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Reflecting on the quantitative turn in linguistics

Linguistics, English linguistics in particular, has witnessed a remarkable quantitative turn since the 1990s and the early 2000s. It was a turn both in scale and in quality, a turn concerning the degree (including the degree of sophistication) to which quantitative empirical studies, statistical techniques, and statistical modelling have come to be used and determine linguistic research. Which role have corpus linguistics and probabilistic linguistics, including usage-based approaches, played in this development? Has this turn been to the detriment of qualitative methods, or even of linguistic theorizing in general? Has linguistics reached the point of a “quantitative crisis”, or is it still a discipline characterized by a healthy equilibrium, if not mutual reinforcement, of quantitative and qualitative approaches? What are, or should be, major repercussions of the strong quantitative turn for the publication system of (English) linguistics? These are the major overarching questions underlying the reflections offered in this opinion paper.

1 Introduction

Linguistics, English linguistics in particular, [1] has witnessed a remarkable quantitative turn over the last two decades. Has this turn been to the detriment of qualitative methods, or even of linguistic theorizing in general? Has linguistics reached the point of a “quantitative crisis”, or is it still a discipline characterized by a healthy equilibrium, if not mutual reinforcement, of quantitative and qualitative approaches? What are, or should be, major repercussions of the strong quantitative turn for the publication system of (English) linguistics? These are the major overarching questions underlying the reflections offered here, in what may perhaps most appropriately be characterized as an opinion paper. One general conclusion will be that, apart from the need for an increased level of sophistication and problem-awareness in choosing, applying and interpreting statistical methods in linguistic research, the natural next step for a strongly quantitatively oriented linguistics needs to be the increasing adoption of multi-method approaches. Overall, this paper is fueled by the optimism that linguistics stands a real chance to retain all its traditional strengths and, at the same time, to develop into an even more respected showcase of the Digital Humanities, capable of bridging the disciplinary boundaries especially to the behavioral and neurosciences. The all-important precondition for this is that linguistics continues to force itself onwards on the thorny path of standing up to the rigorous standards of quantification, statistical analysis and modelling of these highly developed quantitative sciences.

The quantitative turn addressed in this paper is the one we have been witnessing on a broad scale since the 1990s and the early 2000s. It was a turn both in scale and in quality, a turn concerning the degree (including the degree of sophistication) to which quantitative empirical studies, statistical techniques, and statistical modelling have come to be used and determine linguistic research. Even in fields of linguistics like sociolinguistics, interactional linguistics, multimodal studies, or even in semantics and pragmatics, all of which typically, or even dominantly, work with qualitative methods, [2] some degree of quantification is increasingly expected (especially for journal publications). Overall then, the inclusion of quantitative methods has changed from a “nice to have” to a “better to have” status.

2 The quantitative turn

In Brian Joseph’s final editorial as the editor of Language , one of the long-time flagship journals of the discipline, he comments on recent developments in the field, among them the following: [3]

Linguistics has always had a numerical and mathematical side … but the use of quantitative methods, and, relatedly, formalizations and modeling, seems to be ever on the increase; rare is the paper that does not report on some statistical analysis of relevant data or offer some model of the problem at hand. ( Joseph 2008 : 687)

Joseph also identifies what I consider as the two major aspects, or even drivers, of this development:

[…] research papers are more experimentally based than ever before (‘experimental’ in the sense of pertaining to any sort of controlled investigation). Also, they are more corpus-based, with many studies using as primary data (not just as corroborating data) examples that have been gleaned from available corpora, including the Internet ( 2008 : 687)

Whereas the experimental turn is increasingly, however much more slowly, making its inroads into linguistics (but see Sections 3 and 4 ), the rise of corpus linguistics has been vastly influential and has left a lasting imprint on the field. Thus Gries (2015 : 93, 113), one of the first and still major protagonists of a statistics-heavy corpus linguistics, is right when stating that “corpus linguistics has been among the fastest-growing methodological disciplines in linguistics”, “has become mainstream […]”, and bears witness to the trend” that linguistics in general has become much more quantitative/statistical in nature […]: For example, 10 or 15 years ago it would have been quite difficult to find papers with multifactorial statistical techniques in corpus-linguistics papers – now, monofactorial statistical tests at least are much more frequent, and multifactorial statistical methods are on the rise”.

