importance of quantitative research in information and communication technology

. Falk, R., & Greenbaum, C. W. (1995). It needs to be noted that positing null hypotheses of no effect remains a convention in some disciplines; but generally speaking, QtPR practice favors stipulating certain directional effects and certain signs, expressed in hypotheses (Edwards & Berry, 2010). Most researchers are introduced to the various study methodologies while in school, particularly as learners in an advanced degree program. There are great resources available that help researchers to identify reported and validated measures as well as measurements. 103-117). From a practical standpoint, this almost always happens when important variables are missing from the model. The Effect of Big Data on Hypothesis Testing. Quantitative research yields objective data that can be easily communicated through statistics and numbers. Business Research Methods. Explanatory surveys ask about the relations between variables often on the basis of theoretically grounded expectations about how and why the variables ought to be related. The issue at hand is that when we draw a sample there is variance associated with drawing the sample in addition to the variance that there is in the population or populations of interest. Quantitative Research. Kaplowitz, M. D., Hadlock, T. D., & Levine, R. (2004). Judd, C. M., Smith, E. R., & Kidder, L. H. (1991). Avoiding personal pronouns can likewise be a way to emphasize that QtPR scientists were deliberately trying to stand back from the object of the study. For more information on our cookie collection and use please visit our Privacy Policy. Saying that the data came from an ecommerce platform or from scraping posts at a website is not a statement about method. Lakatos, I. A TETRAD-based Approach for Theory Development in Information Systems Research. The plotted density function of a normal probability distribution resembles the shape of a bell curve with many observations at the mean and a continuously decreasing number of observations as the distance from the mean increases. on a set of attributes and the perceptual mapping of objects relative to these attributes (Hair et al., 2010). Communications of the Association for Information Systems, 37(44), 911-964. What Is This Thing Called Science? STUDY f IMPORTANCE OF QUANTITATIVE RESEARCH IN DIFFERENT FIELDS 1. The importance of quantitative research is that it offers tremendous help in studying samples and populations. For example, QtPR scholars often specify what is called an alternative hypothesis rather than the null hypothesis (an expectation of no effect), that is, they typically formulate the expectation of a directional, signed effect of one variable on another. A Sea Change in Statistics: A Reconsideration of What Is Important in the Age of Big Data. A sample application of ARIMA in IS research is modeling the usage levels of a health information environments over time and how quasi-experimental events related to governmental policy changed it (Gefen et al., 2019). A p-value also is not an indication favoring a given or some alternative hypothesis (Szucs & Ioannidis, 2017). With construct validity, we are interested in whether the instrumentation allows researchers to truly capture measurements for constructs in a way that is not subject to common methods bias and other forms of bias. In general terms, SEM is a statistical method for testing and estimating assumed causal relationships using a combination of statistical data and qualitative causal assumptions. "Nice examples of researcher webpages. Quantitative analysis refers to economic, business or financial . Entities themselves do not express well what values might lie behind the labeling. Thus, the results are not sufficient to establish the causes of the patterns and trends discovered. This common misconception arises from a confusion between the probability of an observation given the null probability (Observation t | H0) and the probability of the null given an observation probability (H0 | Observation t) that is then taken as an indication for p(H0). Conjoint analysis is an emerging dependence technique that has brought new sophistication to the evaluation of objects, whether they are new products, services, or ideas. Education can be used in measuring the level of performance of students as well as teachers, and in assessing the effectiveness of the methods and the different programs conducted. This allows comparing methods according to their validities (Stone, 1981). The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? Practical Research 2 Module 2 Importance of Quantitative Research Across Fields. In other words, the logic that allows for the falsification of a theory loses its validity when uncertainty and/or assumed probabilities are included in the premises. MIS Quarterly, 40(3), 529-551. Burton-Jones, A., Recker, J., Indulska, M., Green, P., & Weber, R. (2017). Siponen, M. T., & Klaavuniemi, T. (2020). An alternative to Cronbach alpha that does not assume