importance of quantitative research in information and communication technology

An alternative to Cronbach alpha that does not assume tau-equivalence is the omega test (Hayes and Coutts, 2020). The most direct application is in new product or service development, allowing for the evaluation of the complex products while maintaining a realistic decision context for the respondent (Hair et al., 2010). Quantitative studies are focused. Statistical Conclusion Validity: Some Common Threats and Simple Remedies. It is a closed deterministic system in which all of the independent and dependent variables are known and included in the model. (2001) are referring to in their third criterion: How can we show we have reasonable internal validity and that there are not key variables missing from our models? Multinormal distribution occurs when also the polynomial expression aX1+bX2 itself has a normal distribution. Organizational Research Methods, 13(4), 668-689. To achieve this goal, companies and employees must use technology wisely. 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). 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. 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. This is the Falsification Principle and the core of positivism. Since the data is coming from the real world, the results can likely be generalized to other similar real-world settings. A Theory of Data. 221-238). Reliability does not guarantee validity. In Poppers falsification view, for example, one instance of disconfirmation disproves an entire theory, which is an extremely stringent standard. The autoregressive part of ARIMA regresses the current value of the series against its previous values. Social scientists, including communication researchers, use quantitative research to observe phenomena or occurrences that affect individuals. Low power thus means that a statistical test only has a small chance of detecting a true effect or that the results are likely to be distorted by random and systematic error. (1996). Written 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 have been met . Elden, M., & Chisholm, R. F. (1993). Hence interpreting the readings of a thermometer cannot be regarded as a pure observation but itself as an instantiation of theory. (1951). No Starch Press. Communications of the Association for Information Systems, 8(9), 141-156. Gefen, D., & Larsen, K. R. T. (2017). All measures in social sciences, thus, are social constructions that can only approximate a true, underlying reality. 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). Quantitative research methods were originally developed in the natural sciences to study natural phenomena. QtPR is a set of methods and techniques that allows IS researchers to answer research questions about the interaction of humans and digital information and communication technologies within the sociotechnical systems of which they are comprised. Validation in Information Systems Research: A State-of-the-Art Assessment. 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). In the latter case, the researcher is not looking to confirm any relationships specified prior to the analysis, but instead allows the method and the data to explore and then define the nature of the relationships as manifested in the data. Finally, there is a perennial debate in QtPR about null hypothesis significance testing (Branch, 2014; Cohen, 1994; Pernet, 2016; Schwab et al., 2011; Szucs & Ioannidis, 2017; Wasserstein & Lazar, 2016; Wasserstein et al., 2019). For example, using a survey instrument for data collection does not allow for the same type of control over independent variables as a lab or field experiment. 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. Rand McNally College Publishing Company. The posterior can also be used for making predictions about future events. In this context, loading refers to the correlation coefficient between each measurement item and its latent factor. Flourishing for a brief period in the early 1900s, logical positivism, which argued that all natural laws could be reduced to the mathematics of logic, was one culmination of a deterministic positivism, but these ideas came out of a long tradition of thinking of the world as an objective reality best described by philosophical determinism. The plotted density function of a normal probability distribution resembles the shape of a bell curve with many observations at the mean and a continuously decreasing number of observations as the distance from the mean increases. Meta-analyses are extremely useful to scholars in well-established research streams because they can highlight what is fairly well known in a stream, what appears not to be well supported, and what needs to be further explored. This stage also involves assessing these candidate items, which is often carried out through expert panels that need to sort, rate, or rank items in relation to one or more content domains of the constructs. Vessey, I., Ramesh, V., & Glass, R. L. (2002). This website does not fully support Internet Explorer. CT Bauer College of Business, University of Houston, USA, 15, 1-16. (2007). Quantitative research produces objective data that can be clearly communicated through statistics and numbers. Lawrence Erlbaum Associates. Action Research and Organizational Change. Series A, Containing Papers of a Mathematical or Physical Character, 231, 289-337. (Wikipedia.org). Qualitative Research on Information and Communication Technology. If items do not converge, i.e., measurements collected with them behave statistically different from one another, it is called a convergent validity problem. While differences exist in some aspects, the general manner of interpretation is quite similar to linear regression (Hair et al., 2010). Central to understanding this principle is the recognition that there is no such thing as a pure observation. The Effect of Statistical Training on the Evaluation of Evidence. It should be noted at this point that other, different approaches to data analysis are constantly emerging. The world is experiencing a digital revolution and the Philippines have the opportunity to play an enormous role in it. This task can be carried out through an analysis of the relevant literature or empirically by interviewing experts or conducting focus groups. 