To account for the differing amounts of articles per year, all graphs show the percentage of each article type by year, proportional to the total articles in that year. Figure 1 shows a linear regression line fitted to the four categories listed above plotted against the percentage of the total articles by year. Note that there is a lot of variance by year, which cannot be seen in such a strictly linear model. Thus, Figure 2 shows the same categorization as a bar graph, again proportional to the total number of articles by year. Both figures are telling the same story, though: the proportion of qualitative articles per volume is strongly decreasing (from some 75% to less than 25%), the proportions of articles making use of simple and, even more pronounced, advanced statistics are steadily increasing (ending up between 25 and 30% each), while the proportion of “freq”-articles remains fairly stable (at a level slightly below the 25% mark).

Linear regression lines for all four methodological categories as a percentage of the total articles by year in English Language and Linguistics from 1997 to 2019.

Bar graph for all four methodological categories as a percentage of the total articles by year in English Language and Linguistics from 1997 to 2019.

In Figure 3 , we collapse simple and advanced models into one category (“statistical”), and plot it against “frequency” and “qualitative” to see statistical measures compared to articles reporting frequency counts and qualitative works.

Linear regression lines for qualitative methodologies compared to frequency-based and statistical methodologies as a percentage of the total articles by year in English Language and Linguistics from 1997 to 2019.

Finally, combining frequency together with the other quantitative methodologies provides an even broader overview. Figure 4 shows a linear regression line for qualitative articles compared to quantitative articles in general (i.e., including frequency counts) as a percentage of the total articles for that year. This figure says it all: the quantitative turn in linguistics as reflected by the longitudinal study of one of the leading journals in the field could not be more pronounced. [5]

Linear regression lines for qualitative methodologies compared to quantitative methodologies as a percentage of the total articles by year in English Language and Linguistics from 1997 to 2019.

The rise, power, and pervasiveness of corpus linguistics can be read off easily from the following figures as well. At the time of writing this article (December 2019), the number of currently publicly available corpora just for English has passed the threshold of 100 (excluding subcorpora of the International Corpus of English (ICE) or the International Corpus of Learner English (ICLE), comprising some 20 billion words. To a substantial part (some 12 billion words), this includes web-based corpora (e.g., NOW – News on the Web, GloWbE – Corpus of Global Web-based English, the Hansard Corpus, the Wikipedia Corpus), which are growing on a daily basis. Add to this, for example, about 2,000 billion words in Google Books or data that can constantly be compiled from the internet, notably from social networks (e.g., Twitter). Further striking indicators of the corpus-linguistic and quantitative turn in linguistics are the following (see Appendix ): 16 introductions to Corpus Linguistics have been published since the very first one (by McEnery and Wilson) in 1996, five relevant handbooks since 2009, five book series since 1998, six journals since 2002, and nine introductions to statistics for linguist(ic)s since 1998, four of them since 2015 (see Appendix ). Moreover, methodological textbooks have standardly come to include sections on corpus use (e.g., Krug and Schlüter 2013 ).

This corpus linguistic turn, spearheaded by English linguistics, is largely considered a development for the better. Three powerful reasons are given, again, by Gries (2013 : 361–362):

First, it situates the field of linguistics more firmly in the domains of social sciences and cognitive science […]. Other fields in the social sciences and in cognitive science – psychology, sociology, computer science, to name but a few – have long recognized the power of quantitative methods for their respective fields of study, and since linguists deal with phenomena just as multifactorial and interrelated as scholars in these disciplines, it was time we also began to use the tools that have been so useful in neighboring disciplines.

Second, the quantitative study of phenomena affords us with a higher degree of comparability, objectivity, and replicability.