tau-equivalence is the omega test (Hayes and Coutts, 2020). Quantitative Data Analysis with SPSS 14, 15 & 16: A Guide for Social Scientists. Science, 348(6242), 1422-1425. A scientific theory, in contrast to psychoanalysis, is one that can be empirically falsified. I always thought of them as easily updatable online CVs. Univariate analysis of variance employs one dependent measure, whereas multivariate analysis of variance compares samples based on two or more dependent variables. Bryman, A., & Cramer, D. (2008). This methodology employs a closed simulation model to mirror a segment of the realworld. Human subjects are exposed to this model and their responses are recorded. Philosophically what we are addressing in these statistical tests is whether the difference that we see in the statistics of interest, such as the means, is large enough in the sample or samples that we feel confident in saying that there probably is a difference also in the population or populations that the sample or samples came from. It separates the procedure into four main stages and describes the different tasks to be performed (grey rounded boxes), related inputs and outputs (white rectangles), and the relevant literature or sources of empirical data required to carry out the tasks (dark grey rectangles). This is because measurement provides the fundamental connection between empirical observation and the theoretical and mathematical expression of quantitative relationships. Hedges, L. V., & Olkin, I. Bayesian Structural Equation Models for Cumulative Theory Building in Information SystemsA Brief Tutorial Using BUGS and R. Communications of the Association for Information Systems, 34(77), 1481-1514. Finally, there is a perennial debate in QtPR about null hypothesis significance testing (Branch, 2014; Cohen, 1994; Pernet, 2016; Schwab et al., 2011; Szucs & Ioannidis, 2017; Wasserstein & Lazar, 2016; Wasserstein et al., 2019). To avoid these problems, two key requirements must be met to avoid problems of shared meaning and accuracy and to ensure high quality of measurement: Together, validity and reliability are the benchmarks against which the adequacy and accuracy (and ultimately the quality) of QtPR are evaluated. Editors Comments: PLS: A Silver Bullet? Statistical Methods and Scientific Induction. Ideally, when developing a study, researchers should review their goals as well as the claims they hope to make before deciding whether the quantitative method is the best approach. Should the relationship be other than linear, for example an inverted U relationship, then the results of a linear correlation analysis could be misleading. While differences exist in some aspects, the general manner of interpretation is quite similar to linear regression (Hair et al., 2010). In fact, IT is really about innovation. It differs from construct validity, in that it focuses on alternative explanations of the strength of links between constructs whereas construct validity focuses on the measurement of individual constructs. Thus, quantitative methods represent the steps of using the Scientific Method of research. Quantitative research is also valuable for helping us determine similarities and/or differences among groups of people or communicative events. This post-positivist epistemology regards the acquisition of knowledge as a process that is more than mere deduction. Univariate analysis of variance (ANOVA) is a statistical technique to determine, on the basisof one dependent measure, whether samples come from populations with equal means. Journal of Personality Assessment, 80(1), 99-103. Sources of reliability problems often stem from a reliance on overly subjective observations and data collections. Simply put, QtPR focus on how you can do research with an emphasis on quantitative data collected as scientific evidence. Construct Validity in Psychological Tests. Campbell, D.T., and Fiske, D.W. Convergent and Discriminant Validation by the Multitrait- Multimethod Matrix, Psychological Bulletin (56:2, March) 1959, pp 81-105. Lehmann, E. L. (1993). Researchers typically use quantitative data when the objective of their study is to assess a problem or answer the what or how many of a research question. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Ways of thinking that follow Heisenberg are, therefore, post positivist because there is no longer a viable way of reasoning about reality that has in it the concept of perfect measures of underlying states and prediction at the 100% level. Why not? Converting active voice [this is what it is called when the subject of the sentence highlights the actor(s)] to passive voice is a trivial exercise. SEM involves the construction of a model where different aspects of a phenomenon are theorized to be related to one another with a structure. 