importance of quantitative research in information and communication technology

The typical way to set treatment levels would be a very short delay, a moderate delay and a long delay. If the data or phenomenon concerns changes over time, an analysis technique is required that allows modeling differences in data over time. But as with many other concepts, one should note that other characterizations of content validity also exist (e.g., Rossiter, 2011). Information and communication technology, or ICT, is defined as the combination of informatics . The basic procedure of a quantitative research design is as follows:3, GCU supports four main types of quantitative research approaches: Descriptive, correlational, experimental and comparative.4. This debate focuses on the existence, and mitigation, of problematic practices in the interpretation and use of statistics that involve the well-known p-value. .Unlike covariance-based approaches to structural equation modeling, PLS path modeling does not fit a common factor model to the data, it rather fits a composite model. Hackett. Assessing measure and measurement validity is the critical first step in QtPR. In turn, there are theoretical assessments of validity (for example, for content validity,), which assess how well an operationalized measure fits the conceptual definition of the relevant theoretical construct; and empirical assessments of validity (for example, for convergent and discriminant validity), which assess how well collected measurements behave in relation to the theoretical expectations. In theory, it is enough, in Poppers way of thinking, for one observation that contradicts the prediction of a theory to falsify it and render it incorrect. The current ICT master plan for the Philippines dates back to 2006. MANOVA is useful when the researcher designs an experimental situation (manipulation of several non-metric treatment variables) to test hypotheses concerning the variance in group responses on two or more metric dependent variables (Hair et al., 2010). Christensen, R. (2005). Cronbach, L. J. Other popular ways to analyze time-series data are latent variable models such as latent growth curve models, latent change score models, or bivariate latent difference score models (Bollen & Curran, 2006; McArdle, 2009). Sage. Information Systems Research, 28(3), 451-467. The final stage is validation, which is concerned with obtaining statistical evidence for reliability and validity of the measures and measurements. This methodology is similar to experimental simulation, in that with both methodologies the researcher designs a closed setting to mirror the real world and measures the response of human subjects as they interact within the system. Information and Organization, 30(1), 100287. Data computing equipment makes it possible to process and analyze data quickly, even with large sample sizes. The term research instrument is neutral and does not imply a methodology. This is the Falsification Principle and the core of positivism. (1971). Our knowledge about research starts from here because it will lead us to the path of changing the world. There are great resources available that help researchers to identify reported and validated measures as well as measurements. The researcher analyses the data with the help of statistics. The next stage is measurement development, where pools of candidate measurement items are generated for each construct. This video emphasized the Importance of quantitative research across various fields such as Science, Technology, Engineering, and Mathematics (STEM), Account. Multicollinearity can result in paths that are statistically significant when they should not be, they can be statistically insignificant when they are statistically significant, and they can even change the sign of a statistically significant path. Kim, G., Shin, B., & Grover, V. (2010). Since the data is coming from the real world, the results can likely be generalized to other similar real-world settings. Gigerenzer, G. (2004). This post-positivist epistemology regards the acquisition of knowledge as a process that is more than mere deduction. first of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and Human Relations, 46(2), 121-142. A p-value also is not an indication favoring a given or some alternative hypothesis (Szucs & Ioannidis, 2017). However, this is a happenstance of the statistical formulas being used and not a useful interpretation in its own right. Note, however, that a mis-calibrated scale could still give consistent (but inaccurate) results. The decision tree presented in Figure 8 provides a simplified guide for making the right choices. This notion that scientists can forgive instances of disproof as long as the bulk of the evidence still corroborates the base theory lies behind the general philosophical thinking of Imre Lakatos (1970). If multiple measurements are taken, reliable measurements should all be consistent in their values. Communications of the Association for Information Systems, 4(7), 1-77. In post-positivist understanding, pure empiricism, i.e., deriving knowledge only through observation and measurement, is understood to be too demanding. Moving from the left (theory) to the middle (instrumentation), the first issue is that of shared meaning. Different approaches follow different logical traditions (e.g., correlational versus counterfactual versus configurational) for establishing causation (Antonakis et al., 2010; Morgan & Winship. Like the theoretical research model of construct relationships itself, they are intended to capture the essence of a phenomenon and then to reduce it to a parsimonious form that can be operationalized through measurements. The table in Figure 10 presents a number of guidelines for IS scholars constructing and reporting QtPR research based on, and extended from, Mertens and Recker (2020). This structure is a system of equations that captures the statistical properties implied by the model and its structural features, and which is then estimated with statistical algorithms (usually based on matrix algebra and generalized linear models) using experimental or observational data. Churchill Jr., G. A. An Updated Guideline for Assessing Discriminant Validity. A Post-Positivist Answering Back. Data analysis techniques include univariate analysis (such as analysis of single-variable distributions), bivariate analysis, and more generally, multivariate analysis. Linear probability models accommodate all types of independent variables (metric and non-metric) and do not require the assumption of multivariate normality (Hair et al., 2010). (2020). Statistical control variables are added to models to demonstrate that there is little-to-no explained variance associated with the designated statistical controls. Even the bottom line of financial statements is structured by human thinking. Research results are totally in doubt if the instrument does not measure the theoretical constructs at a scientifically acceptable level. 