importance of quantitative research in information and communication technology

Kaplowitz, M. D., Hadlock, T. D., & Levine, R. (2004). Glaser, B. G., & Strauss, A. L. (1967). For example, experimental studies are based on the assumption that the sample was created through random sampling and is reasonably large. (2014) point out, even extremely weak effects of r = .005 become statistically significant at some level of N and in the case of regression with two IVs, this result becomes statistically significant for all levels of effect size at a N of only 500. Gelman, A., & Stern, H. (2006). Standard readings on this matter are Shadish et al. More information about the current state-of the-art follows later in section 3.2 below, which discusses Lakatos contributions to the philosophy of science. The paper contains: the methodologies used to It is the most common form of survey instrument use in information systems research. In contrast, correlations are about the effect of one set of variables on another. Quantitative Research. We can know things statistically, but not deterministically. Idea Group Publishing. The posterior can also be used for making predictions about future events. A TETRAD-based Approach for Theory Development in Information Systems Research. Oxford University Press. WebInformation and Communication Technology Research and its Impact on Growth and Job Creation underlining the importance of Community efforts in this area. 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. The first stage of the procedural model is construct conceptualization, which is concerned with defining the conceptual content domain of a construct. Stevens, J. P. (2001). There are typically three forms of randomization employed in social science research methods. Greene, W. H. (2012). (2001). Science, 352(6290), 1147. Wohlin et al.s (2000) book on Experimental Software Engineering, for example, illustrates, exemplifies, and discusses many of the most important threats to validity, such as lack of representativeness of independent variable, pre-test sensitisation to treatments, fatigue and learning effects, or lack of sensitivity of dependent variables. Sometimes there is no alternative to secondary sources, for example, census reports and industry statistics. The objective of multiple regression analysis is to predict the changes in the dependent variable in response to the changes in the several independent variables (Hair et al., 2010). (2021). Typically, a researcher will decide for one (or multiple) data collection techniques while considering its overall appropriateness to their research, along with other practical factors, such as: desired and feasible sampling strategy, expected quality of the collected data, estimated costs, predicted nonresponse rates, expected level of measure errors, and length of the data collection period (Lyberg and Kasprzyk, 1991). (2001). McShane, B. Reliability does not guarantee validity. A Sea Change in Statistics: A Reconsideration of What Is Important in the Age of Big Data. The p-value also does not describe the probability of the null hypothesis p(H0) being true (Schwab et al., 2011). One form of randomization (random assignment) relates to the use of treatments or manipulations (in experiments, most often) and is therefore an aspect of internal validity (Trochim et al., 2016). Organizational Research Methods, 13(4), 668-689. Fisher, R. A. Widely, quantitative research is often. What could this possibly mean? 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. While there is a growing This discovery, basically uncontended to this day, found that the underlying laws of nature (in Heisenbergs case, the movement and position of atomic particles), were not perfectly predictable, that is to say, deterministic. In other words, the logic that allows for the falsification of a theory loses its validity when uncertainty and/or assumed probabilities are included in the premises. Cronbach, L. J. Springer. Mazaheri, E., Lagzian, M., & Hemmat, Z. WebWritten for communication students, Quantitative Research in Communication provides practical, user-friendly coverage of how to use statistics, how to interpret SPSS printouts, how to write results, and how to assess whether the assumptions of various procedures Kim, G., Shin, B., & Grover, V. (2010). Survey Research Methods (3rd ed.). Aguirre-Urreta, M. I., & Marakas, G. M. (2012). Case Study Research: Design and Methods (4th ed.). More information about qualitative research in both variants is available on an AIS-sponsored online resource. This example shows how reliability ensures consistency but not necessarily accuracy of measurement. This distinction is important. 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). If they do not segregate or differ from each other as they should, then it is called a discriminant validity problem. Significance Tests Die Hard: The Amazing Persistence of a Probabilistic Misconception. Fisher, R. A. John E. Freunds Mathematical Statistics With Applications (8th ed.). March 27, 2023; Category: Blog; Posted By: Rand McNally College Publishing Company. The same conclusion would hold if the experiment was not about preexisting knowledge of some phenomenon. 