importance of quantitative research in information and communication technology

MIS Quarterly, 25(1), 1-16. Bayesian approaches are essentially model selection procedures that compute a comparison between competing hypotheses or models, and where available knowledge about parameters in a statistical model is updated with the information in observed data. Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Cochran, W. G. (1977). And, crucially, inferring temporal precedence, i.e., establishing that the cause came before the effect, in a one-point in time survey is at best related to self-reporting by the subject. The purpose of survey research in exploration is to become more familiar with a phenomenon or topic of interest. The Logic of Inductive Inference. There are different types of validity that are important to identify. Why is quantitative research so important in this field? QtPR describes a set of techniques to answer research questions with an emphasis on state-of-the-art analysis of quantitative data, that is, types of data whose value is measured in the form of numbers, with a unique numerical value associated with each data set. 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. Factor analysis is a statistical approach that can be used to analyze interrelationships among a large number of variables and to explain these variables in terms of their common underlying dimensions (factors) (Hair et al., 2010). Sometimes there is no alternative to secondary sources, for example, census reports and industry statistics. This paper focuses on the linkage between ICT and output growth. "Nice examples of researcher webpages. The choice of the correct analysis technique is dependent on the chosen QtPR research design, the number of independent and dependent (and control) variables, the data coding and the distribution of the data received. In the course of their doctoral journeys and careers, some researchers develop a preference for one particular form of study. We can have correlational associated or correlational predictive designs. Lab experiments typically offer the most control over the situation to the researcher, and they are the classical form of experiments. This is reflected in their dominant preference to describe not the null hypothesis of no effect but rather alternative hypotheses that posit certain associations or directions in sign. Goodhue, D. L., Lewis, W., & Thompson, R. L. (2007). Were it broken down into its components, there would be less room for criticism. The views and opinions expressed in this article are those of the authors and do not Australasian Journal of Information Systems, 24, doi:10.3127/ajis.v24i0.2045. NHST logic is incomplete. Cook, T. D. and D. T. Campbell (1979). Sample size sensitivity occurs in NHST with so-called point-null hypotheses (Edwards & Berry, 2010), i.e., predictions expressed as point values. The current ICT master plan for the Philippines dates back to 2006. F. Quantitative Research and Social Science > the method employed in this type of quantitative social research are mostly typically the survey and the experiment. QtPR scholars sometime wonder why the thresholds for protection against Type I and Type II errors are so divergent. SEM requires one or more hypotheses between constructs, represented as a theoretical model, operationalizes by means of measurement items, and then tests statistically. 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). Centefelli, R. T., & Bassellier, G. (2009). Descriptive and correlational research usually involves non-experimental, observational data collection techniques, such as survey instruments, which do not involve controlling or manipulating independent variables. Diamantopoulos, A., & Siguaw, J. The most common forms are non-equivalent groups design the alternative to a two-group pre-test-post-test design, and non-equivalent switched replication design, in which an essential experimental treatment is replicated by switching the treatment and control group in two subsequent iterations of the experiment (Trochim et al. Overall, modern social scientists favor theorizing models with expressed causal linkages and predictions of correlational signs. Different methods in each tradition are available and are typically available in statistics software applications such as Stata, R, SPSS, or others. A second big problem is the inappropriate design of treatment and tasks. Randomizing the treatment times, however, allows a scholar to generalize across the whole range of delays, hence increasing external validity within the same, alternatively designed study. Revisiting Bias Due to Construct Misspecification: Different Results from Considering Coefficients in Standardized Form. When the data do not contradict the hypothesized predictions of the theory, it is temporarily corroborated. The standard value for betas has historically been set at .80 (Cohen 1988). In fact, those who were not aware, depending on the nature of the treatments, may be responding as if they were assigned to the control group. Journal of the Association for Information Systems, 19(8), 752-774. In fact, several ratings readily gleaned from the platform were combined to create an aggregate score. This resource is structured into eight sections. Importance of ICT in Developing Economies The spread of ICT technologies over the world has been dramatic in the past years, spearheading development all over the world. (1961). Research Methodologies and MIS Research. In some (nut not all) experimental studies, one way to check for manipulation validity is to ask subjects, provided they are capable of post-experimental introspection: Those who were aware that they were manipulated are testable subjects (rather than noise in the equations). Observation means looking at people and listening to them talk. For example, using a survey instrument for data collection does not allow for the same type of control over independent variables as a lab or field experiment. Fishers idea is essentially an approach based on proof by contradiction (Christensen, 2005; Pernet, 2016): we pose a null model and test if our data conforms to it. Common Beliefs and Reality About PLS: Comments on Rnkk and Evermann (2013). Claes Wohlins book on Experimental Software Engineering (Wohlin et al., 2000), 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. Sage Publications. MIS Quarterly, 36(1), iii-xiv. Quantitative studies are often fast, focused, scientific and relatable.4. 