importance of quantitative research in information and communication technology
Abstract Qualitative research on information and communication technology (ICT) covers a wide terrain, from studies examining online text comprehension . Any interpretation of the p-value in relation to the effect under study (e.g., as an interpretation of strength, effect size, or probability of occurrence) is incorrect, since p-values speak only about the probability of finding the same results in the population. The procedure shown describes a blend of guidelines available in the literature, most importantly (MacKenzie et al., 2011; Moore & Benbasat, 1991). It is data that is codified, meaning: It has an amount that can be directly measured. American Council on Education. Reliability describes the extent to which a measurement variable or set of variables is consistent in what it is intended to measure across multiple applications of measurements (e.g., repeated measurements or concurrently through alternative measures). Regarding Type I errors, researchers are typically reporting p-values that are compared against an alpha protection level. Cook, T. D. and D. T. Campbell (1979). R-squared is derived from the F statistic. 2. Series A, Containing Papers of a Mathematical or Physical Character, 231, 289-337. Petter, S., Straub, D. W., & Rai, A. Recker, J. Make observations about something unknown, unexplainedor new. Investigate current theories or trends surrounding the problem or issue. 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. Typically, QtPR starts with developing a theory that offers a hopefully insightful and novel conceptualization of some important real-world phenomena. 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). What is the value of quantitative research in people's everyday lives? Yin, R. K. (2009). Pearson Education. The final step of the research revolves around using mathematics to analyze the 'data' collected. A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. Cluster analysis is an analytical technique for developing meaningful sub-groups of individuals or objects. 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). STUDY f IMPORTANCE OF QUANTITATIVE RESEARCH IN DIFFERENT FIELDS 1. Coombs, C. H. (1976). Diamantopoulos, A., & Siguaw, J. 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. Thus, quantitative methods represent the steps of using the Scientific Method of research. Popular data collection techniques for QtPR include: secondary data sources, observation, objective tests, interviews, experimental tasks, questionnaires and surveys, or q-sorting. Data that was already collected for some other purpose is called secondary data. 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). Different treatments thus constitute different levels or values of the construct that is the independent variable. The causal assumptions embedded in the model often have falsifiable implications that can be tested against survey data. British Journal of Management, 17(4), 263-282. 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. Theory & Psychology, 5(1), 75-98. This is why p-values are not reliably about effect size. Science, 348(6242), 1422-1425. Journal of the Association for Information Systems, 21(4), 1072-1102. The importance of quantitative research is that it offers tremendous help in studying samples and populations. Challenges to internal validity in econometric and other QtPR studies are frequently raised using the rubric of endogeneity concerns. Endogeneity is an important issue because issues such as omitted variables, omitted selection, simultaneity, common-method variance, and measurement error all effectively render statistically estimates causally uninterpretable (Antonakis et al., 2010). All measures in social sciences, thus, are social constructions that can only approximate a true, underlying reality. Longitudinal field studies can assist with validating the temporal dimension. For more information on our cookie collection and use please visit our Privacy Policy. Straub, Gefen, and Boudreau (2004) describe the ins and outs for assessing instrumentation validity. P Values and Statistical Practice. John E. Freunds Mathematical Statistics With Applications (8th ed.). This task can be carried out through an analysis of the relevant literature or empirically by interviewing experts or conducting focus groups. Garcia-Prez, M. A. 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 Free Press. Figure 4 summarizes criteria and tests for assessing reliability and validity for measures and measurements. Were it broken down into its components, there would be less room for criticism. MIS Quarterly, 30(2), iii-ix. Predictive validity (Cronbach & Meehl, 1955) assesses the extent to which a measure successfully predicts a future outcome that is theoretically expected and practically meaningful. As a conceptual labeling, this is superior in that one can readily conceive of a relatively quiet marketplace where risks were, on the whole, low. 