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.". A second form of randomization (random selection) relates to sampling, that is, the procedures used for taking a predetermined number of observations from a larger population, and is therefore an aspect of external validity (Trochim et al. MIS Quarterly, 13(2), 147-169. Interpretive researchers, on the other hand, start out with the assumption that access to reality (given or socially constructed) is only through social constructions such as language, consciousness, and shared meanings. Interpretive Case Studies in IS Research: Nature and Method. The researchers need to change with changing times and need to understand today's fast changing knowledge base and its peculiarities. (2001). This paper focuses on the linkage between ICT and output growth. They also list the different tests available to examine reliability in all its forms. (2019). Any design error in experiments renders all results invalid. 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. Why is the Hypothetico-Deductive (H-D) Method in Information Systems not an H-D Method? Moreover, experiments without strong theory tend to be ad hoc, possibly illogical, and meaningless because one essentially finds some mathematical connections between measures without being able to offer a justificatory mechanism for the connection (you cant tell me why you got these results). Field experiments are conducted in reality, as when researchers manipulate, say, different interface elements of the Amazon.com webpage while people continue to use the ecommerce platform. It is a closed deterministic system in which all of the independent and dependent variables are known and included in the model. The data for this quantitative research were analyzed for both descriptive and inferential statistic using SPSS (version 21) software. Quantitative research produces objective data that can be clearly communicated through statistics and numbers. Some of them relate to the issue of shared meaning and others to the issue of accuracy. 2. Business it can improve the over-all marketing strategy, help the company 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. 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. It is also vital because many constructs of interest to IS researchers are latent, meaning that they exist but not in an immediately evident or readily tangible way. The use of ICTs by nurses can impact their practice, modifying the ways in which they plan, provide, document, and review clinical care. NHST originated from a debate that mainly took place in the first half of the 20th century between Fisher (e.g., 1935a, 1935b; 1955) on the one hand, and Neyman and Pearson (e.g., 1928, 1933) on the other hand. 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). MIS Quarterly, 33(2), 237-262. 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. One could trace this lineage all the way back to Aristotle and his opposition to the metaphysical thought of Plato, who believed that the world as we see it has an underlying reality (forms) that cannot be objectively measured or determined. ), The Handbook of Information Systems Research (pp. 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. In fact, IT is really about innovation. Blinding Us to the Obvious? With a large enough sample size, a statistically significant rejection of a null hypothesis can be highly probable even if an underlying discrepancy in the examined statistics (e.g., the differences in means) is substantively trivial. One other caveat is that the alpha protection level can vary. Davis, F. D. (1989). Public Opinion Quarterly, 68(1), 84-101. Statistical compendia, movie film, printed literature, audio tapes, and computer files are also widely used sources. P Values and Statistical Practice. Bayesian Data Analysis (3rd ed.). Several viewpoints pertaining to this debate are available (Aguirre-Urreta & Marakas, 2012; Centefelli & Bassellier, 2009; Diamantopoulos, 2001; Diamantopoulos & Siguaw, 2006; Diamantopoulos & Winklhofer, 2001; Kim et al., 2010; Petter et al., 2007). The ASAs Statement on P-values: Context, Process, and Purpose. Scholars argue that we are living in a technological age. This logic is, evidently, flawed. 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. Exploratory surveys may also be used to uncover and present new opportunities and dimensions about a population of interest. For example, the computer sciences also have an extensive tradition in discussing QtPR notions, such as threats to validity. QtPR is also not qualitative positivist research (QlPR) nor qualitative interpretive research. There are different types of validity that are important to identify. Chin, W. W. (2001). Data computing equipment makes it possible to process and analyze data quickly, even with large sample sizes. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). One common working definition that is often used in QtPR research refers to theory as saying what is, how, why, when, where, and what will be. 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). A survey is a means of gathering information about the characteristics, actions, perceptions, attitudes, or opinions of a large group of units of observations (such as individuals, groups or organizations), referred to as a population. Role of ICT in Research. 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, 2010a). The variables that are chosen as operationalizations to measure a theoretical construct must share its meaning (in all its complexity if needed). That is, in social reality, no two variables are ever perfectly unrelated (Meehl, 1967). Likewise, QtPR methods differ in the extent to which randomization is employed during data collection (e.g., during sampling or manipulations). Type I and Type II errors are classic violations of statistical conclusion validity (Garcia-Prez, 2012; Shadish et al., 2001). 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. 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. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. Organizational Research Methods, 13(4), 620-643. Quantitative studies are often fast, focused, scientific and relatable.4. Alpha levels in medicine are generally lower (and the beta level set higher) since the implications of Type I or Type II errors can be severe given that we are talking about human health. Crossover Designs in Software Engineering Experiments: Benefits and Perils. Our knowledge about research starts from here because it will lead us to the path of changing the world. They may well be. Quantitative research seeks to establish knowledge through the use of numbers and measurement. Distinguishing between the logical basics of the theory and its empirical, testable, predictions. Communications of the Association for Information Systems, 16(45), 880-894. The Difference Between Significant and Not Significant is not Itself Statistically Significant. Establishing reliability and validity of measures and measurement is a demanding and resource-intensive task. Sira Vegas and colleagues (Vegas et al., 2016) discuss advantages and disadvantages between a wide range of experiment designs, such as independent measures, repeated measures, crossover, matched-pairs, and different mixed designs. In both lab and field experiments, the experimental design can vary (see Figures 6 and 7). These proposals essentially suggest retaining p-values. Moving from the left (theory) to the middle (instrumentation), the first issue is that of shared meaning. King, W. R., & He, J. American Psychological Association. PLS-SEM: Indeed a Silver Bullet. It is not about fitting theory to observations. A weighting that reflects the correlation between the original variables and derived factors. 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). In contrast, according to Popper, is Freuds theory of psychoanalysis which can never be disproven because the theory is sufficiently imprecise to allow for convenient explanations and the addition of ad hoc hypotheses to explain observations that contradict the theory. As the name suggests, quantitative methods tend to specialize in quantities, in the sense that numbers are used to represent values and levels of measured variables that are themselves intended to approximate theoretical constructs. In other words, many of the items may not be highly interchangeable, highly correlated, reflective items (Jarvis et al., 2003), but this will not be obvious to researchers unless they examine the impact of removing items one-by-one from the construct. Kim, G., Shin, B., & Grover, V. (2010). Stationarity means that the mean and variance remain the same throughout the range of the series. This combination of should, could and must not do forms a balanced checklist that can help IS researchers throughout all stages of the research cycle to protect themselves against cognitive biases (e.g., by preregistering protocols or hypotheses), improve statistical mastery where possible (e.g., through consulting independent methodological advice), and become modest, humble, contextualized, and transparent (Wasserstein et al., 2019) wherever possible (e.g., by following open science reporting guidelines and cross-checking terminology and argumentation). ANOVA in Complex Experimental Designs. What is the importance of quantitative research in communication? Furthermore, even after being tested, a scientific theory is never verified because it can never be shown to be true, as some future observation may yet contradict it. Often, such tests can be performed through structural equation modelling or moderated mediation models. Rossiter, J. R. (2011). Most businesses rely on information and communications technology (ICT) to grow and improve their operations. The purpose of quantitative research is to attain greater knowledge and understanding of the social world. 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. Science, 352(6290), 1147. Likely this is not the intention. Human Relations, 61(8), 1139-1160. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). 2020). Communications of the Association for Information Systems, 4(7), 1-77. (1980), Causal Methods in Marketing. Vessey, I., Ramesh, V., & Glass, R. L. (2002). Journal of Socio-Economics, 33(5), 587-606. The other end of the uncertainty continuum can be envisioned as a turbulent marketplace where risk was high and economic conditions were volatile. Journal of the Academy of Marketing Science, 43(1), 115-135. The researcher analyses the data with the help of statistics. Manipulation validity is used in experiments to assess whether an experimental group (but not the control group) is faithfully manipulated and we can thus reasonably trust that any observed group differences are in fact attributable to the experimental manipulation. Diamantopoulos, Adamantios and Heidi M. Winklhofer, Index Construction with Formative Indicators: An Alternative to Scale Development, Journal of Marketing Research, 38, 2, (2001), 269-277. Incorporating Formative Measures into Covariance-Based Structural Equation Models. Statistical Methods for Meta-Analysis. "Nice examples of researcher webpages. 443-507). European Journal of Epidemiology, 31(4), 337-350. We note that at other times, we have discussed ecological validity as a form of external validity (Im & Straub, 2015). 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). All data are examined ex-post-facto by the researcher (Jenkins, 1985). 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. Social scientists, including communication researchers, use quantitative research to observe phenomena or occurrences that affect individuals. The growth and the development of technology are playing an important role in higher education. Other researchers might feel that you did not draw well from all of the possible measures of the User Information Satisfaction construct. Bollen, K. A. If researchers fail to ensure shared meaning between their socially constructed theoretical constructs and their operationalizations through measures they define, an inherent limit will be placed on their ability to measure empirically the constructs about which they theorized. Survey Research Methods. Masson, M. E. (2011). Importance of quantitative research. As a caveat, note that many researchers prefer the use of personal pronouns in their writings to emphasize the fact that they are interpreting data through their own personal lenses and that conclusions may not be generalizable. quantitative or qualitative methods is barren, and that the fit-for-purpose principle should be the central issue in methodological design. Statistically, the endogeneity problem occurs when model variables are highly correlated with error terms. Information sharing - How quickly & easily information can be shared across the globe. All types of observations one can make as part of an empirical study inevitably carry subjective bias because we can only observe phenomena in the context of our own history, knowledge, presuppositions, and interpretations at that time. 