. Women who worked at Australian mine sites reported significantly less organizational and interpersonal sexism and fewer mental health problems than did women who worked at African, South American, and South East Asian worksites. These differences may reflect cross-cultural differences, with Australia’s more progressive Western culture prescribing less sexism and better mental health practices in the workplace.

It is important to note that our study’s cross-sectional correlational design prevents clear conclusions regarding the causal direction of the associations between the variables that we studied. Future research may wish to use longitudinal research designs to address this issue.

For further information about this research, please see the following journal article:

Rubin. M., Subasic, E., Giacomini, A., & Paolini, S. (2017). An exploratory study of the relations between women miners’ gender-based workplace issues and their mental health and job satisfaction. Journal of Applied Social Psychology. doi: 10.1111/jasp.12448

For an open access self-archived version, please click here.

If Collectivists like Social Groups, and Cities are Social Groups, do Collectivists like Cities?

noreply@blogger.com (Mark Rubin) — Mon, 06 Mar 2017 04:46:00 +0000

Do you like the place where you live? Maybe it's got great architecture, it's clean and crime free, the housing is cheap, and/or the nightlife is good? But maybe your liking for the place is also related to something else - your own tendency to identify with social groups? In some recent research, my colleagues and I investigated this issue by considering the relations between collectivism, city identification, and city evaluation.

Collectivism is a sociocultural orientation towards perceiving the self and others as belonging to social groups, and it influences the extent to which people identify with social groups. The more collectivist you are, the more strongly you identify with social groups. Prior research has found that people who identify strongly with a place tend to like that place more. Hence, it is possible that people who are relatively high in collectivism identify strongly with the place that they live and, consequently, evaluate that place more positively.

To investigate this possibility, my colleagues and I sampled 1,660 residents of four cities in three countries: Newcastle, Australia; Sydney, Australia; Paris, France; and Istanbul, Turkey. Participants completed an online survey containing measures of collectivism, city identification, and city evaluation. We found that, within each city sample and across the combined samples, a specific measure of collectivism called collective interdependent self-construal was positively related to city evaluation. We also found that city identification mediated this relation. Hence, people's general tendency to construe social groups as part of their self (collectivism; e.g., “The groups I belong to are an important reflection of who I am”) predicted their level of identification with their city (city identification; e.g., "I identify with other people living in Sydney"), which in turn helped to explain their positive appraisal of that city (city evaluation).

A key limitation of our research is that it employed a cross-sectional correlational design, which prevented us from drawing clear conclusions about the causal direction of the relations that we observed. Future research should employ a longitudinal research design in order to provide clearer conclusions on this issue.

The present research results imply that the social psychological group processes that are responsible for people's identification with and evaluation of social groups based on gender, ethnicity, nationality, etc. may also apply to cities because, at their base, cities are social groups.

For further information please see the following journal article:

Rubin, M., Badea, C., Condie, J., Mahfud, Y., Morrison, T., & Peker, M. (2017). Individual differences in collectivism predict city identification and city evaluation in Australian, French, and Turkish cities Journal of Environmental Psychology, 50, 9-16 DOI: 10.1016/j.jenvp.2017.01.007

For a self-archived version, please click here.

Lower Social Status Causes Less Social Contact and More Depression in Uni Students

noreply@blogger.com (Mark Rubin) — Wed, 09 Nov 2016 23:09:00 +0000

A person’s subjective social status reflects how they rank themselves relative to others in their community. Social status can be considered to be a psychological dimension of social class and socioeconomic status, and it has been shown to be positively related to mental health: The higher one’s perceived social status, the better one’s mental health. However, the process underlying this relation is unclear. In some recent research, my colleagues and I considered social contact as a potential explanatory variable. We investigated the possibility that lower social class reduces the amount of social contact that people have with others, and that this reduced social contact leads to poorer mental health.

We asked 314 first-year undergraduate students at an Australian university to complete an online survey in which they indicated their perceived social status in terms of their money, education, and occupation relative to other people in Australia. Students also indicated the amount of social contact that they had with university friends during the past week (e.g., face-to-face meetings, social media, phone, text messages, etc.). Finally, students reported on the depression, anxiety, and stress that they had experienced over the past week. Students completed the survey twice at least 11 weeks apart. This longitudinal research design allowed us to reach firmer conclusions about the causality.

Our results showed that students who perceived themselves to be lower in social status had less social contact with university friends and greater depressive symptoms. Furthermore, lack of social contact helped to explain (mediated) the relation between social status and depressive symptoms. Notably, these effects were restricted to depressive symptoms and did not generalize to either anxiety or stress, perhaps because depression is more of an interpersonal disorder than either anxiety or stress.

Our research findings suggest that lower class students may be more depressed partly because they have less social contact with friends at university. One method of addressing this problem is to increase the amount of social contact with other university students, perhaps via online social media.

More generally, the present results highlight the importance of social contact in the relation between social status and mental health. Having a lower social status appears to be detrimental to mental health partly because it impacts negatively on one's social relationships. Beneficial mental health interventions may attempt to leverage this social process by improving the quality and quantity of people's social relationships.