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). In the classic Hawthorne experiments, for example, one group received better lighting than another group. What is the importance of quantitative research in communication? Falk, R., & Greenbaum, C. W. (1995). The most important difference between such time-series data and cross-sectional data is that the added time dimension of time-series data means that such variables change across both units and time. 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. Sources of reliability problems often stem from a reliance on overly subjective observations and data collections. An introduction is provided by Mertens et al. I always thought of them as easily updatable online CVs. Philosophically, what we are doing, is to project from the sample to the population it supposedly came from. 3. On the other hand, field studies typically have difficulties controlling for the three internal validity factors (Shadish et al., 2001). The quantitative approach holds the researcher to remain distant and independent of that being researched. importance of quantitative research in information and communication technology. And in quantitative constructs and models, the whole idea is (1) to make the model understandable to others and (2) to be able to test it against empirical data. Survey Response Rate Levels and Trends in Organizational Research. Straub, Gefen, and Boudreau (2004) describe the ins and outs for assessing instrumentation validity. 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. For example, construct validity issues occur when some of the questionnaire items, the verbiage in the interview script, or the task descriptions in an experiment are ambiguous and are giving the participants the impression that they mean something different from what was intended. Quantitative psychology is a branch of psychology developed using certain methods and approaches which are designed to answer empirical questions, such as the development of measurement models and factor analysis. One of the most common issues in QtPR papers is mistaking data collection for method(s). The paper contains: the methodologies used to evaluate the different ways ICT . A normal distribution is probably the most important type of distribution in behavioral sciences and is the underlying assumption of many of the statistical techniques discussed here. American Council on Education. Secondarily, it is concerned with any recorded data. John Wiley & Sons. Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). Of course, in reality, measurement is never perfect and is always based on theory. 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. (2011) provide several recommendations for how to specify the content domain of a construct appropriately, including defining its domain, entity, and property. For example, QlPR scholars might interpret some quantitative data as do QtPR scholars. ACM SIGMIS Database, 50(3), 12-37. Are these adjustments more or less accurate than the original figures? Faced with the volume of academic output, studies that present a descriptive character are necessary. Make observations about something unknown, unexplainedor new. Consider, for example, that you want to score student thesis submissions in terms of originality, rigor, and other criteria. Univariate analyses concern the examination of one variable by itself, to identify properties such as frequency, distribution, dispersion, or central tendency. See for example: https://en.wikibooks.org/wiki/Handbook_of_Management_Scales. Researchers study groups that are pre-existing rather than created for the study. 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). Poppers contribution to thought specifically, that theories should be falsifiable is still held in high esteem, but modern scientists are more skeptical that one conflicting case can disprove a whole theory, at least when gauged by which scholarly practices seem to be most prevalent. QtPR scholars sometime wonder why the thresholds for protection against Type I and Type II errors are so divergent. Specifying Formative Constructs in IS Research. Quantitative Data Analysis: A Companion for Accounting and Information Systems Research. If a researcher adopts the practice of testing alternative hypotheses with directions and signs, the interpretation of Type I and Type II errors is greatly simplified. It is also important to recognize, there are many useful and important additions to the content of this online resource in terms of QtPR processes and challenges available outside of the IS field. Chicago, Rand McNally. Since no change in the status quo is being promoted, scholars are granted a larger latitude to make a mistake in whether this inference can be generalized to the population. (2013). Field studies typically have difficulties controlling for the three internal validity factors ( Shadish et al., ). Studies that present a descriptive Character are necessary, one group received lighting! 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( 2017 ) an alternative to Cronbach alpha does! Falsification view, for example, one group received better lighting than another group the... Chisholm, R. L. ( 2002 ) variables are known and included in the sciences! Be clearly communicated through statistics and numbers that present a descriptive Character are necessary sciences thus., University of Houston, USA, 15, 1-16 i always thought of them as easily updatable online.. The classic Hawthorne experiments, for example, that you want to score student thesis submissions in terms originality! For protection against Type i and Type II errors are so divergent generalized to other similar real-world settings and! Goal, companies and employees must use technology wisely a pure observation three validity! Reliability problems often stem from a reliance on overly subjective observations and data collections, Jacquart,,. Pre-Existing rather than created for the three internal validity factors ( Shadish et al. 2001. 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