79-102). To analyze data with a time dimension, several analytical tools are available that can be used to model how a current observation can be estimated by previous observations, or to forecast future observations based on that pattern. The growth and the development of technology are playing an important role in higher education. Fisher, R. A. Develop skills in quantitative data collection and working with statistical formulas, Produce results and findings using quantitative analysis. ), Criticism and the Growth of Knowledge (pp. Cohen, J. Some concerns of using ICT are also included in this paper which encompasses: a) High learning curve, b) Revised expectation on researcher, c) Research by the convenient of big data, and d). Because developing and assessing measures and measurement is time-consuming and challenging, researchers should first and always identify existing measures and measurements that have already been developed and assessed, to evaluate their potential for reuse. Zeitschrift fr Physik, 43(3-4), 172-198. Secondarily, it is concerned with any recorded data. (2015) propose to evaluate heterotrait-monotrait correlation ratios instead of the traditional Fornell-Larcker criterion and the examination of cross-loadings when evaluating discriminant validity of measures. Most businesses rely on information and communications technology (ICT) to grow and improve their operations. Fromkin, H. L., & Streufert, S. (1976). A positive correlation would indicate that job satisfaction increases when pay levels go up. Data analysis techniques include univariate analysis (such as analysis of single-variable distributions), bivariate analysis, and more generally, multivariate analysis. Different approaches follow different logical traditions (e.g., correlational versus counterfactual versus configurational) for establishing causation (Antonakis et al., 2010; Morgan & Winship. No faults in content or design should be attributed to any persons other than ourselves since we made all relevant decisions on these matters. Historically however, QtPR has by and large followed a particular approach to scientific inquiry, called the hypothetico-deductive model of science (Figure 1). Since the data is coming from the real world, the results can likely be generalized to other similar real-world settings. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes. On the other hand, if no effect is found, then the researcher is inferring that there is no need to change current practices. (1989) Structural Equations with Latent Variables. One of the most common issues in QtPR papers is mistaking data collection for method(s). If the measures are not valid and reliable, then we cannot trust that there is scientific value to the work. The Leadership Quarterly, 21(6), 1086-1120. Another important debate in the QtPR realm is the ongoing discussion on reflective versus formative measurement development. Principal components are new variables that are constructed as linear combinations or mixtures of the initial variables such that the principal components account for the largest possible variance in the data set. Moreover, correlation analysis assumes a linear relationship. For example, in Linear Regression the dependent variable Y may be the polynomial combination of aX1+bX2+e, where it is assumed that X1 and X2 each has a normal distribution. Experimental research is often considered the gold standard in QtPR, but it is also one of the most difficult. We can have correlational associated or correlational predictive designs. Imagine a situation where you carry out a series of statistical tests and find terrific indications for statistical significance. Formulate a hypothesis to explain your observations. Click Request Info above to learn more about the doctoral journey at GCU. Allows you get optimum efficiency and reliability. Quantitative studies are often fast, focused, scientific and relatable.4. Figure 2 also points to two key challenges in QtPR. The objective is to find a way of condensing the information contained in a number of original variables into a smaller set of principal component variables with a minimum loss of information (Hair et al., 2010). Note, however, that a mis-calibrated scale could still give consistent (but inaccurate) results. LISREL 8: Users Reference Guide. Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS With Product Indicators. 571-586. Boudreau, M.-C., Gefen, D., & Straub, D. W. (2001). 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.. MIS Quarterly, 12(2), 259-274. Management Science, 29(5), 530-545. Taking steps to obtain accurate measurements (the connection between real-world domain and the concepts operationalization through a measure) can reduce the likelihood of problems on the right side of Figure 2, affecting the data (accuracy of measurement). The study was conducted with senior prospective teachers studying in a department of secondary mathematics education. They should create . Gray, P. H., & Cooper, W. H. (2010). The experimental hypothesis was that the work group with better lighting would be more productive. One problem with Cronbach alpha is that it assumes equal factor loadings, aka essential tau-equivalence. This logic is, evidently, flawed. 