Third, there is increasing evidence that much of the cognitive and/or linguistic system is statistical or probabilistic in nature. […] and if one adopts a probabilistic theoretical perspective, then the choice of probabilistic – i.e., statistical – tools is only natural; […].

In the following, I will first look a bit more closely into exactly this probabilistic theoretical perspective which is so strongly fueling the quantitative turn in linguistics ( Section 3 ) and the related question whether corpora do indeed mirror psychological reality ( Section 4 ). This will be followed by some major caveats, formulated in the form of do s and don’t s, concerning the quantitative turn and the use of heavy quantitative machinery ( Section 5 ) and, in conclusion, by brief answers to the three overarching questions raised at the outset ( Section 6 ).

3 Probabilistic linguistics

Bod (2010 : 634) aptly summarizes the crucial underlying assumption of probabilistic linguistics as follows:

Knowledge of language is sensitive to distributions of previous language experiences. Whenever an expression is processed, it is seen as a piece of evidence that affects the probability distribution of language experiences. New expressions are constructed by probabilistically generalizing over previous expressions.

One of the strongest arguments for using probabilities comes from the study of frequency effects in language. Frequent words and constructions have been shown to be learned faster than infrequent ones (e.g., Ellis 2002 ; Goodman et al. 2008 ), just as frequent combinations of phonemes, morphemes and structures could be shown to be perceived as more grammatical, or well-formed, than infrequent combinations (cf. Coleman and Pierrehumbert 1997 ; Manning 2003 ). Frequency effects have also been shown to pervade gradience in language ( Bybee and Hopper 2001 ). [6]

Now what is important, not only when exploring frequency effects, is that probabilistic linguistics makes a strong cognitive claim, namely that our language faculty is probabilistic, probabilities being “operative in acquisition, perception, production, language change, language variation, language universals, and more” ( Bod 2010 : 634; cf. similarly Bresnan and Ford 2010 : 205).

For language acquisition this cognitive claim is worked out in more detail by Gries and Ellis (2015 : 241):

[l]anguage learners do not consciously tally […] corpus-based statistics. The frequency tuning under consideration here is computed by the learner’s system automatically during language usage. The statistics are implicitly learned and implicitly stored […]; learners do not have conscious access to them. Nevertheless, every moment of language cognition is informed by these data […].

Indeed, since the early 2000s, linguistics has seen “a growing number of experimental studies confirming principled correlations between statistical generalizations over corpus data and subjects’ experimental behaviors at various levels of language description” ( Blumenthal-Dramé 2016 ). And yet there is one fundamental question, and enormous challenge, that the cognitive claim of probabilistic linguistics (and usage-based linguistics, in general) needs to answer: Is their model of language processing and language storage realistic? According to this model, called exemplar store model by Blumenthal-Dramé (2016) , the mind is conceived as a multi-dimensional memory space that is permanently switched on like a multi-channel recording system. Through this system, the entirety of experience (down to the most fine-grained linguistic and non-linguistic facets of the input) is channeled, indexed and stored away in memory, and language users compute statistical generalizations over usage data. Thereby the system is continuously updated (cf. Docherty and Foulkes 2014 : 51). The question is whether the “exemplar store” really models what goes on in people’s minds. Moreover, can this question, which is asked by an increasing number of people, be answered by using off-line linguistic, i.e., corpus, data? Do corpora mirror psychological reality, at all? This set of questions will be addressed, however selective and briefly, in the following section.

4 From corpus to cognition?

An early statement concerning the strong links between cognition and recurrent usage events was formulated by one of the pioneers of cognitive linguistics, Ronald Langacker (1987 : 100): “[a]n event […] becomes more and more deeply entrenched through continued repetition”. Corpora enter the picture as, according to Arppe et al. (2010 : 8–9), Langacker’s “assumption entails that corpora, which contain information about what is likely to be repeated or not in language, should make it possible to identify those items that have a special status in the mind. However, this assumption is mainly that – an assumption, and linguists have made relatively few efforts hitherto to test the cognitive reality of corpora”. Such tests have, however, increasingly been conducted in recent years, [7] and thus two relevant case studies from English linguistics will briefly be presented in this section, one from word-formation, the other from inflectional morphology and morphosyntax. Both book-length studies, this is important to note, adopt a multi-method design, and both studies show that corpora offer no shortcut to cognition.