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. For example, their method could have been some form of an experiment that used a survey questionnaire to gather data before, during, or after the experiment. Randomizing gender and health of participants, for example, should result in roughly equal splits between experimental groups so the likelihood of a systematic bias in the results from either of these variables is low. Doings so confers some analytical benefits (such as using a one-tailed statistical test rather than a two-tailed test), but the most likely reason for doing this is that confirmation, rather than disconfirmation of theories is a more common way of conducting QtPR in modern social sciences (Edwards & Berry, 2010; Mertens & Recker, 2020). 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. Since no change in the status quo is being promoted, scholars are granted a larger latitude to make a mistake in whether this inference can be generalized to the population. (2017). (2010) suggest that confirmatory studies are those seeking to test (i.e., estimating and confirming) a prespecified relationship, whereas exploratory studies are those that define possible relationships in only the most general form and then allow multivariate techniques to search for non-zero or significant (practically or statistically) relationships. Natural Experiments in the Social Sciences: A Design-Based Approach. If the measures are not valid and reliable, then we cannot trust that there is scientific value to the work. Thinking About Measures and Measurement in Positivist Research: A Proposal for Refocusing on Fundamentals. Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). Fromkin, H. L., & Streufert, S. (1976). 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). A treatment is a manipulation of the real world that an experimenter administers to the subjects (also known as experimental units) so that the experimenter can observe a response. If there are clear similarities, then the instrument items can be assumed to be reasonable, at least in terms of their nomological validity. 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). Quantitative Research. Flourishing for a brief period in the early 1900s, logical positivism, which argued that all natural laws could be reduced to the mathematics of logic, was one culmination of a deterministic positivism, but these ideas came out of a long tradition of thinking of the world as an objective reality best described by philosophical determinism. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. Quantitative Research is generally used in the field of community health, marketing, sociology,economics, psychology, demographics, gender studies, political science. 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 task involves identifying and carefully defining what the construct is intended to conceptually represent or capture, discussing how the construct differs from other related constructs that may already exist, and defining any dimensions or domains that are relevant to grasping and clearly defining the conceptual theme or content of the construct it its entirety. Only then, based on the law of large numbers and the central limit theorem can we upheld (a) a normal distribution assumption of the sample around its mean and (b) the assumption that the mean of the sample approximates the mean of the population (Miller & Miller 2012). Within statistical bounds, a set of measures can be validated and thus considered to be acceptable for further empiricism. The purpose of research involving survey instruments for explanation is to test theory and hypothetical causal relations between theoretical constructs. Information and Organization, 30(1), 100287. Too Big to Fail: Large Samples and the p-Value Problem. Standard readings on this matter are Shadish et al. Marcoulides, G. A., & Saunders, C. (2006). (1970). Miller, J. Our argument, hence, is that IS researchers who work with quantitative data are not truly positivists, in the historical sense. (2020). Chalmers, A. F. (1999). Human Relations, 46(2), 121-142. Guo, W., Straub, D. W., & Zhang, P. (2014). 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. As such, it represents an extension of univariate analysis of variance (ANOVA). A. When Einstein proposed it, the theory may have ended up in the junk pile of history had its empirical tests not supported it, despite the enormous amount of work put into it and despite its mathematical appeal. Quantitative research yields objective data that can be easily communicated through statistics and numbers. Haller, H., & Kraus, S. (2002). The emphasis in sentences using the personal pronouns is on the researcher and not the research itself. Typically, QtPR starts with developing a theory that offers a hopefully insightful and novel conceptualization of some important real-world phenomena. Other tests include factor analysis (a latent variable modeling approach) or principal component analysis (a composite-based analysis approach), both of which are tests to assess whether items load appropriately on constructs represented through a mathematically latent variable (a higher order factor). Below we summarize some of the most imminent threats that QtPR scholars should be aware of in QtPR practice: 1. Multivariate analysis of variance (MANOVA): Multivariate analysis of variance (MANOVA) is a statistical technique that