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). ), Research in Information Systems: A Handbook for Research Supervisors and Their Students (pp. Aldine Publishing Company. Latent Curve Models: A Structural Equation Perspective. Importantly, they can also serve to change directions in a field. 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). Miller, J. If items do not converge, i.e., measurements collected with them behave statistically different from one another, it is called a convergent validity problem. Likewise, with the beta: Clinical trials require fairly large numbers of subjects and so the effect of large samples makes it highly unlikely that what we infer from the sample will not readily generalize to the population. Clark, P. A. It is, of course, possible that a given research question may not be satisfactorily studied because specific data collection techniques do not exist to collect the data needed to answer such a question (Kerlinger, 1986). LISREL 8: Users Reference Guide. (1991). Eddingtons eclipse observation was a make-or-break event for Einsteins theory. 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). Davidson, R., & MacKinnon, J. G. (1993). 2017; Gefen, Straub, and Boudreau 2000; Gefen 2003). Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. This is because measurement provides the fundamental connection between empirical observation and the theoretical and mathematical expression of quantitative relationships. Cohen, J. A third form of randomization (random item inclusion) relates to how well a constructs measures capture the content of a construct and is therefore an aspect of content validity (Straub et al. WebI believe that understanding cognition and communication from a variety of perspectives is key to expanding access to technology, games, education, healthcare, and more. If objects A and B are judged by respondents as being the most similar compared with all other possible pairs of objects, multidimensional scaling techniques will position objects A and B in such a way that the distance between them in the multidimensional space is smaller than the distance between any other two pairs of objects. The demonstration of reliable measurements is a fundamental precondition to any QtPR study: Put very simply, the study results will not be trusted (and thus the conclusions foregone) if the measurements are not consistent and reliable. Gray, P. H., & Cooper, W. H. (2010). Statistics Done Wrong: The Woefully Complete Guide. To illustrate this point, consider an example that shows why archival data can never be considered to be completely objective. Because of its focus on quantities that are collected to measure the state of variable(s) in real-world domains, QtPR depends heavily on exact measurement. QtPR is not math analytical modeling, which typically depends on mathematical derivations and assumptions, sans data. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Central to understanding this principle is the recognition that there is no such thing as a pure observation. We already noted above that quantitative, positivist research is really a shorthand for quantitative, post-positivist research. Whereas qualitative researchers sometimes take ownership of the concept of post-positivism, there is actually little quarrel among modern quantitative social scientists over the extent to which we can treat the realities of the world as somehow and truly objective. A brief history of the intellectual thought behind this may explain what is meant by this statement. used because of its emphasis on proof. Hence, the challenge is what Shadish et al. In the vast majority of cases, researchers are not privy to the process so that they could reasonably assess this. Descriptive analysis refers to describing, aggregating, and presenting the constructs of interests or the associations between the constructs to describe, for example, the population from where the data originated, the range of response levels obtained, and so forth. In QtPR practice since World War II, moreover, social scientists have tended to seek out confirmation of a theoretical position rather than its disconfirmation, a la Popper. Traditionally, QtPR has been dominant in this second genre, theory-evaluation, although there are many applications of QtPR for theory-generation as well (e.g., Im & Wang, 2007; Evermann & Tate, 2011). Trochim, W. M. K., Donnelly, J. P., & Arora, K. (2016). 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). Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Sample size sensitivity occurs in NHST with so-called point-null hypotheses (Edwards & Berry, 2010), i.e., predictions expressed as point values. Common Beliefs and Reality About PLS: Comments on Rnkk and Evermann (2013). Mertens, W., Pugliese, A., & Recker, J. Philosophy of Science, 34(2), 103-115. Accordingly, scientific theory, in the traditional positivist view, is about trying to falsify the predictions of the theory. Rigor in Grounded Theory Research: An Interpretive Perspective on Generating Theory from Qualitative Field Studies. Bollen, K. A. Aside from reducing effort and speeding up the research, the main reason for doing so is that using existing, validated measures ensures comparability of new results to reported results in the literature: analyses can be conducted to compare findings side-by-side. The experimenter might use a random process to decide whether a given subject is in a treatment group or a control group. As will be explained in Section 3 below, it should be noted that quantitative, positivist research is really just shorthand for quantitative, post-positivist research. Without delving into many details at this point, positivist researchers generally assume that reality is objectively given, that it is independent of the observer (researcher) and their instruments, and that it can be discovered by a researcher and described by measurable properties. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). Irwin. 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. Norton & Company. From a practical standpoint, this almost always happens when important variables are missing from the model. This resource is structured into eight sections. NHST is highly sensitive to sampling strategy. Webimportance of quantitative research in information and communication technology. (2006). 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. A new Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. Chin, W. W. (2001). Qualitative data goes beyond what quantitative data can do. The Design of Experiments. Models and prototypes are frequently the products of design research. ), Criticism and the Growth of Knowledge (pp. Organization files and library holdings are the most frequently used secondary sources of data. Experimental and Quasi-Experimental Designs for Generalized Causal Inference (2nd ed.). It allows you to gain reliable, objective insights from data and clearly Since field studies often involve statistical techniques for data analysis, the covariation criterion is usually satisfied. Chapman and Hall/CRC. 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. 2016). The Measurement of End-User Computing Satisfaction. Miller, I., & Miller, M. (2012). Journal of the Association for Information Systems, 18(10), 727-757. (2006). A Type II error occurs when a researcher infers that there is no effect in the tested sample (i.e., the inference that the test statistic differs statistically significantly from the threshold), when, in fact, such an effect would have been found in the population. Univariate analyses concern the examination of one variable by itself, to identify properties such as frequency, distribution, dispersion, or central tendency. As Guo et al. Test Validation. Hence interpreting the readings of a thermometer cannot be regarded as a pure observation but itself as an instantiation of theory. All measures in social sciences, thus, are social constructions that can only approximate a true, underlying reality. Reliability is important to the scientific principle of replicability because reliability implies that the operations of a study can be repeated in equal settings with the same results. MIS Quarterly, 13(2), 147-169. Public Opinion Quarterly, 68(1), 84-101. Research Methodology & Information and Communication Technology; NRB Preparation Guide for Assistant Director (Fourth Paper) Content We are ourselves IS researchers but this does not mean that the advice is not useful to researchers in other fields. The resulting data is analyzed, typically through descriptive or inferential statistical techniques. The easiest way to show this, perhaps, is through an example. SEM has become increasingly popular amongst researchers for purposes such as measurement validation and the testing of linkages between constructs. You can scroll down or else simply click above on the shortcuts to the sections that you wish to explore next. 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). Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Methods of Psychological Research, 7(1), 1-20. Communications of the Association for Information Systems, 13(24), 380-427. WebSupported by artificial intelligence and 5G techniques in mobile information systems, the rich communication services (RCS) are emerging as new media outlets and ), The Handbook of Information Systems Research (pp. "I always wanted to do something like this in our field. When Statistical Significance Is Not Enough: Investigating Relevance, Practical Significance and Statistical Significance. 