For more information, please see the following journal article:

Rubin, M., Evans, O., & Wilkinson, R. (2016). A longitudinal study of the relations among university students' subjective social status, social contact with university friends, and mental health and well-being Journal of Social and Clinical Psychology, 35 (9), 722-737. doi: 10.1521/jscp.2016.35.9.722

For a self-archived version of the article, please click here.

This study was supported by a research grant from the Australian Government’s Department of Education Higher Education Participation and Partnerships Programme National Priorities Pool.

Older Women, Deeper Learning, and Greater Satisfaction at University

noreply@blogger.com (Mark Rubin) — Mon, 12 Sep 2016 02:41:00 +0000

“The ‘ideal learner’, from an institutional view, is young, well-resourced and not bound by conflicting family obligations” (Mallman & Lee, 2014, p. 3). However, some recent research published in the Journal of Diversity in Higher Education challenges this view. If we consider “ideal learners” to be active, independent, critical, and inquisitive students who go beyond the set curriculum, then older women should be regarded as more ideal than younger women and men of all ages.

In our research, we asked 983 Australian undergraduate students to complete a research survey that contained measures of learning approach and degree satisfaction. We found that older women showed the greatest deep learning. In other words, they were most likely to go beyond the set curriculum and relate the material that they studied to its wider context. This greater deep learning helped to explain older women’s greater satisfaction with their degree.

This study corroborates previous qualitative research in this area, which has found that older women have more intrinsic motives for undertaking higher education. For example, consider the following quotes from mature-aged female university students:

“I’m not doing this because I want to change my job. I’m not doing this because I think it’s going to get me any more money, I’m doing it because I want to learn” (McCune, Hounsell, Christie, Cree, & Tett, 2010, p. 696).

“I’m not doing it for vocational reasons. I’m doing it for me” (Reay, 2003, p. 304).

For more information, please see the following journal article:

Rubin, M., Scevak, J., Southgate, E., Macqueen, S., Williams, P., & Douglas, H. (2016). Older women, deeper learning, and greater satisfaction at university: Age and gender predict university students’ learning approach and degree satisfaction. Journal of Diversity in Higher Education. doi: 10.1037/dhe0000042

For a self-archived version of the article, please click here

Mates Make Groups for Individualists But Not for Collectivists

noreply@blogger.com (Mark Rubin) — Fri, 11 Mar 2016 03:08:00 +0000

Humans are an incredibly groupy type of animal. We form psychologically-meaningful groups based on our gender, age, nationality, religion, politics, skin colour, occupation, sexual inclination, and sports teams, to name just a few. Even in the artificial environment of psychology labs, people will identify with groups based on their totally random allocation to “Group A.” Indeed, they will declare that they feel “more similar” to Group A members than to Group B members, and even discriminate in favour of Group A members and against Group B members! But does everyone around the world identify with groups in the same way?

To investigate this issue, my colleagues and I conducted two studies in which we compared individualists (people from Western countries such as Australia and the USA) with collectivists (people from non-Western countries such as China and India). We measured people’s interpersonal closeness with other group members (in-group ties) and the degree to which they felt similar to other group members (perceived self-to-group similarity; a key indicator of social identification). In both studies, we found that interpersonal closeness was a significant positive predictor of perceived self-to-group similarity. In other words, the closer people felt to other people in their groups, the more similar they felt to them. Critically, however, this positive relation only held for individualists. There was no significant relationship between perceived interpersonal closeness and self-to-group similarity among the collectivists in our samples.

This suggests that interpersonal closeness is a stronger predictor of social identification among people from individualist cultures than among people from collectivist cultures. This is an important finding because social identification predicts prejudice and stereotyping, and so a better understanding of cross-cultural differences in the basis for social identification may help to improve the effectiveness of social interventions that reduce prejudice and stereotyping. For example, interventions based on interpersonal closeness may be more effective among people from individualist Western countries like the USA than among people from collectivist non-Western countries like China.

Our research helps to explain the basis for social identification among individualists. But it does leave an important question unanswered: On what basis do collectivists form their social identities? If interpersonal ties with other group members are not crucial, then what is? We believe that group harmony and sense of duty may represent two potential answers to this question.

For further information about this research, please see the following journal article:

Rubin, M., Milanov, M., & Paolini, S. (2016). Uncovering the diverse cultural bases of social identity: Ingroup ties predict self-stereotyping among individualists but not among collectivists Asian Journal of Social Psychology, 19 (3), 225-234 DOI: 10.1111/ajsp.12137 Self-Archived Version

For a You Tube video explaining the research, please click here.

Social Class Differences in Mental Health: Do Parenting Style and Friendship Play a Role?

noreply@blogger.com (Mark Rubin) — Thu, 08 Oct 2015 03:30:00 +0000

It is now well-established that social class and socioeconomic status (SES) are positively related to mental health. For example, a meta-analysis of 51 studies found that people with a higher SES are less likely to be depressed than people with a lower SES. However, researchers remain unclear about the specific processes that underlie the relation between social class and depression.

In some recent research, Benjamin Kelly and I investigated the potential roles of parenting style and friendship in explaining the relationship between social class and mental health. We predicted that people from higher social class backgrounds experience a warmer and more responsive parenting style from their mother and father than students from lower social class backgrounds, who experience a more restrictive, disciplinary, and controlling parenting style. We also predicted that a more responsive parenting style promotes the development of a range of socially-beneficial psychological resources such as self-management and social competence, which enable people to develop more and better quality friendships. In turn, better friendships were expected to lead to better mental health and well-being due to their stress-buffering effects and beneficial effects on self-esteem, sense of belonging, and perceived social support. Our model is outlined below.