4.1 Entrenchment

In the following, entrenchment will be understood as characterized by Zeschel in his account of the usage-based hypothesis, which “assumes that there is a connection between the usage frequency of linguistic structures and their degree of cognitive routinisation, or likelihood to be memorised/stored (entrenchment)” (in Arppe et al. 2010 : 10). It is this hypothesis that Blumenthal-Dramé puts to the test in her 2012 monograph, operationalizing entrenchment in gestalt psychological terms and conducting a series of behavioral and neuroimaging experiments on the processing and storage of complex words in English. By adopting a multi-method design, she wants to reach a better understanding of entrenchment and explore the crucial methodological question whether cognitively realistic insights into speakers’ linguistic knowledge can be reached via the indirect method of making quantitative generalizations over offline linguistic data, i.e., corpus data. In a nutshell, the main results and conclusions to be drawn from them can be summarized as follows. First, it is at most a weak version of the corpus-to-cognition principle Blumenthal-Dramé (2012 : 205) finds evidence for (my emphases):

“If a whole range of caveats is heeded, certain corpus-extracted variables may, to some extent , be used as a yardstick for entrenchment in the brain of an average language user.” And even then this “[…] may be rather weakly representative of actual brains. Although this is not in itself objectionable […] wide-scope generalizations of this kind will necessarily miss important generalizations at a higher level of granularity […].”

Moreover, Blumenthal-Dramé (2012 : 205) alerts us to the much-overlooked fact that the assumption of an average language user may distort a far more complex reality. There may well exist systematic intersubjective differences in processing style, some language users employing more holistic, others less holistic cognitive styles, for example. Such differences must not be handled (or rather dismissed) as mere noise when interpreting the statistical models employed in the relevant studies.

4.2 Analyticity versus syntheticity

The second case study putting to test the cognitive reality of corpora ( Kunter 2017 ) [8] is concerned with English inflectional morphology and morphosyntax, more exactly with the alternation between analytic and synthetic forms in comparison and possessive marking. The author’s primary target of investigation is processing complexity, but similar to Blumenthal-Dramé in her study on entrenchment he starts out from the observation that frequency distributions and observations on structural complexity in corpora are often taken as indirect evidence for cognitive processing complexity. Kunter (2017 : 4) states:

Frequently, these corpus-based studies follow an indirect line of argumentation: if a particular grammatical variant is found to co-occur with structures that have relatively high linguistic complexity, this high linguistic complexity is often equated to an increased processing complexity. In a second step, the correlational relation between the grammatical variant and its co-occurring structures is interpreted as a causal relation, thus arguing that the occurrence of a particular grammatical variant does not only co-occur with another structure, but is caused by the higher processing complexity of that structure.

In other words, corpus studies are in essence observational and perfect for identifying correlations between variable features, on the basis of which it is of course possible, and useful, to formulate hypotheses on causal relations. However, these hypotheses, in turn, must be tested independently, namely by way of experimental (psycholinguistic) studies. In short: corpus data reveal no more than indirect evidence for cognitive processes; only experimental data offer the chance to reveal direct evidence. Applied to Kunter’s processing complexity study of analytic versus synthetic coding in English comparison and possessive marking, he therefore finds the following:

Consequently, the main claim of the analytic support hypothesis is supported almost exclusively by indirect evidence, the claim that speakers use the analytic variants as an immediate reaction to increases in the cognitive complexity of the environment, and not merely together with increases in cognitive complexity. In other words, the hypothesis states a causal relation between high processing effort and the choice of analytic forms, but the bulk of supporting evidence comes from observational data that is not ideal for identifying causal links. ( Kunter 2017 : 221)

This is why Kunter, like Blumenthal-Dramé in her study presented in Section 4.1 , conducts production experiments alongside corpus studies, arriving at the following overall conclusion of this dual approach design (my emphases):