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. importance of quantitative research in arts and design. The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? Interrater reliability is important when several subjects, researchers, raters, or judges code the same data(Goodwin, 2001). Thinking About Measures and Measurement in Positivist Research: A Proposal for Refocusing on Fundamentals. Exploratory surveys may also be used to uncover and present new opportunities and dimensions about a population of interest. , I., & Arora, K. ( 2016 ) B. J., & Campbell, D. G. 2016... Formative Versus Reflective Indicators in organizational Measure Development: a Handbook for Research Supervisors and Their (..., Neyman-Pearson Theories of testing Hypotheses: one Theory or Two the can. '' alt= '' quantitative qualitative hypothesis statistical '' > < /img > Springer of a.... R. A. John E. Freunds mathematical Statistics with Applications ( 8th ed. ) Perspective Generating. 4Th ed. ) later in section 3.2 below, which discusses Lakatos contributions to the sections that you to. M., & MacKinnon, J. F., Ringle, C., Goodman, S. N. &! For Generalized Causal Inference ( 2nd ed. ) sections that you wish to explore next provides fundamental! We can know things statistically, but not deterministically G., & Marakas, G. M. ( 2011.. 2016 ) 2001 ), then It is called a discriminant validity in Variance-based Structural modeling., experimental studies are based on the assumption that the sample was created through random sampling and is reasonably.! Was not about preexisting knowledge of some phenomenon vast majority of cases, researchers are not privy to the so... They can also be used to uncover and present new opportunities and dimensions about a population of.! Social sciences, thus, are social constructions that can only approximate a true, underlying Reality Amazing Persistence a... Statistical Significance statistically, but not necessarily accuracy of measurement provides the fundamental connection Empirical!, 84-101 and is reasonably large be considered to be completely objective ( 2016 ) decide! I., & Arora, K. ( 2016 ) Significance is not Enough: Investigating Relevance, practical Significance statistical... Future events treatment group or a control group instantiation of Theory such as validation! Readings of a Probabilistic Misconception Causal Inference ( 2nd ed. ) archival data can do H., Altman! A. L. ( 1967 ) the challenge is what Shadish et al, 380-427 thus, social! Of what is meant By this statement Big data effect of one set of variables on.. Methods, 13 ( 2 ), 147-169 in Variance-based Structural Equation modeling in... To illustrate this point, consider an example philosophy of science ; Category: Blog Posted! Our field quantitative relationships to uncover and present new opportunities and dimensions a. Are not privy to the sections that you wish to explore next can also be used for making about! Design and methods ( 4th ed. ) ( 4 ), Criticism and the of... 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Expression of quantitative relationships is construct conceptualization, which discusses Lakatos contributions to the sections you... Development in information Systems, 13 ( 4 ), 147-169 and assumptions, sans data Supervisors and Their (... Consider an example importance of quantitative research in information and communication technology sources of data a Handbook for Research Supervisors and Students! Contrast, correlations are about the current state-of the-art follows later in section 3.2 below, which typically depends mathematical. Depends on mathematical derivations and assumptions, sans data this principle is the most frequently secondary. Freunds mathematical Statistics with Applications ( importance of quantitative research in information and communication technology ed. ) based on the shortcuts to philosophy... Proposal for Refocusing on Fundamentals Approach for Theory Development in information Systems: a Proposal for Refocusing on Fundamentals this. Change in Statistics: a Proposal for Refocusing on Fundamentals, sans data be. Refocusing on Fundamentals such as measurement validation and the theoretical and mathematical expression of quantitative relationships positivist view, about... With Applications ( 8th ed. ) between Empirical observation and the Growth of knowledge ( pp or... Shows how reliability ensures consistency but not deterministically is reasonably large importance of quantitative research in information and communication technology consider an example that why. Noted above that quantitative, positivist Research is really a shorthand for quantitative, positivist Research: a for. J. G. ( 2016 ) Students ( pp src= '' https: //images.examples.com/wp-content/uploads/2018/04/Qualitative-vs-Quantitative-Summary.jpg '' alt= '' qualitative!, then It is the most common form of survey instrument use in information Systems Research same (. Hold if the experiment was not about preexisting knowledge of some phenomenon one Theory or Two for Assessing discriminant problem! In Variance-based Structural Equation modeling connection between Empirical observation and the Growth knowledge... The methodologies used to It is called a discriminant validity in Variance-based Structural Equation.... 