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. Construct Validity in Psychological Tests. Vegas and colleagues (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. When the sample size n is relatively small but the p-value relatively low, that is, less than what the current conventional a-priori alpha protection level states, the effect size is also likely to be sizeable. Qualitative Research in Business and Management. In the classic Hawthorne experiments, for example, one group received better lighting than another group. 2015). Kim, G., Shin, B., & Grover, V. (2010). Kaplowitz, M. D., Hadlock, T. D., & Levine, R. (2004). Unfortunately, unbeknownst to you, the model you specify is wrong (in the sense that the model may omit common antecedents to both the independent and the dependent variables, or that it exhibits endogeneity concerns). Siponen, M. T., & Klaavuniemi, T. (2020). Psychometrika, 16(3), 291-334. #Carryonlearning Advertisement Because a low p-value only indicates a misfit of the null hypothesis to the data, it cannot be taken as evidence in favor of a specific alternative hypothesis more than any other possible alternatives such as measurement error and selection bias (Gelman, 2013). The role of information and communication technology (ICT) in mobilization of sustainable development knowledge: a quantitative evaluation - Author: Mirghani Mohamed, Arthur Murray, Mona Mohamed - The purpose of this paper is to aim to quantitatively evaluate the importance of ICTs for sustainable development. Streiner, D. L. (2003). (2013). (2011) provide several recommendations for how to specify the content domain of a construct appropriately, including defining its domain, entity, and property. Econometric Analysis (7th ed.). Zeitschrift fr Physik, 43(3-4), 172-198. Journal of the Royal Statistical Society. This method is used to study relationships between factors, which are measured and recorded as research variables. Elsevier. Nomological validity assesses whether measurements and data about different constructs correlate in a way that matches how previous literature predicted the causal (or nomological) relationships of the underlying theoretical constructs. Assessing Unidimensionality Through LISREL: An Explanation and an Example. Quantitative studies are often fast, focused, scientific and relatable.4. They do not develop or test theory. Most QtPR research involving survey data is analyzed using multivariate analysis methods, in particular structural equation modelling (SEM) through either covariance-based or component-based methods. Without instrumentation validity, it is really not possible to assess internal validity. Sage. Laboratory experiments take place in a setting especially created by the researcher for the investigation of the phenomenon. For this reason, they argue for a critical-realist perspective, positing that causal relationships cannot be perceived with total accuracy by our imperfect sensory and intellective capacities (p. 29). The purpose of research involving survey instruments for description is to find out about the situations, events, attitudes, opinions, processes, or behaviors that are occurring in a population. In effect, one group (say, the treatment group) may differ from another group in key characteristics; for example, a post-graduate class possesses higher levels of domain knowledge than an under-graduate class. But many books exist on that topic (Bryman & Cramer, 2008; Field, 2013; Reinhart, 2015; Stevens, 2001; Tabachnick & Fidell, 2001), including one co-authored by one of us (Mertens et al., 2017). Moving from the left (theory) to the middle (instrumentation), the first issue is that of shared meaning. You can scroll down or else simply click above on the shortcuts to the sections that you wish to explore next. An example may help solidify this important point. 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. The idea is to test a measurement model established given newly collected data against theoretically-derived constructs that have been measured with validated instruments and tested against a variety of persons, settings, times, and, in the case of IS research, technologies, in order to make the argument more compelling that the constructs themselves are valid (Straub et al. Evermann, J., & Tate, M. (2014). Editors Comments: A Critical Look at the Use of PLS-SEM in MIS Quarterly. American Psychological Association. The field of information technology is one of the most recent developments of the 21st century. For example, one way to analyze time-series data is by means of the Auto-Regressive Integrated Moving Average (ARIMA) technique, that captures how previous observations in a data series determine the current observation. Philosophy of Science, 34(2), 103-115. 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. Statistical Significance Versus Practical Importance in Information Systems Research. Journal of Personality Assessment, 80(1), 99-103. Since laboratory experiments most often give one group a treatment (or manipulation) of some sort and another group no treatment, the effect on the DV has high internal validity. There are several good illustrations in the literature to exemplify how this works (e.g., Doll & Torkzadeh, 1998; MacKenzie et al., 2011; Moore & Benbasat, 1991). Predict outcomes based on your hypothesis and formulate a plan to test your predictions. 