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. All other things being equal, field experiments are the strongest method that a researcher can adopt. Formulate a hypothesis to explain your observations. Miller, J. (1961). Multivariate Data Analysis (7th ed.). North-Holland. The number of such previous error terms determines the order of the moving average. Often, we approximate objective data through inter-subjective measures in which a range of individuals (multiple study subjects or multiple researchers, for example) all rate the same observation and we look to get consistent, consensual results. Their selection rules may then not be conveyed to the researcher who blithely assumes that their request had been fully honored. Historically, internal validity was established through the use of statistical control variables. Practical Research 2 Module 2 Importance of Quantitative Research Across Fields. The original inspiration for this approach to science came from the scientific epistemology of logical positivism during the 1920s and 1930s as developed by the Vienna Circle of Positivists, primarily Karl Popper,. And because even the most careful wording of questions in a survey, or the reliance on non-subjective data in data collection does not guarantee that the measurements obtained will indeed be reliable, one precondition of QtPR is that instruments of measurement must always be tested for meeting accepted standards for reliability. A Sea Change in Statistics: A Reconsideration of What Is Important in the Age of Big Data. From a practical standpoint, this almost always happens when important variables are missing from the model. This is . Central to understanding this principle is the recognition that there is no such thing as a pure observation. Pine Forge Press. This idea introduced the notions of control of error rates, and of critical intervals. 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). By chance, of course, there could be a preponderance of males or unhealthier persons in one group versus the other but in such rare cases researchers can regulate this in media res and adjust the sampling using a quota process (Trochim et al., 2016). External Validity in IS Survey Research. Increasing the pace of globalization, this trend opened new opportunities not only for developed nations but also for improving ones as the costs of ICT technologies decrease. Does it mean that the firm exists or not? (2019). Pearl, J. Random assignment makes it highly unlikely that subjects prior knowledge impacted the DV. If they do not segregate or differ from each other as they should, then it is called a discriminant validity problem. The ability to explain any observation as an apparent verification of psychoanalysis is no proof of the theory because it can never be proven wrong to those who believe in it. Tests of content validity (e.g., through Q-sorting) are basically intended to verify this form of randomization. Aguirre-Urreta, M. I., & Marakas, G. M. (2012). This step concerns the, The variables that are chosen as operationalizations must also guarantee that data can be collected from the selected empirical referents accurately (i.e., consistently and precisely). Wohlin, C., Runeson, P., Hst, M., Ohlsson, M. C., Regnell, B., & Wessln, A. In E. Mumford, R. Hirschheim, & A. T. Wood-Harper (Eds. If items load appropriately high (viz., above 0.7), we assume that they reflect the theoretical constructs. Supported by artificial intelligence and 5G techniques in mobile information systems, the rich communication services (RCS) are emerging as new media outlets and conversational agents for both institutional and individual users in China, which inherit the advantages of the short messaging service (SMS) with larger coverage and higher reach rate. Intermediaries may have decided on their own not to pull all the data the researcher requested, but only a subset. If you are interested in conducting research or enhancing your skills in a research field, earning a doctoral degree can support your career goals. MIS Quarterly, 35(2), 293-334. Wiley. Journal of the Association for Information Systems, 21(4), 1072-1102. A Comparison of Web and Mail Survey Response Rates. (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. In physical and anthropological sciences or other distinct fields, quantitative research is methodical experimental research of noticeable events via analytical, numerical, or computational methods. I still check those, "Resubmitted two revisions today. Also note that the procedural model in Figure 3 is not concerned with developing theory; rather it applies to the stage of the research where such theory exists and is sought to be empirically tested. The primary strength of experimental research over other research approaches is the emphasis on internal validity due to the availability of means to isolate, control and examine specific variables (the cause) and the consequence they cause in other variables (the effect). We felt that we needed to cite our own works as readily as others to give readers as much information as possible at their fingertips. The amount is with respect to some known units of measurement. Checking for manipulation validity differs by the type and the focus of the experiment, and its manipulation and experimental setting. Following the MAP (Methods, Approaches, Perspectives) in Information Systems Research. We are ourselves IS researchers but this does not mean that the advice is not useful to researchers in other fields. Different treatments thus constitute different levels or values of the construct that is the independent variable. Several threats are associated with the use of NHST in QtPR. There is not enough space here to cover the varieties or intricacies of different quantitative data analysis strategies. Social scientists, including communication researchers, use quantitative research to observe phenomena or occurrences that affect individuals. While modus tollens is logically correct, problems in its application can still arise. Tabachnick, B. G., & Fidell, L. S. (2001). If they include measures that do not represent the construct well, measurement error results. Assuming that the experimental treatment is not about gender, for example, each group should be statistically similar in terms of its gender makeup. PLS-SEM: Indeed a Silver Bullet. (2009). 0. With the caveat offered above that in scholarly praxis, null hypotheses are tested today only in certain disciplines, the underlying testing principles of NHST remain the dominant statistical approach in science today (Gigerenzer, 2004). The procedure shown describes a blend of guidelines available in the literature, most importantly (MacKenzie et al., 2011; Moore & Benbasat, 1991). Field experiments are difficult to set up and administer, in part because they typically involve collaborating with some organization that hosts a particular technology (say, an ecommerce platform). (2020). Garcia-Prez, M. A. Understanding and addressing these challenges are important, independent from whether the research is about confirmation or exploration. One of the most prominent current examples is certainly the set of Bayesian approaches to data analysis (Evermann & Tate, 2014; Gelman et al., 2013; Masson, 2011). Surveys thus involve collecting data about a large number of units of observation from a sample of subjects in field settings through questionnaire-type instruments that contain sets of printed or written questions with a choice of answers, and which can be distributed and completed via mail, online, telephone, or, less frequently, through structured interviewing. 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.

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