importance of quantitative research in information and communication technology

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). 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. The emphasis in social science empiricism is on a statistical understanding of phenomena since, it is believed, we cannot perfectly predict behaviors or events. 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. Malignant Side Effects of Null-hypothesis Significance Testing. Yin, R. K. (2009). Garcia-Prez, M. A. Investigate current theories or trends surrounding the problem or issue. Historically, QtPR scholars in IS research often relied on methodologies for measurement instrument development that build on the work by Churchill in the field of marketing (Churchill, 1979). You cannot trust or contend that you have internal validity or statistical conclusion validity. The conceptual labeling of this construct is too broad to easily convey its meaning. 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. Quantitative research has the goal of gaining a better understanding of the social world. The posterior can also be used for making predictions about future events. Churchill Jr., G. A. Unfortunately, though, based on observations of hundreds of educational technology projects over the past decade, it is pretty clear to me that, in too many cases, investments in educational technologies remain a largely faith-based initiative in many places around the world. You can scroll down or else simply click above on the shortcuts to the sections that you wish to explore next. Jenkins, A. M. (1985). Likely not that there are either environmental factors or not. It is also referred to as the maximum likelihood criterion or U statistic (Hair et al., 2010). 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). But even more so, in an world of big data, p-value testing alone and in a traditional sense is becoming less meaningful because large samples can rule out even the small likelihood of either Type I or Type II errors (Guo et al., 2014). (2010). As this discussion already illustrates, it is important to realize that applying NHST is difficult. In physical and anthropological sciences or other distinct fields, quantitative research is methodical experimental research of noticeable events via analytical, numerical, or computational methods. The ASAs Statement on P-values: Context, Process, and Purpose. Bagozzi, R.P. This form of validity is discussed in greater detail, including stats for assessing it, in Straub, Boudreau, and Gefen (2004). You can contact the co-editors at: straubdetmar@gmail.com, gefend@drexel.edu, and jan.christof.recker@uni-hamburg.de. Allyn & Bacon. A wonderful introduction to behavioral experimentation is Lauren Slaters book Opening Skinners Box: Great Psychological Experiments of the Twentieth Century (Slater, 2005). If the DWH test indicates that there may be endogeneity, then the researchers can use what are called instrumental variables to see if there are indeed missing variables in the model. If they are randomly assigned, then there is a low probability that the effect is caused by any factors other than the treatment. So, essentially, we are testing whether our obtained data fits previously established causal models of the phenomenon including prior suggested classifications of constructs (e.g., as independent, dependent, mediating, or moderating). 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. 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. On the other hand, if no effect is found, then the researcher is inferring that there is no need to change current practices. Lin, M., Lucas Jr., H. C., & Shmueli, G. (2013). If the measures are not valid and reliable, then we cannot trust that there is scientific value to the work. 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). Goodhue, D. L., Lewis, W., & Thompson, R. L. (2007). Emerging Varieties of Action Research: Introduction to the Special Issue. 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. 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). ), Research in Information Systems: A Handbook for Research Supervisors and Their Students (pp. The Design of Experiments. Applied Multivariate Statistics for the Social Sciences (4th ed.). 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. A typical way this is done is to divide the subjects into groups randomly where each group is treated differently so that the differences in these treatments result in differences in responses across these groups as hypothesize. Since the data is coming from the real world, the results can likely be generalized to other similar real-world settings. In P. P. Biemer, R. M. Groves, L. E. Lyberg, N. A. Mathiowetz, & S. Sudman (Eds. Marcoulides, G. A., & Saunders, C. (2006). econometrics) and numerical methods such as mathematical modeling. A weighting that reflects the correlation between the original variables and derived factors. The most common test is through Cronbachs (1951) alpha, however, this test is not without problems. Converting active voice [this is what it is called when the subject of the sentence highlights the actor(s)] to passive voice is a trivial exercise. Overall, modern social scientists favor theorizing models with expressed