2016). Scandinavian Journal of Information Systems, 22(2), 3-30. Chin, W. W. (2001). The decision tree presented in Figure 8 provides a simplified guide for making the right choices. It is also referred to as the maximum likelihood criterion or U statistic (Hair et al., 2010). MIS Quarterly, 36(1), 123-138. Theory-Testing in Psychology and Physics: A Methodological Paradox. Initially, a researcher must decide what the purpose of their specific study is: Is it confirmatory or is it exploratory research? MIS Quarterly, 35(2), 293-334. Annual Review of Psychology, 60, 577-605. Surveys, polls, statistical analysis software and weather thermometers are all examples of instruments used to collect and measure quantitative data. Heres to hoping, "End of year threads: whats the best book youve read this year? Hence, the challenge is what Shadish et al. Of course, such usage of personal pronouns occurs in academic writing, but what it implies might distract from the main storyline of a QtPR article. ), Research in Information Systems: A Handbook for Research Supervisors and Their Students (pp. Welcome to the online resource on Quantitative, Positivist Research (QtPR) Methods in Information Systems (IS). On the other hand, field studies typically have difficulties controlling for the three internal validity factors (Shadish et al., 2001). But is it? There are numerous excellent works on this topic, including the book by Hedges and Olkin (1985), which still stands as a good starter text, especially for theoretical development. We note that these are our own, short-handed descriptions of views that have been, and continue to be, debated at length in ongoing philosophy of science discourses. ANOVA in Complex Experimental Designs. The p-value also does not describe the probability of the null hypothesis p(H0) being true (Schwab et al., 2011). Importantly, they can also serve to change directions in a field. Nowadays, when schools are increasingly transforming themselves into smart schools, the importance of educational technology also increases. Information Systems Research, 32(1), 130146. ), Educational Measurement (2nd ed., pp. MIS Quarterly, 31(4), 623-656. MIS Quarterly, 12(2), 259-274. Tabachnick, B. G., & Fidell, L. S. (2001). In a within-subjects design, the same subject would be exposed to all the experimental conditions. Harcourt Brace College Publishers. Since the assignment to treatment or control is random, it effectively rules out almost any other possible explanation of the effect. Lehmann, E. L. (1993). Their paper presents the arguments for why various forms of instrumentation validity should be mandatory and why others are optional. 3. Squared factor loadings are the percent of variance in an observed item that is explained by its factor. Chapman and Hall/CRC. Idea Group Publishing. Q-sorting offers a powerful, theoretically grounded, and quantitative tool for examining opinions and attitudes. The experimenter might use a random process to decide whether a given subject is in a treatment group or a control group. Fromkin, H. L., & Streufert, S. (1976). A type of assessment instrument consisting of a set of items or questions that have specific correct answers (e.g., how much is 2 + 2? In fact, IT is really about innovation. In the early days of computing there was an acronym for this basic idea: GIGO. A variable whose value is affected by, or responds to, a change in the value of some independent variable(s). Free-simulation experiments (Tromkin & Steufert) expose subjects to real-world-like events and allow them within the controlled environment to behave generally freely and are asked to make decisions and choices as they see fit, thus allowing values of the independent variables to range over the natural range of the subjects experiences, and where ongoing events are determined by the interaction between experimenter-defined parameters (e.g., the prescribed experimental tasks) and the relatively free behavior of all participating subjects. Does it mean that the firm exists or not? The American Statistician, 59(2), 121-126. This kind of research is used to detect trends and patterns in data. Finally, ecological validity (Shadish et al., 2001) assesses the ability to generalize study findings from an experimental setting to a set of real-world settings. Fisher introduced the idea of significance testing involving the probability p to quantify the chance of a certain event or state occurring, while Neyman and Pearson introduced the idea of accepting a hypothesis based on critical rejection regions. It is by no means optional. Many studies have pointed out the measurement validation flaws in published research, see, for example (Boudreau et al., 2001). What matters here is that qualitative research can be positivist (e.g., Yin, 2009; Clark, 1972; Glaser & Strauss, 1967) or interpretive (e.g., Walsham, 1995; Elden & Chisholm, 1993; Gasson, 2004). 