We tested our model using a sample of 397 psychology undergraduate students at a large public Australian university. Consistent with our predictions, we found that:

Students with higher social class experienced better mental health and well-being than students with lower social class.
Students with higher social class reported their parents to be the warmer and less disciplinary than students with lower social class.
Students with higher social class reported better friendships social integration at university than students with lower social class.
Students who had experienced a warmer and less disciplinary parenting style reported (a) better friendships and (b) better mental health.
Friendship and social integration mediated (statistically explained) the relation between social class and mental health.

Based on this evidence, we concluded that working-class parenting styles may inhibit the development of socially-supportive friendships that protect against mental health problems at university.

Our single cross-sectional study only provides preliminary evidence, and further longitudinal studies that sample from different populations are required in order to arrive at firmer conclusions. However, our initial results suggest two potential interventions for reducing social class differences in mental health in university communities and, potentially other communities if our effects generalize to these communities. The first is to increase working-class people’s social integration, and I have discussed this issue with regards to working-class students at university elsewhere. The second, more distal intervention is to alter the working-class parenting style in order to make it warmer, more responsive, and less disciplinary. However, any such parenting style intervention needs to take into consideration the impact of an array of sociocultural factors, and we consider these in some depth in our article.

For more information about this research, please see the following open-access article:

Rubin, M., & Kelly, B. (2015). A cross-sectional investigation of parenting style and friendship as mediators of the relation between social class and mental health in a university community International Journal for Equity in Health, 14 (1), 1-11 DOI: 10.1186/s12939-015-0227-2

Party On! (If You're Middle-Class and Young): Age Differences Explain Social Class Differences in University Friendships

noreply@blogger.com (Mark Rubin) — Fri, 12 Dec 2014 08:32:00 +0000

In a recent meta-analytic review, I found that working-class students are less integrated at university than their middle-class peers. I offered up nine potential explanations for this working-class exclusion effect. It turns out that one of the simplest explanations in this list is also the most promising. It’s all to do with age.

Working-class students tend to be older than middle-class students. Why? Most likely because they don’t tend to go to university immediately after school but instead get out into the real world and earn a bit of money before accumulating the financial security to upskill in higher education (James, 2000). So, there tends to be a negative correlation between social class and age at university. Younger students tend to be middle-class school leavers, and older students tend to be more mature-aged, worldly-wise students.

In some recent research, Chrysalis Wright and I found that age differences help to explain social class differences in students’ friendships. We surveyed 376 first-year undergraduate psychology students, asking them how many friends they had at uni and how much their friends mattered to their identity. We found that working-class students had fewer identity-relevant friends and regarded the friends that they did have as being less relevant to their identity. Moreover, we found that age differences explained this social class effect.

Put Your Hands Up!...Unless you have kids and no money to go out!

So what? Well, as I’ve argued elsewhere, a potentially important method of improving working-class students’ academic outcomes is to improve the quality and quantity of their university friendships. University friends can help to explain coursework assignments, remind one another about due dates, act as study buddies, provide a shoulder to cry on during stressful periods, and instil a sense of belonging and institutional identification that increases degree commitment and persistence. Research has shown that working-class students are most in need of this type of support.

What our current research shows is that any attempts at improving working-class students’ friendships need to take their more mature age into account. So, night-time discos and parties might be fun for the 20-somethings, but it’s not a realistic approach to social integration for the more mature-aged, child-caring, working-class students. Likewise, on-campus accommodation is an excellent method of improving social integration at university, but this tried-and-tested approach needs to be adapted to take into account students’ social class, age, and concomitant family commitments.

Last thing before I go! Our research found that working-class students had not only fewer friends than middle-class students but also less desire and concern about making new friends. Hence, simply providing opportunities for friendship-building is unlikely to be sufficient. Universities also need to motivate older working-class students to participate in social life at university, perhaps through the use of information campaigns that highlight the informational and emotional support that is provided by university friends.

For more information about our research, please see the following paper:

Rubin, M., & Wright, C. (2014). Age differences explain social class differences in students’ friendship at university: Implications for transition and retention Higher Education DOI: 10.1007/s10734-014-9844-8

Please click here for a self-archived version.

How You Feel About People is Related to How You Feel About Cities

noreply@blogger.com (Mark Rubin) — Wed, 15 Oct 2014 08:38:00 +0000

You take delight not in a city's seven or seventy wonders, but in the answer it gives to a question of yours. (Calvino, 1978, p. 44).

There are numerous structural factors that influence people’s attitudes towards cities, including the city’s architecture, size, infrastructure, transport, crime rates, population density, and quality of housing, to name just a few. However, as the Italian writer Calvino (1978) alluded to in his book Invisible Cities, these factors may be constituents of broader sociocultural “questions” that people ask about their cities. For example, residents’ concern about the transport and entertainment venues of a city might form part of a broader social psychological concern about the potential for the city to accommodate their need to meet friends and socialize with others. Alternatively, people might focus on a city’s economy and job opportunities because they are concerned about the ability of the city to meet their needs for personal income and wealth.