causal linkages and predictions of correlational signs. For example, experimental studies are based on the assumption that the sample was created through random sampling and is reasonably large. Or, experiments often make it easier for QtPR researchers to use a random sampling strategy in comparison to a field survey. These nuances impact how quantitative or qualitative researchers conceive and use data, they impact how researchers analyze that data, and they impact the argumentation and rhetorical style of the research (Sarker et al., 2018). Typically, QtPR starts with developing a theory that offers a hopefully insightful and novel conceptualization of some important real-world phenomena. The most commonly used methodologies are experiments, surveys, content analysis, and meta-analysis. Houghton Mifflin. Eddingtons eclipse observation was a make-or-break event for Einsteins theory. 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. Petter, S., Straub, D. W., & Rai, A. (2001) criteria for internal validity. One of the main reasons we were interested in maintaining this online resource is that we have already published a number of articles and books on the subject. Correspondence analysis is a recently developed interdependence technique that facilitates both dimensional reduction of object ratings (e.g., products, persons, etc.) When we compare two means(or in other tests standard deviations or ratios etc. 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. The p-value below .05 is there because when Mr. Pearson (of the Pearson correlation) was asked what he thought an appropriate threshold should be, and he said one in twenty would be reasonable. Providing a strong conceptual orientation to techniques and procedures that range from the "moderately basic" to . Methods of Psychological Research, 7(1), 1-20. The Effect of Statistical Training on the Evaluation of Evidence. MIS Quarterly, 35(2), 335-358. This worldview is generally called positivism. 2021): Whereas seeking to falsify theories is the idealistic and historical norm, in practice many scholars in IS and other social sciences are, in practice, seeking confirmation of their carefully argued theoretical models (Gray & Cooper, 2010; Burton-Jones et al., 2017). Recker, J., & Rosemann, M. (2010). The term research instrument is neutral and does not imply a methodology. LISREL 8: Users Reference Guide. Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS With Product Indicators. Baruch, Y., & Holtom, B. C. (2008). This matrix is one of many methods that can be used to evaluate construct validity by demonstrating both convergent and discriminant validity. Gefen, D., Straub, D. W., & Boudreau, M.-C. (2000). Sage. Lindman, H. R. (1974). 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. 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. The simplest distinction between the two is that quantitative research focuses on numbers, and qualitative research focuses on text, most importantly text that captures records of what people have said, done, believed, or experienced about a particular phenomenon, topic, or event. Communications of the Association for Information Systems, 16(45), 880-894. (2001). 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.". 130 Information Technology Research Topics And Quick Writing Prompts. While differences exist in some aspects, the general manner of interpretation is quite similar to linear regression (Hair et al., 2010). Science, Technology, Engineering, . QtPR can be used both to generate new theory as well as to evaluate theory proposed elsewhere. Starting at the Beginning: An Introduction to Coefficient Alpha and Internal Consistency. The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? One caveat in this case might be that the assignment of treatments in field experiments is often by branch, office, or division and there may be some systematic bias in choosing these sample frames in that it is not random assignment. The aim of this study was to determine the effect of dynamic software on prospective mathematics teachers' perception levels regarding information and communication technology (ICT). Springer. In interpreting what the p-value means, it is therefore important to differentiate between the mathematical expression of the formula and its philosophical application. This is necessary because if there is a trend in the series then the model cannot be stationary. It is used to describe the current status or circumstance of the factor being studied. This methodology is primarily concerned with the examination of historical documents. Accounting principles try to control this, but, as cases like Enron demonstrate, it is possible for reported revenues or earnings to be manipulated. Sources of data are of less concern in identifying an approach as being QtPR than the fact that numbers about empirical observations lie at the core of the scientific evidence assembled. Experimentation in Software Engineering: An Introduction. It separates the procedure into four main stages and describes the different tasks to be performed (grey rounded boxes), related inputs and outputs (white rectangles), and the relevant literature or sources of empirical data required to carry out the tasks (dark grey rectangles). Scandinavian