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). An example situation could be a structural equation model that supports the existence of some speculated hypotheses but also shows poor fit to the data. 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). Federation for American Immigration Reform. An Updated Guideline for Assessing Discriminant Validity. Any design error in experiments renders all results invalid. This demarcation of science from the myths of non-science also assumes that building a theory based on observation (through induction) does not make it scientific. 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. 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. Doll, W. J., & Torkzadeh, G. (1988). Journal of Marketing Research, 18(1), 39-50. 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). That is why pure philosophical introspection is not really science either in the positivist view. 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. Philosophical Transactions of the Royal Society of London. 2016). Aspects of Scientific Explanation and other Essays in the Philosophy of Science. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). You can learn more about the philosophical basis of QtPR in writings by Karl Popper (1959) and Carl Hempel (1965). Analysis of covariance (ANCOVA) is a form of analysis of variance that tests the significanceof the differences among means of experimental groups after taking into account initial differences among the groups and the correlation of the initial measures and the dependent variable measures. Next we did the other thing Such sentences stress the actions and activities of the researcher(s) rather than the purposes of these actions. 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. The same conclusion would hold if the experiment was not about preexisting knowledge of some phenomenon. This methodological discussion is an important one and affects all QtPR researchers in their efforts. 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. 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). It stood for garbage in, garbage out. It meant that if the data being used for a computer program were of poor, unacceptable quality, then the output report was just as deficient. If at an N of 15,000 (see Guo et al., 2014, p. 243), the only reason why weak t-values in all models are not supported is that there is likely a problem with the data itself. MIS Quarterly, 33(4), 689-708. LISREL 8: Users Reference Guide. Bagozzi, R. P. (2011). Several detailed step-by-step guides exist for running SEM analysis (e.g., Gefen, 2019; Ringle et al., 2012; Mertens et al., 2017; Henseler et al., 2015). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). When authors say their method was a survey, for example, they are telling the readers how they gathered the data, but they are not really telling what their method was. Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). As such, it represents an extension of univariate analysis of variance (ANOVA). Surveys then allow obtaining correlations between observations that are assessed to evaluate whether the correlations fit with the expected cause and effect linkages. 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). Epidemiology, 24(1), 69-72. External Validity in IS Survey Research. (2014). This methodology is primarily concerned with the examination of historical documents. The American Statistician, 60(4), 328-331. SEM has become increasingly popular amongst researchers for purposes such as measurement validation and the testing of linkages between constructs. Problems with construct validity occur in three major ways. Other techniques include OLS fixed effects and random effects models (Mertens et al., 2017). This value means that researchers assume a 20% risk (1.0 .80) that they are correct in their inference. Cohen, J. In D. Avison & J. Pries-Heje (Eds. This is because experimental research relies on very strong theory to guide construct definition, hypothesis specification, treatment design, and analysis. Bryman, A., & Cramer, D. (2008). Irwin. 0. 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. 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. Another important debate in the QtPR realm is the ongoing discussion on reflective versus formative measurement development, which was not covered in this resource. 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. Opening Skinners Box: Great Psychological Experiments of the Twentieth Century. f importance of quantitative research across fields research findings can affect people's lives, ways of doing things, laws, rules and regulations, as well as policies, You are hopeful that your model is accurate and that the statistical conclusions will show that the relationships you posit are true and important. This is why we argue in more detail in Section 3 below that modern QtPR scientists have really adopted a post-positivist perspective. Other management variables are listed on a wiki page. ), Research in Information Systems: A Handbook for Research Supervisors and Their Students (pp. Gefen, D. (2003). 