Hong Kong - Why Would You Want to Live There?

In some recently published research, Dr Tessa Morrison and I predicted that individual differences in individualism and collectivism operate as important predictors of people's city needs and goals. Individualism and collectivism are sociocultural orientations towards treating the self and others as individuals or group members respectively: Individualists see themselves and others as being self-reliant, autonomous, and independent, whereas collectivists are more interdependent and concerned about their social groups, including their family, friends, and community. We predicted that these dispositional orientations towards the self and others might also influence how people feel about cities.

To test our predictions, we asked 148 psychology undergraduate students to take virtual guided tours around one of four Utopian historical cities - cities that had never been built and were unfamiliar to our participants. YouTube videos of the four guided tours can be viewed here: Christianopolis, City of the Sun, New Harmony, and Victoria, and the picture below shows a scene from one of the tours. Participants then evaluated the cities’ liveability and environmental quality and completed measures of individualism and collectivism.

Consistent with our predictions, people with a strong sense of self-responsibility (a form of individualism) tended to evaluate the virtual cities in terms of their potential to meet the goal of acquiring resources, income, and wealth, whereas people with a strong sense of collectivism tended to evaluate the cities in terms of their potential to provide community and a sense of connection with others.

Scene from a virtual tour around the Utopian city of Victoria

To paraphrase Calvino (1978), city evaluation may be based on the answers that cities provide to our questions. However, our research suggests that different types of people have different types of questions. Individualists appear to ask: “can this city enhance my personal wealth?” whereas collectivists appear to ask: “can this city enhance my group’s community?”

These findings are important because they can help us to understand why some people choose to move into certain cities and others choose to leave. However, a key limitation of our work is that it lacked ecological validity because it involved nonresidents evaluating novel, historical, virtual, and unpopulated cities. In our future research, we intend to measure residents’ evaluations of more familiar, modern, real-world, populated cities.

For further information, please see the following journal article:

Rubin, M., & Morrison, T. (2014). Individual Differences in Individualism and Collectivism Predict Ratings of Virtual Cities’ Liveability and Environmental Quality The Journal of General Psychology, 141 (4), 348-372 DOI: 10.1080/00221309.2014.938721

A self-archived version of this journal article is available here.

“I am Working-Class”: Self-Identification as a Measure of Social Class in Educational Research

noreply@blogger.com (Mark Rubin) — Tue, 25 Mar 2014 04:02:00 +0000

Governments around the world are trying to open up higher education to working-class people. For example, in January this year, the White House released a report titled: "Increasing college opportunity for low-income students: Promising models and a call to action."

In the context of this general push towards widening participation in higher education, my colleagues and I have been developing a research project that aims to investigate social class differences in social integration among students atuniversity. After all, we need to bring working-class people into our universities socially and psychologically as well as physically. Our Australian research team includes myself, Dr Nida Denson from the University of Western Sydney, Prof Sue Kilpatrick from the University of Tasmania, Ms Kelly Matthews from the University of Queensland, Dr Tom Stehlik from the University of South Australia, and Dr David Zyngier from Monash University.

As we developed our research project, we quickly realised that the measurement of social class is an extremely contentious issue, with different researchers often preferring different measures. In particular, we noticed that there was a clear divergence between social psychologists and educational researchers in the types of social class measures that they used. Following the recommendations of a 2006 American Psychological Association report on measuring social class, modern-day social psychologists use subjective, self-identification measures of social class alongside more objective measures of income, occupation, and education (for a good example, see Michael Kraus’ work). In contrast, educational researchers have tended to restrict themselves to objective measures and to ignore the more subjective aspects of social class (for a recent review, see Rubin, 2012; for a notable exception, see Ostrove & Long, 2007).

We have discussed this interdisciplinary discrepancy in a recent review article published online this month in Educational Researcher. In our article, we call for educational researchers to follow the lead of social psychologists and complement (not replace) their objective measures of social class with measures of subjective social class. We believe that subjective measures are not only valid and reliable but also more direct and sensitive in their assessment of social class compared with objective measures. Most importantly, subjective measures tap the social identity aspect of social class, and they give a voice to students’ own opinions about their social class.

For further information, please see the following article:

Rubin, M., Denson, N., Kilpatrick, S., Matthews, K., Stehlik, T., & Zyngier, D. (2014). "I am working-class": Subjective self-definition as a missing measure of social class and socioeconomic status in higher education research. Educational Researcher DOI: 10.3102/0013189X14528373

A self-archived version of this journal article is available here.

Spock's Not One of Us! Exploring the In-Group Overexclusion Effect

noreply@blogger.com (Mark Rubin) — Sun, 09 Feb 2014 01:39:00 +0000

Liberal or Conservative?

We all belong to many different social groups. For example, we belong to groups based on our age, gender, nationality, sexuality, and occupation, to name just a few. Most of the time, it's fairly easy to work out who belongs to which group. But sometimes it's not that clear. For example, if you had to guess, would you say that the man opposite is a liberal or a conservative? Well, social psychologists have found that your answer will sometimes depend on which group you belong to. If you're a liberal, then you'll probably guess that the man is a conservative. And if you're a conservative, then you'll probably guess that he's a liberal. This mysterious phenomenon is called the in-group overexclusion effect.

Human or Vulcan?

