importance of quantitative research in information and communication technologyimportance of quantitative research in information and communication technology

The next stage is measurement development, where pools of candidate measurement items are generated for each construct. As for the comprehensibility of the data, we chose the Redinger algorithm with its sensitivity metric for determining how closely the text matches the simplest English word and sentence structure patterns.. Wohlin et al.s (2000) book on Experimental Software Engineering, for example, illustrates, exemplifies, and discusses many of the most important threats to validity, such as lack of representativeness of independent variable, pre-test sensitisation to treatments, fatigue and learning effects, or lack of sensitivity of dependent variables. The Presence of Something or the Absence of Nothing: Increasing Theoretical Precision in Management Research. Moreover, real-world domains are often much more complex than the reduced set of variables that are being examined in an experiment. Think of students sitting in front of a computer in a lab performing experimental tasks or think of rats in cages that get exposed to all sorts of treatments under observation. The fact of the matter is that the universe of all items is quite unknown and so we are groping in the dark to capture the best measures. Goodhue, D. L., Lewis, W., & Thompson, R. L. (2012). Exploratory surveys may also be used to uncover and present new opportunities and dimensions about a population of interest. For example, there is a longstanding debate about the relative merits and limitations of different approaches to structural equation modelling (Goodhue et al., 2007, 2012; Hair et al., 2011; Marcoulides & Saunders, 2006; Ringle et al., 2012), including alternative approaches such as Bayesian structural equation modeling (Evermann & Tate, 2014), or the TETRAD approach (Im & Wang, 2007). quantitative or qualitative methods is barren, and that the fit-for-purpose principle should be the central issue in methodological design. Given that the last update of that resource was 2004, we also felt it prudent to update the guidelines and information to the best of our knowledge and abilities. Chin, W. W. (2001). The quantitative methods acquired in a Sustainability Master's online combine information from various sources to create more informed predictions, while importantly providing the scientific reasoning to accurately describe what is known and what is not. A researcher that gathers a large enough sample can reject basically any point-null hypothesis because the confidence interval around the null effect often becomes very small with a very large sample (Lin et al., 2013; Guo et al., 2014). Im, G., & Wang, J. But is it? The simplest distinction between the two is that quantitative research focuses on numbers, and qualitative research focuses on text, most importantly text that captures records of what people have said, done, believed, or experienced about a particular phenomenon, topic, or event. Other sources of reliability problems stem from poorly specified measurements, such as survey questions that are imprecise or ambiguous, or questions asked of respondents who are either unqualified to answer, unfamiliar with, predisposed to a particular type of answer, or uncomfortable to answer. See for example: https://en.wikibooks.org/wiki/Handbook_of_Management_Scales. This video emphasized the Importance of quantitative research across various fields such as Science, Technology, Engineering, and Mathematics (STEM), Account. R-squared or R2: Coefficient of determination: Measure of the proportion of the variance of the dependent variable about its mean that is explained by the independent variable(s). A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., Buck, S., Chambers, C. D., Chin, G., Christensen, G., Contestabile, M., Dafoe, A., Eich, E., Freese, J., Glennerster, R., Goroff, D., Green, D. P., Hesse, B., Humphreys, M., Ishiyama, J., Karlan, D., Kraut, A., Lupia, A., Mabry, P., Madon, T., Malhotra, N., Mayo-Wilson, E., McNutt, M., Miguel, E., Paluck, E. L., Simonsohn, U., Soderberg, C., Spellman, B. Logit analysis is a special form of regression in which the criterion variable is a non-metric, dichotomous (binary) variable. The resulting perceptual maps show the relative positioning of all objects, but additional analysis is needed to assess which attributes predict the position of each object (Hair et al., 2010). Journal of Management Information Systems, 19(2), 129-174. Experimentation in Software Engineering: An Introduction. The alpha protection levels are often set at .05 or lower, meaning that the researcher has at most only a 5% risk of being wrong and subject to a Type I error. A. In research concerned with exploration, problems tend to accumulate from the right to the left of Figure 2: No matter how well or systematically researchers explore their data, they cannot guarantee that their conclusions reflect reality unless they first take steps to ensure the accuracy of their data. MIS Quarterly, 35(2), 261-292. Multivariate analyses, broadly speaking, refer to all statistical methods that simultaneously analyze multiple measurements on each individual or object under