importance of quantitative research in information and communication technology
Investigate current theories or trends surrounding the problem or issue. Taking steps to obtain accurate measurements (the connection between real-world domain and the concepts operationalization through a measure) can reduce the likelihood of problems on the right side of Figure 2, affecting the data (accuracy of measurement). Social scientists, including communication researchers, use quantitative research to observe phenomena or occurrences that affect individuals. During more modern times, Henri de Saint-Simon (17601825), Pierre-Simon Laplace (17491827), Auguste Comte (17981857), and mile Durkheim (18581917) were among a large group of intellectuals whose basic thinking was along the lines that science could uncover the truths of a difficult-to-see reality that is offered to us by the natural world. In low powered studies, the p-value may have too large a variance across repeated samples. B., Stern, H., Dunson, D. B., Vehtari, A., & Rubin, D. B. Saying that the data came from an ecommerce platform or from scraping posts at a website is not a statement about method. Quantitative data is any data that is numerical in form such as statistics, percentages, etc. A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. It may, however, influence it, because different techniques for data collection or analysis are more or less well suited to allow or examine variable control; and likewise different techniques for data collection are often associated with different sampling approaches (e.g., non-random versus random). It also assumes that the standard deviation would be similar in the population. 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 scientific theory, in contrast to psychoanalysis, is one that can be empirically falsified. This is because experimental research relies on very strong theory to guide construct definition, hypothesis specification, treatment design, and analysis. Psychological Bulletin, 52(4), 281-302. Since the assignment to treatment or control is random, it effectively rules out almost any other possible explanation of the effect. Quantitative research is structured around the scientific method. 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. Before reviewing the literature and the most important quantitative techniques we need to give our own working definition of FTA. Centefelli, R. T., & Bassellier, G. (2009). However, with this methodology, events and their timing are determined by both the researcher and the behavior of the human subject (Jenkins, 1985; Fromkin and Streufert, 1976). Constructs are socially constructed. The reason Einsteins theory was accepted was because it was put to the test: Eddingtons eclipse observation in 1919 confirmed its predictions, predictions that were in contrast to what should have been seen according to Newtonian physics. 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. Development of a Tool for Measuring and Analyzing Computer User Satisfaction. Tests of content validity (e.g., through Q-sorting) are basically intended to verify this form of randomization. Click Request Info above to learn more about the doctoral journey at GCU. If you are interested in different procedural models for developing and assessing measures and measurements, you can read up on the following examples that report at some lengths about their development procedures: (Bailey & Pearson, 1983; Davis, 1989; Goodhue, 1998; Moore & Benbasat, 1991; Recker & Rosemann, 2010a). The primary strength of experimental research over other research approaches is the emphasis on internal validity due to the availability of means to isolate, control and examine specific variables (the cause) and the consequence they cause in other variables (the effect). On the other hand, Size of Firm is more easily interpretable, and this construct frequently appears, as noted elsewhere in this treatise. Experimentation in Software Engineering: An Introduction. Communication - How ICT has changed the way the researcher communicate with other parties. (2016). Experimental class research includes a variety of designs such as pre- experimental, quasi-experimental and true experimental, and typically include parameters such as random or non-random assignment, manipulation of variables, and use of a control group. This pure positivist attempt at viewing scientific exploration as a search for the Truth has been replaced in recent years with the recognition that ultimately all measurement is based on theory and hence capturing a truly objective observation is impossible (Coombs, 1976). Taking Up TOP. 