Thus, the two production experiments and the two corpus studies speak against a general compensatory mechanism that can account for both the comparative alternation and of the possessive alternation. There is partial evidence in favour of the more support hypothesis in that speakers show a significantly higher tendency to use the analytic comparative with adjectives that are cognitively more complex. However , this effect is one factor alongside other determinants, […]. The empirical findings provide little reason to assume that processing complexity plays a similar role in the possessive alternation. ( Kunter 2017 : 223)

Moreover, Kunter’s findings on the alternation between synthetic and analytic forms also remind us of the often overlooked (or at least underrated) fact that speakers and hearers may well, and often clearly do, have competing motivations as regards language production and processing (recall Zipf’s 1949 monograph on Human behavior and the principle of least effort ) – again something that corpus analyses alone tell us little, if anything, about: “Synthetic comparatives have been found to be relatively easy to process by listeners, but analytic comparatives may be preferred by speakers” ( Kunter 2017 : 226).

What the above two case studies show very clearly is that only experimental data offer the chance to reveal direct evidence, whereas corpus data are purely observational, show us how people use language but reveal no more than indirect evidence for cognitive processes, and may thus at most give rise to the formulation of research hypotheses concerning cognitive processes. A multi-method design exploring both (offline) corpus and (online) experimental data is thus indispensable. This is what any corpus linguist embarking on a research question relating to language cognition should keep in mind. All this is also convincingly argued in Arppe et al.’s (2010) thought-provoking debate on cognitive corpus linguistics, which ends in three simple pieces of advice for corpus-based cognitive linguists. Of these, I will quote only the first and, in my view, most important one: “First, a certain degree of humility could not hurt” ( Arppe et al. 2010 : 21).

5 Major caveats

In general , do everything that is necessary for achieving a maximum of methodological transparency, rigor, statistical validity, robustness, reproducibility, falsifiability and, ultimately, for a maximum of mileage when putting to test existing, or even formulating novel, linguistic hypotheses. Do first formulate intelligent research questions and a solid research- and theory-grounded set of (potentially competing) hypotheses, which can in a next step be statistically tested/falsified.

Do , in a next step, identify the most appropriate (corpus) data sets and (quantitative or qualitative) methods, notably statistical methods, for the purposes of your research question(s).

Don’t take statistical compatibility with a given hypothesis immediately as (sufficient) proof, nor as automatically implying incompatibility with a competing hypothesis or theory.

Do distinguish between confirmatory and exploratory analyses (as done, for example, by Granlund et al. 2019 ). Confirmatory analyses would be ones of the type in the previous two points. But your findings may lead you to test novel hypotheses, to which you should be absolutely open, which may then serve as the basis for confirmatory statistical testing in follow-up studies, but

Don’t take exploratory analyses as a starting point, even less take them as an escape route or, in the worst case, as pseudo-confirmatory analyses of hypotheses formulated post hoc (i.e., when interpreting the results of your statistical analyses).

In general , don’t do everything that is statistically possible just because you can do it (heaping minor (or worse, irrelevant) detail on minor/irrelevant detail), even less as a remedy for an imperfect data set or inconclusive data analyses. More specifically, don’t use a statistical model that is more complex than needed for your data, don’t multiply statistical testing beyond necessity, and don’t engage in statistics-driven research! Statistical machinery must not determine the research question.

Do interpret your quantitative findings against previous linguistic research and theory building. Moreover:

Don’t confuse correlations with causes as this may, for example, lead you to fall in the circularity trap. Take frequency effects: the strong correlation between frequency of use and semantic bleaching often described in grammaticalization studies, for example, does not tell you anything per se about whether it is semantic bleaching that is the cause for a high usage frequency. It may just as well be the other way round or, as a third option, simply no more than an association, with no unidirectionality in either direction.