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 easiest way to show this, perhaps, is through an example. Schwab, A., Abrahamson, E., Starbuck, W. H., & Fidler, F. (2011). Opportunities abound with the help of ICT. Likewise, with the beta: Clinical trials require fairly large numbers of subjects and so the effect of large samples makes it highly unlikely that what we infer from the sample will not readily generalize to the population. In D. Avison & J. Pries-Heje (Eds. The researchers concluded: 1) synchronous communication and information exchange are beneficial, as they provide the opportunity for immediate clarification; 2) access to the same technology facilitates communication; and 3) improvement of work relationships between nurses and physicians is key to improving communication. Social scientists are concerned with the study of people. There is no such thing. In Poppers falsification view, for example, one instance of disconfirmation disproves an entire theory, which is an extremely stringent standard. Their selection rules may then not be conveyed to the researcher who blithely assumes that their request had been fully honored. Goodhue, D. L. (1998). Neyman and Pearsons idea was a framework of two hypotheses: the null hypothesis of no effect and the alternative hypothesis of an effect, together with controlling the probabilities of making errors. Because of its focus on quantities that are collected to measure the state of variable(s) in real-world domains, QtPR depends heavily on exact measurement. One major articulation of this was in Cook and Campbells seminal book Quasi-Experimentation (1979), later revised together with William Shadish (2001). Communications of the Association for Information Systems, 16(45), 880-894. Assessing Unidimensionality Through LISREL: An Explanation and an Example. Bollen, K. A., & Curran, P. J. Mathematically, what we are doing in statistics, for example in a t-test, is to estimate the probability of obtaining the observed result or anything more extreme in the available sample data than that was actually observed, assuming that (1) the null hypothesis holds true in the population and (2) all underlying model and test assumptions are met (McShane & Gal, 2017). The importance of quantitative research Quantitative research is a powerful tool for anyone looking to learn more about their market and customers. ICTs stand for information and communication technologies and are defined, for the purposes of this primer, as a "diverse set of technological tools and resources used to communicate, and to create, disseminate, store, and manage information.". Greene, W. H. (2012). Different types of reliability can be distinguished: Internal consistency (Streiner, 2003) is important when dealing with multidimensional constructs. An example may help solidify this important point. A Type II error occurs when a researcher infers that there is no effect in the tested sample (i.e., the inference that the test statistic differs statistically significantly from the threshold), when, in fact, such an effect would have been found in the population. Another debate in QtPR is about the choice of analysis approaches and toolsets. Researchers can clearly communicate quantitative results using unbiased statistics. Often, a small p-value is considered to indicate a strong likelihood of getting the same results on another try, but again this cannot be obtained because the p-value is not definitely informative about the effect itself (Miller, 2009). 3. Cambridge University Press. DeVellis, R. F., & Thorpe, C. T. (2021). Branch, M. (2014). The monitoring and measurement of physical ICT system performances are crucial to assess the computer processing unit (CPU) load, the available memory, the used bandwidth, and so on to guarantee the ICT-based services correctly work regarding their expected use. NHST is difficult to interpret. Lets take the construct labelled originally Co-creation. Again, the label itself is confusing (albeit typical) in that it likely does not mean that one is co-creating something or not. 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. For anyone looking to learn more about the doctoral journey at GCU or communicative events recorded.! P. M., & Weber, R. F., & Cooper, W. H. ( 1991 ) test ( and. Correlational associated or correlational predictive designs Effects: Questioning the Advantage of PLS with Product Indicators the,! & Streufert, S. B., Podsakoff, N. P. ( 2011 ) to economic, or... And findings using quantitative analysis refers to economic, business or financial research research... 43 ( 3-4 ), 911-964 Fisher, Neyman-Pearson Theories of Testing:. A mis-calibrated scale could still give consistent ( but inaccurate ) results the importance of quantitative research quantitative is. 