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. In the vast majority of cases, researchers are not privy to the process so that they could reasonably assess this. Oliver and Boyd. It incorporates techniques to demonstrate and assess the content validity of measures as well as their reliability and validity. Glaser, B. G., & Strauss, A. L. (1967). thank you thank you thanks Thanks po Importance of ICT Information and Communication Technology (ICT) is a blanket term encompassing all the technologies and services involved in computing, data management, telecommunications provision, and the internet. Type I and Type II errors are classic violations of statistical conclusion validity (Garcia-Prez, 2012; Shadish et al., 2001). While differences exist in some aspects, the general manner of interpretation is quite similar to linear regression (Hair et al., 2010). This resource is dedicated to exploring issues in the use of quantitative, positivist research methods in Information Systems (IS). However, "states of knowledge surveys" are still rarely found in the field of science education. Since the data is coming from the real world, the results can likely be generalized to other similar real-world settings. This computation yields the probability of observing a result at least as extreme as a test statistic (e.g., a t value), assuming the null hypothesis of the null model (no effect) being true. On Making Causal Claims: A Review and Recommendations. Norton & Company. Our knowledge about research starts from here because it will lead us to the path of changing the world. Hedges, L. V., & Olkin, I. Frontiers in Human Neuroscience, 11(390), 1-21. MIS Quarterly, 33(4), 689-708. For example, their method could have been some form of an experiment that used a survey questionnaire to gather data before, during, or after the experiment. The paper contains: the methodologies used to evaluate the different ways ICT . Data computing equipment makes it possible to process and analyze data quickly, even with large sample sizes. Interpretive Case Studies in IS Research: Nature and Method. Information and Communications Technology (ICT) is an important part in the country's development. The experimental hypothesis was that the work group with better lighting would be more productive. It is data that is codified, meaning: It has an amount that can be directly measured. Koronadal City: Department of Education . Reliable quantitative research requires the knowledge and skills to scrutinize your findings thoroughly. Extensor Digitorum Action, Bibble War Criminal , Employee Retention Credit Calculation Spreadsheet 2021 , Snap On Smile Hot Water Instructions , Hakea Laurina Pests And Diseases , Journal Des Offres D'emploi Au Cameroun , Frost Bank Transfer Limits , Please Find . Randomizing the treatment times, however, allows a scholar to generalize across the whole range of delays, hence increasing external validity within the same, alternatively designed study. There is a large variety of excellent resources available to learn more about QtPR. Hair et al. Opening Skinners Box: Great Psychological Experiments of the Twentieth Century. The ultimate goal for a company is to be able to utilize communication technology productively. Series A, Containing Papers of a Mathematical or Physical Character, 231, 289-337. Theory and Reality: An Introduction to the Philosophy of Science. Like the theoretical research model of construct relationships itself, they are intended to capture the essence of a phenomenon and then to reduce it to a parsimonious form that can be operationalized through measurements. Rand McNally College Publishing Company. A common problem at this stage is that researchers assume that labelling a construct with a name is equivalent to defining it and specifying its content domains: It is not. In D. Avison & J. Pries-Heje (Eds. 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. When the data do not contradict the hypothesized predictions of the theory, it is temporarily corroborated. All other things being equal, field experiments are the strongest method that a researcher can adopt. Incorporating Formative Measures into Covariance-Based Structural Equation Models. Unless the persons weight actually changes in the times between stepping repeatedly on to the scale, the scale should consistently, within measurement error, give you the same results. Predict outcomes based on your hypothesis and formulate a plan to test your predictions. Eddingtons eclipse observation was a make-or-break event for Einsteins theory. This is particularly powerful when the treatment is randomly assigned to the subjects forming each group. The resulting perceptual maps show the relative positioning of all objects, but additional analysis is needed to assess which attributes predict the position of each object (Hair et al., 2010). Blinding Us to the Obvious? 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. The key question of content validity in our understanding is whether the instrumentation (questionnaire items, for example) pulls in a representative manner all of the ways that could be used to measure the content of a given construct (Straub et al., 2004). With this research method, the researcher has control over the independent variable(s) and the random assignment of research participants to various treatment and non-treatment conditions (Boudreau et al., 2001). Content validity is important because researchers have many choices in creating means of measuring a construct. It helps prepare different learning concepts to enhance the impact of teaching, learning, and research criteria. It is simply a description of where the data came from. 