Don’t see language or some variety of a given language exclusively through the lens of (available relevant) corpora, as this may artificially narrow the object of study. For example, features salient for a particular variety may well be low in frequency. Theoretically interesting structural patterns or structural patterns currently undergoing change as observed by native speaker linguists, for example, may not figure at all (yet) in especially smaller-sized corpora like the sub-corpora of the International Corpus of English. Likewise the study of subtle semantic phenomena and pragmatic phenomena involving knowledge about the entire sociological context (incl. language attitudes) and situational communicative (incl. multimodal) context, the interlocutors’ intentions, etc., requires a broader take on data than many corpora can currently offer. Sampson (2005 : 26) is therefore right when stating, “Intersubjectively observable evidence is evidence, wherever it is found. Corpus linguists compile and work with standard corpora because they are especially convenient data sources, but there is no reason to suggest that evidence has to occur in a recognized corpus to count as evidence”.

Don’t commit the “from-corpus-to-cognition fallacy”: rather conduct, as is appropriate for the research question, experimental studies alongside corpus studies (i.e., use a dual-approach or multi-method design). [9]

Do make your data and statistical analyses accessible in the true open science spirit. [10]

Finally, however powerful and promising the corpus revolution and quantitative turn may be (or be felt to be): don’t forget the rich inventory of theories and (largely qualitative) methods which (schools of) linguists have developed and refined over many decades for the analysis of natural language and communication.

6 Conclusion: a quantitative crisis in (English) linguistics?

Overall, in my view at least, the quantitative turn in linguistics has been a largely positive development. It has many strengths and great potential always provided that corpus analyses and statistical techniques are selected and applied in a cautious, reflected manner, thus heeding constraints, challenges and dangers such as the limits of what corpora can tell us about cognition and, the risks of simplistic or naive statistical analyses (e.g., cherry-picking or confusing correlations with causes). The crucial point and task for linguists committed to the quantitative turn is “to boldly go where the others already are”, as it were, without however repeating the mistakes the others have made along the way. Painful as it may be to realize, in the concert of the quantitative sciences, linguistics still is a (somewhat naive) newcomer, but if it wants to be taken seriously it needs to stand up to the rigorous standards of these sciences. This is still a quite hard and long way to go. What is needed besides basic and advanced statistical training as part of degree and doctoral training programs, and besides statistics-savvy linguists, is that the members of every linguistics department should also have the possibility of consulting with professional (ideally linguistics-savvy) statisticians! If all these conditions are fulfilled, linguistics will increasingly (co)operate on eye level with the behavioral and neuro-sciences. Another piece of good news that needs to be stressed by way of conclusion is that the quantitative turn in linguistics has not been to the detriment of qualitative approaches. Rather I see a productive relationship and interplay characterized by mutual respect, reinforcement, and benefit.

What then about the focal problems addressed in the ISLE 5 workshop on “The ‘quantitative crisis’, cumulative science, and English linguistics”: Do quantitative approaches in linguistics suffer from methodological problems such as the non-replicability of studies, high rates of false-positive findings in published research, a lack of transparency as regards methodology and decisions in their analyses, or a negligence of replication studies as “unoriginal” (and nonprestigious)? There is no denying that linguistics in times of the quantitative turn may well suffer from these problems, too. Yet at the same time there is reason for optimism: the quantitative turn in linguistics is still fairly recent or, so I believe at least, recent enough for the research community to develop an awareness of these problems at a fairly early stage and to avoid the mistakes the established quantitative sciences have made along their way. This, in turn, together with the recent developments in formulating standards of linguistics publishing in line with open science policies and best practices (notably with regard to the accessibility of data and analyses, the transparency of data coding, methods and statistical tests, the reproducibility of studies and their findings, and, perhaps least pronounced at the current stage, the courage – both on the side of the authors and the journal editors – to publish “negative” results) offers the realistic opportunity for solving these problems, or avoiding them altogether, within the next few years.