Journal of Information Systems, 22(2), 3-30. There is not enough space here to cover the varieties or intricacies of different quantitative data analysis strategies. The easiest way to show this, perhaps, is through an example. Introductions to their ideas and those of relevant others are provided by philosophy of science textbooks (e.g., Chalmers, 1999; Godfrey-Smith, 2003). Constructs are socially constructed. Data Collection Methods and Measurement Error: An Overview. Typically, the theory behind survey research involves some elements of cause and effect in that not only assumptions are made about relationships between variables but also about the directionality of these relationships. The fact of the matter is that the universe of all items is quite unknown and so we are groping in the dark to capture the best measures. (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. American Council on Education. (1985). 1 Quantitative research produces objective data that can be clearly communicated through statistics and numbers. Equity issues. (2001) distinguish three factors of internal validity, these being (1) temporal precedence of IVs before DVs; (2) covariation; and (3) the ability to show the predictability of the current model variables over other, missing variables (ruling out rival hypotheses). They should create . Quantitative Research in Communication is ideal for courses in Quantitative Methods in Communication, Statistical Methods in Communication, Advanced Research Methods (undergraduate), and Introduction to Research Methods (Graduate) in departments of communication, educational psychology, psychology, and mass communication. Philosophically what we are addressing in these statistical tests is whether the difference that we see in the statistics of interest, such as the means, is large enough in the sample or samples that we feel confident in saying that there probably is a difference also in the population or populations that the sample or samples came from. Alternative proposals essentially focus on abandoning the notion that generalizing to the population is the key concern in hypothesis testing (Guo et al., 2014; Kline, 2013) and instead moving from generalizability to explanatory power, for example, by relying on correlations to determine what effect sizes are reasonable in different research settings. Chicago, Rand McNally. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Aldine Publishing Company. Journal of Management Information Systems, 19(2), 129-174. The emphasis in sentences using the personal pronouns is on the researcher and not the research itself. The purpose of quantitative analysis is to improve and apply numerical principles, methods, and theories about . Communication. Elsevier. Ideally, when developing a study, researchers should review their goals as well as the claims they hope to make before deciding whether the quantitative method is the best approach. Therefore, a scientific theory is by necessity a risky endeavor, i.e., it may be thrown out if not supported by the data. Sampling Techniques (3rd ed.). It results in the captured patterns of respondents to the stimulus presented, a topic on which opinions vary. There is a wealth of literature available to dig deeper into the role, and forms, of randomization (e.g., Cochran, 1977; Trochim et al., 2016; Shadish et al., 2001). Several threats are associated with the use of NHST in QtPR. The term research instrument can be preferable to specific names such as survey instruments in many situations. Quantitative Research. That being said, constructs are much less clear in what they represent when researchers think of them as entity-relationship (ER) models. Fisher, R. A. McNutt, M. (2016). Goodhue, D. L. (1998). While modus tollens is logically correct, problems in its application can still arise. A Paradigm for Developing Better Measures of Marketing Constructs. 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. Researchers use quantitative methods to observe situations or events that affect people.1Quantitative research produces objective data that can be clearly communicated through statistics and numbers. Of course, in reality, measurement is never perfect and is always based on theory. Orne, M. T. (1962). first of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and Einsteins Theory of Relativity is a prime example, according to Popper, of a scientific theory. (2014). Bayesian Data Analysis (3rd ed.). Conducting research is necessary for many reasons, like building knowledge, learning new skills, innovating in business, understanding issues, and improving human health. The most popular SEM methods include LISREL (Jreskog & Srbom, 2001) and equivalent software packages such as AMOS and Mplus, on the one hand, and Partial Least Squares (PLS) modeling (Chin, 2001; Hair et al., 2013), on the other hand. Cohen, J. Research in Information Systems: An Empirical Study of Diversity in the Discipline and Its Journals. 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). MIS Quarterly, 13(2), 147-169. Reasonably large sampling and is reasonably large perfect and is reasonably large 1 ),.... Of this construct is too broad to easily convey its meaning is through An.! 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