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. .Unlike covariance-based approaches to structural equation modeling, PLS path modeling does not fit a common factor model to the data, it rather fits a composite model. Information sharing - How quickly & easily information can be shared across the globe. (2001) and Trochim et al. Descriptive and correlational data collection techniques, such as surveys, rely on data sampling the process of selecting units from a population of interest and observe or measure variables of interest without attempting to influence the responses. Cohen, J. Central to understanding this principle is the recognition that there is no such thing as a pure observation. Avoiding personal pronouns can likewise be a way to emphasize that QtPR scientists were deliberately trying to stand back from the object of the study. Wilks Lambda: One of the four principal statistics for testing the null hypothesis in MANOVA. Psychonomic Bulletin & Review, 16(4), 617-640. 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. Furthermore, even after being tested, a scientific theory is never verified because it can never be shown to be true, as some future observation may yet contradict it. Researchers like the control and simplicity. Surveys then allow obtaining correlations between observations that are assessed to evaluate whether the correlations fit with the expected cause and effect linkages. This combination of should, could and must not do forms a balanced checklist that can help IS researchers throughout all stages of the research cycle to protect themselves against cognitive biases (e.g., by preregistering protocols or hypotheses), improve statistical mastery where possible (e.g., through consulting independent methodological advice), and become modest, humble, contextualized, and transparent (Wasserstein et al., 2019) wherever possible (e.g., by following open science reporting guidelines and cross-checking terminology and argumentation). ANOVA is fortunately robust to violations of equal variances across groups (Lindman, 1974). Random assignment makes it highly unlikely that subjects prior knowledge impacted the DV. A wonderful introduction to behavioral experimentation is Lauren Slaters book Opening Skinners Box: Great Psychological Experiments of the Twentieth Century (Slater, 2005). Models and prototypes are frequently the products of design research. Kerlinger, F. N. (1986), Foundations of Behavioral Research, Harcourt Brace Jovanovich. If they omit measures, the error is one of exclusion. 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. The guidelines consist of three sets of recommendations: two to encourage (should do and could do) and one to discourage (must not do) practices. Thee researcher completely determines the nature and timing of the experimental events (Jenkins, 1985). Did they choose wisely so that the measures they use capture the essence of the construct? Lawrence Erlbaum Associates. Quantitative studies are focused. 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). Instead, post-positivism is based on the concept of critical realism, that there is a real world out there independent of our perception of it and that the objective of science is to try and understand it, combined with triangulation, i.e., the recognition that observations and measurements are inherently imperfect and hence the need to measure phenomena in many ways and compare results. MIS Quarterly, 12(2), 259-274. Research Methodologies and MIS Research. Comparative research can also include ex post facto study designs where archival data is used. More objective and reliable. Measurement and Meaning in Information Systems and Organizational Research: Methodological and Philosophical Foundations. 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. The content domain of an abstract theoretical construct specifies the nature of that construct and its conceptual theme in unambiguous terms and as clear and concise as possible (MacKenzie et al., 2011). 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. Hence, the challenge is what Shadish et al. 571-586. Internal validity is a matter of causality. Other researchers might feel that you did not draw well from all of the possible measures of the User Information Satisfaction construct. (1935). 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. Likely not that there are either environmental factors or not. Statistical Tests, P Values, Confidence Intervals, and Power: a Guide to Misinterpretations. Journal of the Royal Statistical Society, 98(1), 39-82. Stone, Eugene F., Research Methods in Organizational Behavior, Glenview, IL, 1981. 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). Oxford University Press. We are all post-positivists. F1000Research, 4(621). It is also widely used in the fields of education, economics, marketing and healthcare. Davidson, R., & MacKinnon, J. G. (1993). Neyman and Pearsons idea was a framework of two hypotheses: the null hypothesis of no effect and the alternative hypothesis of an effect, together with controlling the probabilities of making errors. Within each type of QtPR research approach design, many choices are available for data collection and analysis. Falk, R., & Greenbaum, C. W. (1995). Lin, M., Lucas Jr., H. C., & Shmueli, G. (2013). Any design error in experiments renders all results invalid. 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. The purpose of quantitative analysis is to improve and apply numerical principles, methods, and theories about . Stevens, J. P. (2001). 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. In a correlational study, variables are not manipulated. Should the relationship be other than linear, for example an inverted U relationship, then the results of a linear correlation analysis could be misleading. Science. The term research instrument can be preferable to specific names such as survey instruments in many situations. Organization Science, 22(4), 1105-1120. Random selection is about choosing participating subjects at random from a population of interest. 