This does not mean, on a final note, that there is no room for improvement with regard to linguistics publishing. A cursory check of some thirty high-ranking linguistics journals regularly publishing state-of-the-art quantitative studies reveals that only a fraction have made a statistical consultant or consultant editor part of the editorial team, and only just about half of them draw at least on the services of members of the editorial or advisory board working quantitatively. Also only a fraction of journals currently have specific guidelines, or at least sections thereof, dedicated to quality standards for the statistics in manuscript submissions. Therefore, even if journal editors regularly involve independent peer reviewers with statistical expertise as one important element for judging the quality of manuscript submissions, in terms of quality assurance and meeting the highest scientific standards of the quantitative sciences most linguistics journals (and, correspondingly, book series) can, and before long need to, do better. At the same time, there are linguistics journals that have strongly changed their policies in accordance with best practice open science policies (see also Footnote 9). Perhaps most ambitious among these, but necessary in light of everything which has been said here on the strong quantitative turn in linguistics (mind the caveats in Section 5 ), is pre-registration. Thus the journal Language Learning has implemented Registered Reports as a new article type as recently as in 2018 ( Marsden et al. 2018 ). This means that, instead of submitting their finished (draft) papers, authors register with journals (or book series, for that matter) the planned design, hypotheses, methods, data preparation and analyses even before actually conducting the relevant studies intended to be published with these journals. The journal editors, in turn, pass on the pre-registration information to peer reviewers as the basis for their (first) reports and subsequently make conditional acceptance offers. This new article type, or rather publication policy, offers a range of advantages, among them the following: It increases transparency, makes reviewing more effective in that the peer reviewers can identify methodological problems at a stage early enough to be remedied by the authors before embarking on the actual study, and not least it helps avoid publication bias towards the “new” and “significant”.

The dataset for this article can be found on Zenodo: https://doi.org/10.5281/zenodo.5102780 .

Acknowledgments

The author wishes to acknowledge and thank the editors, an excellent anonymous reviewer, Natalia Levshina, and the members of his research team for their extremely valuable comments and suggestions.

Striking indicators of the corpus-linguistic and quantitative turn in linguistics in the 2000s: Relevant introductions, handbooks, book series, and journals in chronological order [11]

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Since its inception in the 1960s, the field of second language acquisition (SLA) has sought to document and explore how children and adults acquire a nonnative language. Researchers have investigated the linguistic, cognitive, social, contextual, psychological, and neurobiological characteristics of second language (L2) learning, processing, and use. Typical research questions include: What are the characteristics of learner interlanguage? How do individual differences, such as working memory capacity, impact the learning of an L2? How does the social context (such as stay-at-home vs. study abroad) influence the fluency, accuracy, and complexity of learner language? How do different types of motivation impact the learning process? How is the L2 processed in the learner’s mind and how is this affected by age of acquisition? To investigate these and many other questions, SLA researchers have at their disposal a large array of research designs. In this chapter, we will discuss various research designs, including quantitative, qualitative, and mixed methods traditions. We will also address current works in progress and examine recent topics of concern related to the conducting of research on L2 learning. Finally, we will conclude with future directions for SLA research.

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Abbuhl, R., Mackey, A. (2017). Second Language Acquisition Research Methods. In: King, K., Lai, YJ., May, S. (eds) Research Methods in Language and Education. Encyclopedia of Language and Education. Springer, Cham. https://doi.org/10.1007/978-3-319-02249-9_13

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Educational resources and simple solutions for your research journey

What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Which Programming Language Should You Learn To Get A Quant Developer Job?

I received an email last week from a reader asking about the best enterprise language, such as C++, Java or C#, to learn in order to get a development job in finance. I replied saying that it was not a straightforward question to answer, for a number of reasons!

Let's have a look at the different languages in turn and see the advantages and disadvantages of each:

If you are prepared to learn C++, object-oriented programming and design patterns to a high standard, then you will likely be in high demand in the financial industry - either as a quant developer or a traditional developer.

Much of the older financial infrastructure is based on C++ code and somebody needs to maintain and extend it. This might be quantitative libraries running derivatives pricing models or simply trading infrastructure to process feeds and store the data.

A senior contractor with 5-7 years experience of strong C++ development, contracting in London, should be able to command £500-£700 GBP a day. In the current climate the market for developers is very buoyant. This may or may not last, so don't expect those figures to be around forever.