investigation (Hair et al., 2010); as such, many multivariate techniques are extensions of univariate and bivariate analysis. (2019). It allows you to gain reliable, objective insights from data and clearly understand trends and patterns. Laboratory experiments take place in a setting especially created by the researcher for the investigation of the phenomenon. Experiments are specifically intended to examine cause and effect relationships. Thee researcher completely determines the nature and timing of the experimental events (Jenkins, 1985). Wiley. Gelman, A., Carlin, J. Equity issues. If the measures are not valid and reliable, then we cannot trust that there is scientific value to the work. Hence, positivism differentiates between falsification as a principle, where one negating observation is all that is needed to cast out a theory, and its application in academic practice, where it is recognized that observations may themselves be erroneous and hence where more than one observation is usually needed to falsify a theory. Quantitative research has the goal of generating knowledge and gaining understanding of the social world. Similarly, the choice of data analysis can vary: For example, covariance structural equation modeling does not allow determining the cause-effect relationship between independent and dependent variables unless temporal precedence is included. Dunning, T. (2012). Regarding Type II errors, it is important that researchers be able to report a beta statistic, which is the probability that they are correct and free of a Type II error. Inferential analysis refers to the statistical testing of hypotheses about populations based on a sample typically the suspected cause and effect relationships to ascertain whether the theory receives support from the data within certain degrees of confidence, typically described through significance levels. This task can be fulfilled by performing any field-study QtPR method (such as a survey or experiment) that provides a sufficiently large number of responses from the target population of the respective study. The most important difference between such time-series data and cross-sectional data is that the added time dimension of time-series data means that such variables change across both units and time. This is because in experiments the researchers deliberately impose some treatment to one or more groups of respondents (the one or more treatment groups) but not to another group (the control group) while also maintaining control over other potential confounding factors in order to observe responses. Obtaining such a standard might be hard at times in experiments but even more so in other forms of QtPR research; however, researchers should at least acknowledge it as a limitation if they do not actually test it, by using, for example, a Kolmogorov-Smirnoff test of the normality of the data or an Anderson-Darling test (Corder & Foreman, 2014). ber den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik (in German). Evaluating Structural Equations with Unobservable Variables and Measurement Error. Low power thus means that a statistical test only has a small chance of detecting a true effect or that the results are likely to be distorted by random and systematic error. Often, this stage is carried out through pre- or pilot-tests of the measurements, with a sample that is representative of the target research population or else another panel of experts to generate the data needed. Survey Research Methods (3rd ed.). For instance, recall the challenge of measuring compassion: A question of validity is to demonstrate that measurements are focusing on compassion and not on empathy or other related constructs. Knowledge is acquired through both deduction and induction. Therefore, a scientific theory is by necessity a risky endeavor, i.e., it may be thrown out if not supported by the data. As suggested in Figure 1, at the heart of QtPR in this approach to theory-evaluation is the concept of deduction. Organizational Research Methods, 13(4), 668-689. PLS-Graph users guide. And in quantitative constructs and models, the whole idea is (1) to make the model understandable to others and (2) to be able to test it against empirical data. Communications of the Association for Information Systems, 12(2), 23-47. In Lakatos view, theories have a hard core of ideas, but are surrounded by evolving and changing supplemental collections of both hypotheses, methods, and tests the protective belt. In this sense, his notion of theory was thus much more fungible than that of Popper. The variables that are chosen as operationalizations to measure a theoretical construct must share its meaning (in all its complexity if needed). We have co-authored a set of updated guidelines for quantitative researchers for dealing with these issues (Mertens & Recker, 2020). Rather, they develop one after collecting the data. Reviewers should be especially honed in to measurement problems for this reason. IS research is a field that is primarily concerned with socio-technical systems comprising individuals and collectives that deploy digital information and communication technology for tasks in business, private, or social settings. Surveys in this sense therefore approach causality from a correlational viewpoint; it is important to note that there are other traditions toward causal reasoning (such as configurational or counterfactual), some of which cannot be well-matched with data collected via survey research instruments (Antonakis et al., 2010; Pearl, 2009). The objective of this test is to falsify, not to verify, the predictions of the theory. Researchers using this method do not generally begin with a hypothesis. A test statistic to assess the statistical significance of the difference between two sets of sample means. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Houghton Mifflin. Information Systems Research, 24(4), 906-917. Collect and process your data using one or more of the methods below. In Poppers falsification view, for example, one instance of disconfirmation disproves an entire theory, which is an extremely stringent standard. Epidemiology, 24(1), 69-72. Figure 2 describes in simplified form the QtPR measurement process, based on the work of Burton-Jones and Lee (2017). #Carryonlearning Advertisement Natural Experiments in the Social Sciences: A Design-Based Approach. In the course of their doctoral journeys and careers, some researchers develop a preference for one particular form of study. The procedure shown describes a blend of guidelines available in the literature, most importantly (MacKenzie et al., 2011; Moore & Benbasat, 1991). They also list the different tests available to examine reliability in all its forms. Introductions to their ideas and those of relevant others are provided by philosophy of science textbooks (e.g., Chalmers, 1999; Godfrey-Smith, 2003). Quantitative Research in Communication is ideal for courses in Quantitative Methods in Communication, Statistical Methods in Communication, Advanced Research Methods (undergraduate), and Introduction to Research Methods (Graduate) in departments of communication, educational psychology, psychology, and mass communication. If well designed, quantitative studies are relatable in the sense that they are designed to make predictions, discover facts and test existing hypotheses. Branch, M. (2014). Recker, J., & Rosemann, M. (2010). This distinction is important. The p-value below .05 is there because when Mr. Pearson (of the Pearson correlation) was asked what he thought an appropriate threshold should be, and he said one in twenty would be reasonable. The treatments always precede the collection of the DVs. That is why pure philosophical introspection is not really science either in the positivist view. But statistical conclusion and internal validity are not sufficient, instrumentation validity (in terms of measurement validity and reliability) matter as well: Unreliable measurement leads to attenuation of regression path coefficients, i.e. You are hopeful that your model is accurate and that the statistical conclusions will show that the relationships you posit are true and important. Statistics Done Wrong: The Woefully Complete Guide. Without delving too deeply into the distinctions and their implications, one difference is that qualitative positive researchers generally assume that reality can be discovered to some extent by a researcher as well as described by measurable properties (which are social constructions) that are independent of the observer (researcher) and created instruments and instrumentation. For example, both positivist and interpretive researchers agree that theoretical constructs, or important notions such as causality, are social constructions (e.g., responses to a survey instrument). Lehmann, E. L. (1993). Accounting principles try to control this, but, as cases like Enron demonstrate, it is possible for reported revenues or earnings to be manipulated. One aspect of this debate focuses on supplementing p-value testing with additional analysis that extra the meaning of the effects of statistically significant results (Lin et al., 2013; Mohajeri et al., 2020; Sen et al., 2022). It also generates knowledge and create understanding about the social world. STUDY f IMPORTANCE OF QUANTITATIVE RESEARCH IN DIFFERENT FIELDS 1. A common problem at this stage is that researchers assume that labelling a construct with a name is equivalent to defining it and specifying its content domains: It is not. These proposals essentially suggest retaining p-values. Squaring the correlation r gives the R2, referred to as the explained variance. (2007). From a practical standpoint, this almost always happens when important variables are missing from the model. Block, J. It is also important to recognize, there are many useful and important additions to the content of this online resource in terms of QtPR processes and challenges available outside of the IS field. An example illustrates the error: if a person is a researcher, it is very likely she does not publish in MISQ [null hypothesis]; this person published in MISQ [observation], so she is probably not a researcher [conclusion]. (Note that this is an entirely different concept from the term control used in an experiment where it means that one or more groups have not gotten an experimental treatment; to differentiate it from controls used to discount other explanations of the DV, we can call these experimental controls.).

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