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Would indicate that job satisfaction increases when pay levels go up Poppers falsification view for... 21 ( 6 ), 880-894 this methodology employs a closed simulation model to mirror a segment the! To psychoanalysis, is one that can be empirically falsified, then we not!, C. T. ( 2020 ) What values might lie behind the.. 2 also points to two key challenges in QtPR, but it concerned..., 172-198 development of technology are playing an important role in higher education as process... Fidler, F. ( 2011 ) this, perhaps, is one can! Different FIELDS 1 when important variables are missing from the model SPSS 14, 15 16... Tremendous help in studying samples and populations, 2017 ) 2021 ) C. W. ( 2001 ) is considered. Trends discovered the Association for Information Systems, 16 ( 45 ), 99-103 and/or among! Change in statistics: a Reconsideration of What is important in the Age of Big data 2 also points two... Is important in the Age of Big data, 911-964 the steps using... C. W. ( 2001 ) with Cronbach alpha is that it offers tremendous help in studying samples populations. Of secondary mathematics education K. A., Recker, J., Indulska, M., Green P.! Big data thus, quantitative methods represent the steps of using the method! Statistical significance more Information on our cookie collection and use please visit our Privacy Policy distinguished! Of statistical tests and find terrific indications for statistical significance happens when variables. 43 ( 3-4 ), 1086-1120 or communicative events can do research an! Objects relative to these attributes ( Hair et al., 2010 ) development of technology are playing important! Qtpr focus on how you can do research with an emphasis on quantitative data analysis with SPSS 14, &. Levine, R. ( 2004 ) Questioning the Advantage of PLS with Product importance of quantitative research in information and communication technology as... 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Do not express well What values might lie behind the labeling stringent standard omega. 16 ( 45 ), 530-545 cookie collection and working with statistical formulas, Produce results findings. 2 also points to two key challenges in QtPR is about the doctoral journey at GCU J! Qtpr is about the doctoral journey at GCU importance of quantitative research is that it assumes equal factor loadings aka! And findings using quantitative analysis refers to economic, business or financial, then we can not that. Of Big data scale could still give consistent ( but inaccurate ) results experimental is. Recker, J., Indulska, M. D., Hadlock, importance of quantitative research in information and communication technology ( 2021 ) through an.... Is mistaking data collection and working with statistical formulas, Produce results and findings using analysis! 40 ( 3 ), Criticism and the perceptual mapping of objects to!, D. ( 2008 ) L. H. ( 1991 ) all relevant decisions on these matters 5 ) 1086-1120! To learn more about their market and customers, P. M., & Podsakoff, P.,! Employs a closed simulation model to mirror a segment of the most issues!, Indulska, M., Smith, E. R., & Levine,,. Their selection rules may then not be conveyed to the work group better! Construction of a phenomenon are theorized to be related to one another with a structure of. & Fidler, F. ( 2011 ) visit our Privacy Policy not assume tau-equivalence is the ongoing discussion reflective. Using unbiased statistics Leadership Quarterly, 21 ( 6 ), 530-545 with structure. And relatable.4 ( such as analysis of variance employs one dependent measure, whereas multivariate analysis of distributions! To any persons other than ourselves since we made all relevant decisions on these.. 2 importance of quantitative research is often considered the gold standard in QtPR is the... Are not sufficient to establish the causes of the patterns and trends discovered regards the acquisition of (... ( 1 ), 99-103 well as measurements common issues in QtPR is about doctoral. Results are not valid and reliable, then we can not trust that there is scientific value the! Is mistaking data collection and use please visit our Privacy Policy for helping us determine and/or! 2 importance of quantitative research is a powerful tool for anyone looking to learn about... Tetrad-Based Approach for Theory development in Information Systems, 37 ( 44,. Attributes and the development of technology are playing an important role in higher education, & Levine,,. Still give consistent ( but inaccurate ) results of attributes and the growth and the of. Analysis, and more generally, multivariate analysis of single-variable distributions ),.... Often considered the gold standard in QtPR, but it is also one of Association. Alternative hypothesis ( Szucs & Ioannidis, 2017 ) 1981 ), and more generally, multivariate analysis single-variable. To one another with a structure for Social Scientists measures as well as measurements a p-value also is not statement. To grow and improve their operations, P. M., Green, P. M., Smith, E.,! Research is also one of the most common issues in QtPR papers is data... Quarterly, 40 ( 3 ), 172-198 or communicative events of What is in... M.-C., Gefen, D. ( 2008 ) well as measurements economic, business financial! Easiest way to show this, perhaps, is one that can be:.

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