Now sit up straight - it's formal definition time: The in-group overexclusion effect is an intergroup effect in which people are more likely to classify ambiguous individuals as members of the out-group (i.e., the group that they don't belong to) than their in-group (i.e., the group that they do belong to). So, to take a slightly less real world example, you’d be more likely to classify the lovely Mr Spock as a Vulcan (i.e., an out-group member) rather than a human (i.e., an in-group member), even though he is half human and half Vulcan. (Apologies if you’re of the Vulcan persuasion by the way. My example only works if you're human!)

Based on social identity theory (Tajfel and Turner, 1979), Leyens and Yzerbyt (1992) proposed a motivational explanation for the in-group overexclusion effect: People are motivated to protect their in-group from intrusion (contamination, pollution!) by negatively-valued out-group members in order to protect the positivity of their associated social identity and self-esteem. If those no-good out-group members get classified as in-group members, then they'll sully your group with their negativity and, since part of your self-esteem is derived from belonging to a positive in-group, you're motivated to err on the side of caution and make sure that you exclude anyone from your group who shows even a hint of being an out-group member.

There's some good evidence in support of the social identity part of this explanation: People who identify strongly with their in-group are more likely than those who identify weakly to show the overexclusion effect (Castano, Yzerbyt, Bourguignon, and Seron, 2002). However, to date, there has been no direct evidence for the motivational part of the explanation - the part that relates to the in-group's positivity and your own self-esteem. Do people excluded ambiguous others from their group in order to protect the their group's positivity and their own self-esteem? My colleague, Dr Stefania Paolini, and I aimed to test this motivational part. We made two predictions:

Prediction 1: If the overexclusion effect is caused by the need to protect the in-group’s positivity, then it should only occur when the in-group is positive and the out-group is negative (and not vice versa) because it is only in this situation that group members would be motivated to exclude nasty negative out-group members from their nice positive in-group.

Prediction 2: If the overexclusion effect is caused by the need for self-esteem, then people who have low self-esteem should be most likely to display the effect because they have the greatest need for self-esteem.

To test these predictions, we asked 122 undergraduate students to complete Rosenberg’s (1965) Self-Esteem Scale. This scale includes 10 statements such as "I feel that I have a number of good qualities." Respondents are asked to indicate how much they agree or disagree with each statement.

Next, participants completed a memory recall task in which they were presented with a series of diagrams on a computer screen like the one below. Each diagram showed two groups of people, with each person represented by a code number from 1 to 20. One group was located inside a picture of a bucket of soapy water. The other group was located inside a picture of a dustbin full of rubbish. We asked participants to consider the people in the bucket as "clean" and people in the dustbin as "dirty." So, obviously, the clean group was a positive group and the dirty group was a negative group.

Participants were given an identity number (3 or 14) which they were told identified them, and this code number appeared in one of the two groups. So, one of the groups represented an in-group, and the other represented an out-group. In the diagram above, the dirty group is the in-group because it contains the numbers 3 and 14 (the participants' identity numbers), and the clean group is the out-group. But in other diagrams that we presented, the clean bucket contained the in-group members and the dirty dustbin contained the out-group members.

For each diagram that they viewed, participants were given 5 seconds to memorize which people belonged to which group. The diagram was then removed, a person’s code number was presented, and participants were asked to recall which group the person had belonged to, like this: “Person Number 7. Clean or Dirty?” Our predictions related to those instances in which participants made errors in the memory recall task and assigned a person to the wrong group.

Consistent with previous research, participants erred on the side of caution and misassigned more in-group members to the out-group than they misassigned out-group members to the in-group. In other words, they showed the classic in-group overexclusion effect. However, consistent with Prediction 1, this effect was qualified by whether the group was clean or dirty. The overexclusion effect only occurred when the in-group was clean (i.e., positive) and the out-group was dirty (i.e., negative) – the red line in the diagram below. There was no significant overexclusion effect when the in-group was dirty and the out-group was clean – the green line in the diagram. Hence, consistent with Prediction 1, the overexclusion effect only occurred when the in-group was positive and out-group was negative and not vice versa.

To test Prediction 2, we computed an index that represented the overexclusion effect by subtracting the number of misassignments to the in-group from the number of misassignments to the out-group. Larger positive scores on this index indicated a larger overexclusion effect. Consistent with Prediction 2, participants' scores on the Rosenberg Self-Esteem Scale negatively predicted the overexclusion effect. Hence, the lower people’s self-esteem, and the greater their need for self-esteem, and the more likely they were to show the overexclusion effect by misassigning people to the out-group rather than the in-group.

Our results provide an important piece in the puzzle of the in-group overexclusion effect - the motivational piece. They tell is that it's not enough to simply identify with your group to show the overexclusion effect - you also have to value your group positively. Admittedly, identification and in-group favouritism often go hand in hand, but there are some cases where they diverge. For example, members of stigmatized, low status, and minority groups might identify with their group but also recognise its inherent negativity.

Interestingly, our research also suggests that people with low self-esteem are the most likely to exclude people from their group. Transporting back to the USS Enterpise, perhaps Dr McCoy ("Bones") had low self-esteem because, as Spock observed, he was always the first to exclude Spock from the human race:

Dr McCoy: “Are you out of your Vulcan mind? No human can tolerate the radiation that's in there!”