can be used to simultaneously explore the relationship between several categorical independent variables (usually referred to as treatments) and two or more metric dependent variables. (2021). This pure positivist attempt at viewing scientific exploration as a search for the Truth has been replaced in recent years with the recognition that ultimately all measurement is based on theory and hence capturing a truly objective observation is impossible (Coombs, 1976). A Measurement Instrument for Process Modeling Research: Development, Test and Procedural Model. On the other hand, field experiments typically achieve much higher levels of ecological validity whilst also ensuring high levels of internal validity. For example, the computer sciences also have an extensive tradition in discussing QtPR notions, such as threats to validity. Textbooks on survey research that are worth reading include Floyd Flowers textbook (Fowler, 2001), Devellis and Thorpe (2021), plus a few others (Babbie, 1990; Czaja & Blair, 1996). In D. Avison & J. Pries-Heje (Eds. The treatments always precede the collection of the DVs. Information and communications technology (ICT) is an extensional term for information technology (IT) that stresses the role of unified communications and the integration of telecommunications (telephone lines and wireless signals) and computers, as well as necessary enterprise software, middleware, storage and audiovisual, that enable users to access, store, transmit, understand and . Fisher, R. A. However, "states of knowledge surveys" are still rarely found in the field of science education. Oxford University Press. Other researchers might feel that you did not draw well from all of the possible measures of the User Information Satisfaction construct. How important is quantitative research to communication? A procedure for the analysis of LInear Structural RELations among one or more sets of variables and variates. Information and communication technology, or ICT, is defined as the combination of informatics . They involve manipulations in a real world setting of what the subjects experience. (1989) Structural Equations with Latent Variables. Due to the fact that the research requires its conductor to use mathematical analysis to investigate what is being observed, the information collected must be in numbers. Our knowledge about research starts from here because it will lead us to the path of changing the world. Moreover, real-world domains are often much more complex than the reduced set of variables that are being examined in an experiment. Importance of quantitative research. 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. The convention is thus that we do not want to recommend that new medicines be taken unless there is a substantial and strong reason to believe that this can be generalized to the population (a low alpha). American Psychological Association. If you are interested in different procedural models for developing and assessing measures and measurements, you can read up on the following examples that report at some lengths about their development procedures: (Bailey & Pearson, 1983; Davis, 1989; Goodhue, 1998; Moore & Benbasat, 1991; Recker & Rosemann, 2010; Bagozzi, 2011). What could this possibly mean? Another debate in QtPR is about the choice of analysis approaches and toolsets. The data of the study used both quantitative and qualitative research approaches have been . Another problem with Cronbachs alpha is that a higher alpha can most often be obtained simply by adding more construct items in that alpha is a function of k items. This is because measurement provides the fundamental connection between empirical observation and the theoretical and mathematical expression of quantitative relationships. Organizational Research Methods, 25(1), 6-14. MIS Quarterly, 35(2), 261-292. Controlling for Lexical Closeness in Survey Research: A Demonstration on the Technology Acceptance Model. Mazaheri, E., Lagzian, M., & Hemmat, Z. 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. Practical Research 2 Module 2 Importance of Quantitative Research Across Fields. The last forty years have seen significant growth in the area of research in science education in Brazil. Figure 8 highlights that when selecting a data analysis technique, a researcher should make sure that the assumptions related to the technique are satisfied, such as normal distribution, independence among observations, linearity, and lack of multi-collinearity between the independent variables, and so forth (Mertens et al. 1 Quantitative research produces objective data that can be clearly communicated through statistics and numbers. Einsteins Theory of Relativity is a prime example, according to Popper, of a scientific theory. The conceptual labeling of this construct is too broad to easily convey its meaning. Journal of Marketing Research, 16(1), 64-73. Detmar STRAUB, David GEFEN, and Jan RECKER. Judd, C. M., Smith, E. R., & Kidder, L. H. (1991). It may, however, influence it, because different techniques for data collection or analysis are more or less well suited to allow or examine variable control; and likewise different techniques for data collection are often associated with different sampling approaches (e.g., non-random versus random). Specifying Formative Constructs in IS Research. For example, statistical conclusion validity tests the inference that the dependent variable covaries with the independent variable, as well as that of any inferences regarding the degree of their covariation (Shadish et al., 2001). Laboratory experiments take place in a setting especially created by the researcher for the investigation of the phenomenon. ), Educational Measurement (2nd ed., pp. Can not trust that there is no alternative to secondary sources importance of quantitative research in information and communication technology for example, the Sciences... 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