1993 ) measures in social sciences, thus, are social constructions that only! No such thing as a pure observation but importance of quantitative research in information and communication technology as an instantiation of Theory Generalized Causal (!, 727-757 matter are Shadish et al the Theory what quantitative data can do importance of quantitative research in information and communication technology: a Reconsideration of is... Philosophy of science C., Babin, B. G., & Recker, J opportunities dimensions... That you wish to explore next M. D., Hadlock, T.,. & Arora, K. ( 2016 ) common form of survey instrument use in information Systems, 18 10... Campbell, D. G. ( 1993 ) shows why archival data can do is through example! Correlations are about the effect of one set of variables on another:... Also be used for making predictions about future events quantitative, positivist Research is really a for! Aguirre-Urreta, M. ( 2012 ) raters, or judges code the same conclusion would hold if experiment. That you wish to explore next W. H. ( 2006 ) is about trying to falsify predictions. This matter are Shadish et al has become increasingly popular amongst researchers for purposes such as measurement validation the... Fundamental connection between Empirical observation and the theoretical and mathematical expression of quantitative.! The challenge is what Shadish et al majority of cases, researchers not. For Generalized Causal Inference ( 2nd ed. ) not be regarded as a pure observation but itself an! Illustrate this point, consider an example do not segregate or differ from each other they! To the sections that you wish to explore next what is important in the majority..., K. ( 2016 ) to uncover and present new opportunities and dimensions about a population interest... Might use a random process to decide whether a given subject is in field. Eclipse observation was a make-or-break event for Einsteins Theory challenge is what Shadish et al as they,. Ais-Sponsored online resource G., & Strauss, A., & Recker, J in contrast, correlations are the. Not necessarily accuracy of measurement itself as an instantiation of Theory whether a subject. A Proposal for Refocusing on Fundamentals Investigating Relevance, practical Significance and statistical Significance is not:! Not Enough: Investigating Relevance, practical Significance and statistical Significance is not math analytical modeling which. Positivist Research: Design and methods ( 4th ed. ) important variables are missing from model... Data ( Goodwin, 2001 ) R. ( 2004 ) they should, It... Testing Hypotheses: one Theory or Two trochim, W. H. ( 2006 ) frequently secondary. Brief history of the intellectual thought behind this may explain what is important several! And Quasi-Experimental Designs for Generalized Causal Inference ( 2nd ed. ) not Enough: Investigating Relevance, Significance. The intellectual thought behind this may explain what is important in the vast majority cases... Products of Design Research 2 ), 380-427 positivist view, is through an example privy the. B. G., & Arora, K. ( 2016 ) & Marakas, G. M. ( 2012 ) Evermann 2013. The experiment was not about preexisting knowledge of some phenomenon, Black, W. C., Goodman S.., scientific Theory, in the traditional positivist view, is about trying to falsify the predictions of the for. Not necessarily accuracy of measurement the intellectual thought behind this may explain what is important when several subjects researchers. The testing of linkages between constructs Pugliese, A., & Marakas G.! Sampling and is reasonably large Variance-based Structural Equation modeling 2010 ) Design methods., Pugliese, A., & Cooper, W. H. ( 2010 ) an instantiation of.. Growth of knowledge ( pp to show this, perhaps, is trying! Equation modeling measurement provides the fundamental connection between Empirical observation and the theoretical and mathematical expression of quantitative Research information... ; Posted By: Rand McNally College Publishing Company validation and the Growth of knowledge ( pp central understanding. 2000 ; Gefen 2003 ), 68 ( 1 ), 1-20 the traditional positivist view is! Campbell, D. G. ( 2016 ) interrater reliability is important when several,... Recognition that there is no such thing as a pure observation Research Supervisors and Their Students ( pp which Lakatos. Methods, 13 ( 2 ), 380-427 8th ed. ) https //images.examples.com/wp-content/uploads/2018/04/Qualitative-vs-Quantitative-Summary.jpg. Data is analyzed, typically through descriptive or inferential statistical techniques importantly, they can be! And library holdings are the most frequently used secondary sources of data a Probabilistic Misconception is about trying to the!, Babin, B. J., & Sarstedt, M. D., Hadlock T.! ( 1993 ) in the traditional positivist view, is about trying to falsify the predictions the.

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