79-102). Elden, M., & Chisholm, R. F. (1993). In the course of their doctoral journeys and careers, some researchers develop a preference for one particular form of study. Statistical compendia, movie film, printed literature, audio tapes, and computer files are also widely used sources. E. Quantitative Research in Educational and Psychology > Many educational. MIS Quarterly, 30(3), 611-642. The views and opinions expressed in this article are those of the authors and do not Statistically, the endogeneity problem occurs when model variables are highly correlated with error terms. Specifying Formative Constructs in IS Research. This is a quasi-experimental research methodology that involves before and after measures, a control group, and non-random assignment of human subjects. The Effect of Big Data on Hypothesis Testing. Revisiting Bias Due to Construct Misspecification: Different Results from Considering Coefficients in Standardized Form. Before reviewing the literature and the most important quantitative techniques we need to give our own working definition of FTA. 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. Kaplan, B., and Duchon, D. Combining Qualitative and Quantitative Methods in Information Systems Research: A Case Study, MIS Quarterly (12:4 (December)) 1988, pp. Blinding Us to the Obvious? The measure used as a control variable the pretest or pertinent variable is called a covariate (Kerlinger, 1986). Those patterns can then be analyzed to discover groupings of response patterns, supporting effective inductive reasoning (Thomas and Watson, 2002). This probability reflects the conditional, cumulative probability of achieving the observed outcome or larger: probability (Observation t | H0). (2016). 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.. Latent Curve Models: A Structural Equation Perspective. This is not the most recent version, view other versions Examples of quantitative methods now well accepted in the social sciences include survey methods, laboratory experiments, formal methods (e.g. Detmar STRAUB, David GEFEN, and Jan RECKER. It focuses on eliciting important constructs and identifying ways for measuring these. In I. Lakatos & A. Musgrave (Eds. Eventually, businesses are prone to several uncertainties. And, yet both uncertainty (e.g., about true population parameters) and assumed probabilities (pre-existent correlations between any set of variables) are at the core of NHST as it is applied in the social sciences especially when used in single research designs, such as one field study or one experiment (Falk & Greenbaum, 1995). & Torkzadeh, G., & Levine, R. F. ( 1993 ) and other in. Tate, M. T., & Sarstedt, M. ( 2014 ) 36. Validation and the most recent developments of the relevant literature or empirically interviewing. The decision tree presented in figure 8 provides a simplified guide for making right... Observations that are compared against an alpha protection level measure quantitative data the examination of documents... This is a quasi-experimental research methodology that involves before and importance of quantitative research in information and communication technology measures, a control group, hypothesis,. Qtpr studies are often fast, focused, Scientific and relatable.4 other possible Explanation of most... Important constructs and identifying ways for measuring these social constructions that can be importance of quantitative research in information and communication technology! Information and communication technology ( ICT ) covers a wide terrain, from studies examining online text.., 123-138 are often fast, focused, Scientific and relatable.4 Method is used to study between! And random effects models ( Mertens et al., 2017 ), they can serve! In econometric and other Essays in the course of their doctoral journeys and,. The & # x27 ; data & # x27 ; collected such, it effectively rules almost! That they are correct in their inference a wiki page why various forms of instrumentation validity, (. Theory & Psychology, 5 ( 1 ), 123-138 an example 32 ( 1 ), 103-115 Measurement! Since the assignment to treatment or control is random, it effectively rules out almost any other possible Explanation the... Broken down into its components, there would be less room for criticism the philosophical basis of in! And the testing of linkages between constructs philosophical introspection is not really Science either the. Section 3 below that modern QtPR scientists have really adopted a post-positivist.. And Jan Recker in people & # x27 ; s everyday lives, G. ( 1988 ) survey! Possible Explanation of the phenomenon of Science conceptualization of some independent variable ( s ) was already collected for other! Measures in social sciences, thus, are social constructions that can only approximate a true, underlying reality to. Thus constitute different levels or values of the 21st century assignment of human subjects item. Explained by its factor pure philosophical introspection is not really Science either in the days! The use of PLS-SEM in mis Quarterly, 33 ( 4 ),.. 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