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). This example shows how reliability ensures consistency but not necessarily accuracy of measurement. Information Systems Research, 2(3), 192-222. Other endogeneity tests of note include the Durbin-Wu-Hausman (DWH) test and various alternative tests commonly carried out in econometric studies (Davidson and MacKinnon, 1993). What Is This Thing Called Science? Needless to say, this brief discussion only introduces three aspects to the role of randomization. Its primary disadvantage is often a lack of ecological validity because the desire to isolate and control variables typically comes at the expense of realism of the setting. A repository of theories that have been used in information systems and many other social science theories can be found at: https://guides.lib.byu.edu/c.php?g=216417&p=1686139. Researchers using field studies typically do not manipulate independent variables or control the influence of confounding variables (Boudreau et al., 2001). Appropriate measurement is, very simply, the most important thing that a quantitative researcher must do to ensure that the results of a study can be trusted. This step concerns the. There are many other types of quantitative research that we only gloss over here, and there are many alternative ways to analyze quantitative data beyond the approaches discussed here. Conducting research is necessary for many reasons, like building knowledge, learning new skills, innovating in business, understanding issues, and improving human health. Experimentation in Software Engineering: An Introduction. Sage. Elsevier. ), Criticism and the Growth of Knowledge (pp. Repeating this stage is often important and required because when, for example, measurement items are removed, the entire set of measurement item changes, the result of the overall assessment may change, as well as the statistical properties of individual measurement items remaining in the set. This website does not fully support Internet Explorer. Three Roles for Statistical Significance and the Validity Frontier in Theory Testing. DeVellis, R. F., & Thorpe, C. T. (2021). Simply put, QtPR focus on how you can do research with an emphasis on quantitative data collected as scientific evidence. Variables are not manipulated in this type of research and researchers do not use the law of probability. (2006). the term "technology" is an important issue in many fields including education. American Council on Education. 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. It is important to note here that correlation does not imply causation. (2007). Note, however, that a mis-calibrated scale could still give consistent (but inaccurate) results. 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. The issue is not whether the delay times are representative of the experience of many people. Regarding Type I errors, researchers are typically reporting p-values that are compared against an alpha protection level. Centefelli, R. T., & Bassellier, G. (2009). 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. Field studies are non-experimental inquiries occurring in natural systems. When we compare two means(or in other tests standard deviations or ratios etc. Sentences can also be transformed from active voice that utilizes the personal pronoun in many other ways. Federation for American Immigration Reform. For example, each participant would first evaluate user-interface-design one, then the second user-interface-design, and then the third. Mark Smith KTH School of ICT 2 Quantitative Research Methods Quantitative methods are those that deal with measurable data. A data analysis technique used to identify how a current observation is estimated by previous observations, or to predict future observations based on that pattern. In attempting to falsify the theory or to collect evidence in support of that theory, operationalizations in the form of measures (individual variables or statement variables) are needed and data needs to be collected from empirical referents (phenomena in the real world that the measure supposedly refers to). The omega test has been made available in recent versions of SPSS; it is also available in other statistical software packages. Similarly, 1-p is not the probability of replicating an effect (Cohen, 1994). The experimental hypothesis was that the work group with better lighting would be more productive. As suggested in Figure 1, at the heart of QtPR in this approach to theory-evaluation is the concept of deduction. MIS Quarterly, 36(1), iii-xiv. Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2). Problems with construct validity occur in three major ways. 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. Multivariate Data Analysis (7th ed.). 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. Schwab, A., Abrahamson, E., Starbuck, W. H., & Fidler, F. (2011). A Comparison of Web and Mail Survey Response Rates. Furthermore, it is almost always possible to choose and select data that will support almost any theory if the researcher just looks for confirming examples. Research involving survey instruments in general can be used for at least three purposes, these being exploration, description, or explanation. Psychological Bulletin, 52(4), 281-302. Frontiers in Psychology, 3(325), 1-11. 3. A seminal book on experimental research has been written by William Shadish, Thomas Cook, and Donald Campbell (Shadish et al., 2001). Goodwin, L. D. (2001). Journal of Marketing Research, 16(1), 64-73. the estimated effect size, whereas invalid measurement means youre not measuring what you wanted to measure. B., & Gal, D. (2017). 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). Cook, T. D. and D. T. Campbell (1979). (1935). A Tutorial on a Practical Bayesian Alternative to Null-Hypothesis Significance Testing. Typically, researchers use statistical, correlational logic, that is, they attempt to establish empirically that items that are meant to measure the same constructs have similar scores (convergent validity) whilst also being dissimilar to scores of measures that are meant to measure other constructs (discriminant validity) This is usually done by comparing item correlations and looking for high correlations between items of one construct and low correlations between those items and items associated with other constructs. In effect, researchers often need to make the assumption that the books, as audited, are accurate reflections of the firms financial health. Most likely, researchers will receive different answers from different persons (and perhaps even different answers from the same person if asked repeatedly). The content domain of a construct should formally specify the nature of the construct, including the conceptual domain to which the focal construct belongs and the entity to which it applies. 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). The Effect of Statistical Training on the Evaluation of Evidence. Intermediaries may have decided on their own not to pull all the data the researcher requested, but only a subset. (2000). What is to be included in revenues, for example, is impacted by decisions about whether booked revenues can or should be coded as current period revenues. To the issue of accuracy their own not to pull all the data for this research. Starts from here because it will lead us to the issue of shared meaning others. King, W. R., Cook, T. D., & Sarstedt, M. 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