Mr Spock: “As you are so fond of observing, doctor, I am not human!”

Star Trek II: The Wrath of Khan (1982)

For further information, please see the following journal article:

Rubin, M., & Paolini, S. (2014). Out-group flies in the in-group’s ointment: Evidence of the motivational underpinnings of the in-group overexclusion effect. Social Psychology DOI: 10.1027/1864-9335/a000171

For a self-archived version of this article, please see here.

In-Group Favouritism can be used to Get Even as well as to Get Ahead

noreply@blogger.com (Mark Rubin) — Sat, 11 Jan 2014 09:17:00 +0000

Social identity theory is a major mainstream theory of intergroup relations (Tajfel and Turner, 1979). At its heart lies the assumption that social groups fight and compete with one another in order to attain positive distinctiveness from one another. In other words, group members are motivated to favor their own group (the in-group) and derogate other groups (out-groups) along specific intergroup comparison dimensions in order to increase or maintain their group's relative social status. High in-group status and positive in-group distinctiveness enable in-group members to achieve or maintain a positive social identity and concomitant positive self-esteem (Rubin and Hewstone, 1998, 2004). So, for example, sports fans support their team and derogate the other team in the hope that their team will beat the other group so that they can then bask in the reflected glory of their team's success.

Oxford United Fans Celebrating at a 2011 Game

There is a lot of evidence in support of the social identity explanation of in-group favoritism. However, my colleagues (Constantina Badea and Jolanda Jetten) and I have recently questioned whether beating the out-group is the only motive for in-group favoritism. We assumed that, in the specific case of low status groups, in-group favoritism may be used to make the in-group equal to the out-group, and not necessarily better than the out-group. In this case, in-group favoritism is used to achieve intergroup fairness rather than positive in-group distinctiveness. To give a real world example, gay men and women may favor their low status group not because they want to outdo straight men and women but because they seek equality with straight people in having the right to marry their partners. In this case, in-group favoritism is enacted with the aim of achieving intergroup equality rather than high in-group status or positive distinctiveness.

An Equal Love Rally in Melbourne, Australia

To clarify our assumptions, we distinguished between two types of in-group favoritism that might be used by low status groups: competitive favoritism and compensatory favoritism.

Low status groups can use competitive favoritism to compete with high status out-groups in order to achieve positive in-group distinctiveness.
Low status groups can use compensatory favoritism to compensate for their low status and achieve intergroup fairness.

To test these predictions, we asked 139 psychology undergraduates to take part in a social group resource distribution task. Participants were given an identification number ranging from 1 to 50 and then randomly assigned to two arbitrary groups called the “Red Group” and the “Green Group.” They then allocated points to people from their group and the other group. They were told to think of the points as "points in a game," where the more points a person or group gets, the better. Crucially, the members of one group always started this points allocation task with two more points than the members of the other group. So, in the context of our admittedly artificial laboratory situation, we had an intergroup status hierarchy, with a high status group starting off with more points than a low status group.

Participants used a series of point allocation tables like the one below to award points to group members. In each table, they had to circle one of the four columns listed under the heading “My Allocation to Each Person.” Some of the choices in these columns compensated the low status group member for their initial two-point disadvantage. For example, if a member of the Red Group circled the 3/5 choice in the table, then their fellow low status Red Group member would end up with the same number of points (6 starting points + 5 allocated points = 11 points in total) as the high status Green Group member (8 starting points + 3 allocated points = 11 points in total). Other choices in the tables allowed low status group members to get more points than the high status group member. For example, the 2/6 choice meant that the Red group member ended up with more points (6 + 6 = 12) than the Green group member (8 + 2 = 10).

Using this novel approach, we found that participants in the low status group were significantly more likely than participants in the high status group to choose the competitive in-group favoritism option (e.g., the 2/6 choice in the above table). So, we replicated the classic, competitive favoritism effect that is predicted by social identity theory.

However, we also found that participants in the low status group selected the compensatory favoritism option (e.g., the 3/5 choice in the table) significantly more than chance. Here, awarding more points to the in-group than to the out-group had the effect of compensating the in-group for its initial points disadvantage and leading to intergroup fairness. Hence, the compensatory favoritism choice enabled the low status in-group to do as well as the high status out-group in terms of its points allocations but not necessarily better than the out-group.

Social identity theory has touted social competition as the key driver of social change (Tajfel and Turner, 1979). Our research suggests that, in the context of unequal intergroup status relations, social compensation can also provided a pathway to social change. Specifically, low status groups can use in-group favouritism to change the intergroup hierarchy by getting even with the high status group, rather than by surpassing it in status.

For further information, please see the following journal article:

Rubin, M., Badea, C., and Jetten, J. (2014). Low status groups show in-group favoritism to compensate for their low status and to compete for higher status. Group Processes and Intergroup Relations DOI: 10.1177/1368430213514122

For a self-archived version of this article, please see here.

Boys Don’t Cry, But They Can Be Sensitive! Behavioural Descriptions of Counterstereotypical People Cause Greater Prejudice than Personality Descriptions

noreply@blogger.com (Mark Rubin) — Sat, 03 Aug 2013 07:01:00 +0000

Stereotypes are pretty useful things! We use them to help us to understand and respond to people from a large and diverse array of social groups. But how do people feel about individuals who buck the trend and contradict stereotypes? For example, how do people feel about a man who is crying or a woman who is smoking a cigar!