One additional benefit of being an expert C++ programmer is that it will put you in demand from the high frequency trading funds. However, you will need to be a serious expert in both optimisation of the language, compiler limitations and operating system internals in order to get into that particular aspect of finance. If you're prepared to study hard for a considerable amount of time, or have the necessary background from an industry such as telecoms, then the salaries can be extremely lucrative.

I've lumped together the garbage-collection VM languages such as C# and Java as they hold similar places, often in investment banks. Both of these languages have been around for a while, just not quite as long as C++. Financial institutions, particularly investment banks, use a lot of these languages for their entire trading infrastructure, including data feeds, front-end trading interfaces and sometimes derivatives pricing.

I'm not one to begin a flame war on which programming language is the best, but I will say this: Both C# and Java are heavily utilised in the sell-side (banks) of finance, but you are less likely to be working on more quantitative work and more likely on infrastructure. This may or may not appeal to you. However, if you are good at what you do and make yourself known to be good within your peer group, you will likely not have trouble obtaining good compensation and an interesting development career.

Python, MATLAB and R

I've collected the "scripting" languages together, less so because of their commonalities are languages and more so due to their usage within finance. All three are mainly used for prototyping quant models, especially in hedge funds and quant trading groups within banks.

Quant traders/researchers write their prototype code in these languages. These prototypes are then coded up in a (perceived) faster language such as C++, by a quant developer. This was part of my duties when I was working as a "quant dev". If you are interested in a more relaxed environment than a bank trading floor then hedge funds are a good answer. Any Python/MATLAB/R scripting skills will be extremely valuable.

As an anecdotal data point, I was recently asked by a friend if I knew any Python developers who might be interested in a quant fund developer position as they're using Python exclusively for their entire trading system within a new quant fund.

If you are brand new to programming then I would suggest becoming proficient at C++ and Python. You won't have trouble getting hired with those languages if you can pass the tricky interview questions. To begin learning either have a look at these QuantStart reading lists:

• Top 5 Essential Beginner C++ Books for Financial Engineers
• C++ Reading List
• Python Reading List

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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• Published: 19 June 2024

Language is primarily a tool for communication rather than thought

• Evelina Fedorenko   ORCID: orcid.org/0000-0003-3823-514X 1 , 2 ,
• Steven T. Piantadosi 3 &
• Edward A. F. Gibson 1  

Nature volume  630 ,  pages 575–586 ( 2024 ) Cite this article

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• Human behaviour

Language is a defining characteristic of our species, but the function, or functions, that it serves has been debated for centuries. Here we bring recent evidence from neuroscience and allied disciplines to argue that in modern humans, language is a tool for communication, contrary to a prominent view that we use language for thinking. We begin by introducing the brain network that supports linguistic ability in humans. We then review evidence for a double dissociation between language and thought, and discuss several properties of language that suggest that it is optimized for communication. We conclude that although the emergence of language has unquestionably transformed human culture, language does not appear to be a prerequisite for complex thought, including symbolic thought. Instead, language is a powerful tool for the transmission of cultural knowledge; it plausibly co-evolved with our thinking and reasoning capacities, and only reflects, rather than gives rise to, the signature sophistication of human cognition.

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The authors thank A. Ivanova, R. Jackendoff, N. Kanwisher, K. Mahowald, R. Seyfarth, C. Shain and N. Zaslavsky for helpful comments on earlier drafts of the manuscript; N. Caselli, M. Coppola, A. Hillis, L. Menn, R. Varley and S. Wilson for comments on specific sections; C. Casto, T. Regev, F. Mollica and R. Futrell for help with the figures; and S. Swords, N. Jhingan, H. S. Kim and A. Sathe for help with references. E.F. was supported by NIH awards DC016607 and DC016950 from NIDCD, NS121471 from NINDS, and from funds from MIT’s McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Simons Center for the Social Brain, and Quest for Intelligence.

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

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Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Related Articles

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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