Most evidence shows that people react quite negatively towards counterstereotypical individuals. The typical explanation for this negative bias refers to people’s need to protect and maintain their stereotypes: People are biased against counterstereotypical individuals because they disconfirm stereotypes and threaten people’s need to maintain stable and coherent stereotype systems.

However, recent social psychological research has provided some hope for counterstereotypical people. This new evidence shows that, although counterstereotypical individuals are disliked when they are described using behaviours, they are actually liked when they are described using personality traits. So, for example, although people may dislike “a man who is crying”, they like “a sensitive man”. In both cases, the man is counterstereotypical because he contradicts a gender stereotype. However, in the former case he is described using a behavior (“crying”) and in the latter case he is described using a personality trait (“sensitive”). Notably, this linguistic description effect occurs even when the particular valence of the words that are used (positive/negative) is taken into account.

So, why does this linguistic description effect occur? Well, it’s possible that counterstereotypical individuals are evaluated relatively negatively when they are described using behaviours because this linguistic description promotes a deeper, more systematic processing of the person that highlights their stereotype disconfirmation and, as we know, people don’t like individuals who step out of line with their stereotypes! In contrast, counterstereotypical individuals may be evaluated relatively positively when they are described using personality traits because this linguistic description promotes a more superficial type of processing that highlights individuals’ uniqueness, and people tend to value uniqueness.

How is all this relevant to you? Well, if you stop for a minute and consider your own social categories (i.e., your gender, religion, occupation, age group, political orientation, etc), then I’m sure you’ll find at least one in which your own characteristics contradict your group’s stereotype. Now don’t worry – it's often good to be the black sheep! Throughout the ages, counterstereotypical people have been the agents of beneficial social change. For example, independent women (who were counterstereotypical for their time) spearheaded the Feminist movement. In addition, the diversity embodied by counterstereotypical people brings with it a wealth of positive outcomes in work and organisational contexts. So, you should feel proud if you’re different from the rest of your group. The trick is to get other people to appreciate you for your uniqueness, rather than to denigrate you for your deviance. And describing yourself in terms of personality traits rather than behaviours may provide one way to do this.

For further information, please see the following journal article:

Rubin, M., Paolini, S., & Crisp, R. J. (2013). Linguistic description moderates the evaluations of counterstereotypical people. Social Psychology, 44 (4), 289-298 DOI: 10.1027/1864-9335/a000114

For a self-archived version of this article, please see here.

This research was supported by the Australian Research Council's Discovery Projects funding scheme (Project DP0556908). However, the views expressed above are not necessarily those of the Australian Research Council.

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“It Wasn’t My Idea to Come Here”: Young Women Lack Ownership of the Idea to Immigrate

noreply@blogger.com (Mark Rubin) — Mon, 04 Mar 2013 02:17:00 +0000

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Together with getting married and buying a house, the decision to immigrate is one of the most important decisions that a person can make. So, it’s important that immigrants feel that they have satisfactory input into the process of deciding whether or not to migrate. In some recent research, I looked at a very early stage of this decision-making process: ownership of the idea to immigrate.

I analysed survey data from 1,702 married immigrants to Australia. Each immigrant was asked “whose idea was it to emigrate to Australia?” Responses were coded as indicating either sole ownership (“It was my idea”), joint ownership with spouse or partner (“We thought of the idea together”), or no ownership (“It was my husband or wife’s idea”).

Surprisingly, I found gender, age, and cross-cultural differences on this very simple, early-stage measure of decision-making. Women were significantly less likely than men to claim ownership of the idea to immigrate, and this lack of ownership went on to predict women’s lack of satisfaction following their move to Australia.

In addition, young women and nonWestern women were less likely than older women and Western women to claim ownership of the idea to immigrate. This pattern of results may reflect a lack of power experienced by young and nonWestern women in their marriages.

The present findings do not imply that young or female immigrants were in any way forced or coerced to migrate to Australia. However, they do provide some cause for concern, especially given that ownership of the idea to immigrate appears to predict subsequent satisfaction in the new country.

This research has been officially accepted for publication in the International Journal of Intercultural Relations, which is a top quality journal, ranked in the top third of sociology journals. For further information, please see the following article:

Rubin, M. (2013). “It wasn’t my idea to come here!”: Ownership of the idea to immigrate as a function of gender, age, and culture International Journal of Intercultural Relations DOI: 10.1016/j.ijintrel.2013.02.001

Note: The views expressed above are those of the author and do not necessarily represent those of the Australian Government's Department of Immigration and Citizenship.

“They’re all the Same!”...But for Several Different Reasons

noreply@blogger.com (Mark Rubin) — Tue, 04 Dec 2012 12:00:00 +0000

People often get confused between members of the same social group because “they all look the same!” In a recent review, Constantina Badea and I looked at the various reasons for this group homogeneity effect.

One reason is that group members actually do look the same as one another: Try identifying the culprit in a line-up of the Queen’s Guards!

Another reason is that people, especially Westerners, are motivated to perceive people in their own groups as individuals. This perceived in-group heterogeneity lets you express your individuality and distance yourself from your group's negative aspects.

It also matters what kind of group is being judged. There is a tendency to judge people in small groups, low status groups, and low power groups as being relatively similar to one another.

Finally, it matters what dimensions are being used to judge the groups. Group members tend to be rated as being similar to one another when they are judged on stereotypical dimensions as opposed to nonstereotypical or counterstereotypical dimensions (e.g., men judged on the dimension “adventurous” rather than "sensitive").

So, whether or not “they all look the same” depends on their actual variability, whether you are one of them, how numerous and powerful they are, and what kind of dimensions you’re judging them on.

For further information, please see the following journal article: Rubin, M., & Badea, C. (2012). They're All the Same!. . . but for Several Different Reasons: A Review of the Multicausal Nature of Perceived Group Variability Current Directions in Psychological Science, 21 (6), 367-372 DOI: 10.1177/0963721412457363

A self-archived version of this paper is available here.

Please click here for a collection of research papers investigating perceived group variability.

The Disproportionate Influence of Negative Intergroup Encounters on Prejudice

noreply@blogger.com (Mark Rubin) — Mon, 03 Dec 2012 10:30:00 +0000

Back in February, I wrote about some research in which my colleagues and I showed that negative experiences with people from other groups are better at drawing attention to the people's group memberships than positive experiences (Paolini, Harwood, & Rubin, 2010). In other words, if you have a negative encounter with someone from another group, then you are more likely to think about their group memberships (e.g., their gender, race, nationality, etc.) than if you have a positive encounter with them. This increased awareness of a person's group memberships following a negative encounter with them is potentially problematic for the goal of reducing intergroup conflict in society because it means that people are naturally biased towards attributing bad things to out-group members' groups and generalizing their negative experiences with one out-group member to all of the other out-group members. Our preliminary evidence provided some support for this assumption, showing that people were more likely to mention a woman's ethnicity when she behaved negatively rather than positively. However, we did not go as far as testing whether this out-group salience effect led to greater prejudice against the out-group.

Now, Fiona Barlow and colleagues have found this missing piece of the puzzle in a series of research studies published this week (Barlow, Paolini, Pedersen, Hornsey, Radke, Harwood, Rubin, & Sibley, 2012). We looked at prejudice against Black Australians, Muslim Australians, and asylum seekers and found that negative experiences with these people were a stronger and more consistent predictor of negative attitudes towards them than positive experiences were of positive attitudes. For example, negative experiences, but not positive experiences, with Black Americans predicted suspicion about Barack Obama’s birthplace, which represents a subtle measure of racism.

These results suggest that negative experiences with out-group members are not only more likely than positive experiences to make people think about out-group members' group memberships, but also to influences people's attitudes towards the out-groups. Taken together, these two recent papers suggest that negative experiences with out-group members have a greater potential than positive experiences to influence people's thoughts and feelings about out-groups. This work implies that we should redouble our efforts to encourage positive experiences between members of different groups because this positive intergroup contact is naturally disadvantaged against improving intergroup relations when compared with the more powerful influence of negative intergroup contact.

For further information, please see the following journal article: Barlow, F., Paolini, S., Pedersen, A., Hornsey, M., Radke, H., Harwood, J., Rubin, M., & Sibley, C. (2012). The Contact Caveat: Negative Contact Predicts Increased Prejudice More Than Positive Contact Predicts Reduced Prejudice Personality and Social Psychology Bulletin, 38 (12), 1629-1643 DOI: 10.1177/0146167212457953

Let’s Show Them the Ropes: Why University Friends May Help Australia’s New Intake of Working-Class Students to Succeed

noreply@blogger.com (Mark Rubin) — Wed, 16 May 2012 18:28:00 +0000

Like the USA and UK, Australia is currently attempting to increase the number of working-class students at its universities. I have recently published a short piece in Higher Education Research and Development in which I argue that Australia’s social and education researchers need to focus their efforts on identifying the best approaches towards facilitating these students’ success at uni (Rubin, 2012). In particular, we need to consider how to increase the social integration of working-class students at uni.

In a recent review of American research, I’ve shown that working-class students are significantly less integrated at uni than middle-class students (for more, see here). This is a problem because there’s clear evidence that social integration positively predicts academic performance and persistence.

Social integration is likely to be particularly important for working-class students because many of them are the first in their families to attend university and, consequently, they are less likely to get helpful advice from their parents about how to navigate university life. For example, when a working-class student falls ill and asks their parents what to do about their late coursework assignment, their parents may tell them to do the best they can and submit on time. But middle-class parents, who've been at uni, are likely to suggest applying for an extended due date. It’s for this sort of reason that I think it's vital for working-class students to make friends at uni. Uni friends can provide valuable informational and social support that helps fill the experience gap of working-class students. In short, uni friends can help show working-class students the ropes.

In my article, I argue that we need more research in this area in order to figure out the main causes of working-class students' social exclusion. The results of this research will inform the best approaches towards increasing working-class students’ social integration and give them a better chance of succeeding at uni.

For further information, please see the following journal article:

Rubin, M. (2012). Working-class students need more friends at university: a cautionary note for Australia's higher education equity initiative Higher Education Research & Development, 31 (3), 431-433 DOI: 10.1080/07294360.2012.689246 Free access available at: http://www.tandfonline.com/eprint/vfyypRnNZK27hiH5WDQ8/full