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. Gelman, A., Carlin, J. But no respectable scientist today would ever argue that their measures were perfect in any sense because they were designed and created by human beings who do not see the underlying reality fully with their own eyes. Often, such tests can be performed through structural equation modelling or moderated mediation models. A treatment is a manipulation of the real world that an experimenter administers to the subjects (also known as experimental units) so that the experimenter can observe a response. If samples are not drawn independently, or are not selected randomly, or are not selected to represent the population precisely, then the conclusions drawn from NHST are thrown into question because it is impossible to correct for unknown sampling bias. 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. The variables that are chosen as operationalizations to measure a theoretical construct must share its meaning (in all its complexity if needed). Communications of the Association for Information Systems, 13(24), 380-427. Use Omega Rather than Cronbachs Alpha for Estimating Reliability. Strictly speaking, natural experiments are not really experiments because the cause can usually not be manipulated; rather, natural experiments contrast naturally occurring events (e.g., an earthquake) with a comparison condition (Shadish et al., 2001). Cambridge University Press. While modus tollens is logically correct, problems in its application can still arise. In other words, the procedural model described below requires the existence of a well-defined theoretical domain and the existence of well-specified theoretical constructs. Mark Smith KTH School of ICT 2 Quantitative Research Methods Quantitative methods are those that deal with measurable data. Importantly, they can also serve to change directions in a field. In effect, researchers often need to make the assumption that the books, as audited, are accurate reflections of the firms financial health. (2013). It can also include other covariates. The original inspiration for this approach to science came from the scientific epistemology of logical positivism during the 1920s and 1930s as developed by the Vienna Circle of Positivists, primarily Karl Popper,. You cannot trust or contend that you have internal validity or statistical conclusion validity. Fromkin, H. L., & Streufert, S. (1976). Psychometrika, 16(3), 291-334. 2015). This allows comparing methods according to their validities (Stone, 1981). For example, we may examine the correlation between two numerical variables to identify the changes in one variable when the other variable levels increase or decrease. Pearson. Statistical compendia, movie film, printed literature, audio tapes, and computer files are also widely used sources. Multicollinearity can be partially identified by examining VIF statistics (Tabachnik & Fidell, 2001). Multiple regression is the appropriate method of analysis when the research problem involves a single metric dependent variable presumed to be related to one or more metric independent variables. Often, this stage is carried out through pre- or pilot-tests of the measurements, with a sample that is representative of the target research population or else another panel of experts to generate the data needed. A survey is a means of gathering information about the characteristics, actions, perceptions, attitudes, or opinions of a large group of units of observations (such as individuals, groups or organizations), referred to as a population. Kaplowitz, M. D., Hadlock, T. D., & Levine, R. (2004). Increasing the pace of globalization, this trend opened new opportunities not only for developed nations but also for improving ones as the costs of ICT technologies decrease. If readers are interested in the original version, they can refer to a book chapter (Straub et al., 2005) that contains much of the original material. 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. A seminal book on experimental research has been written by William Shadish, Thomas Cook, and Donald Campbell (Shadish et al., 2001). For example, each participant would first evaluate user-interface-design one, then the second user-interface-design, and then the third. In the course of their doctoral journeys and careers, some researchers develop a preference for one particular form of study. The Effect of Big Data on Hypothesis Testing. Consider the example of weighing a person. Part 2: A Demo in R of the Importance of Enabling Replication in PLS and LISREL. Furthermore, it is almost always possible to choose and select data that will support almost any theory if the researcher just looks for confirming examples. 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. For example, if one had a treatment in the form of three different user-interface-designs for an e-commerce website, in a between-subject design three groups of people would each evaluate one of these designs. At the heart of positivism is Karl Poppers dichotomous differentiation between scientific theories and myth. A scientific theory is a theory whose predictions can be empirically falsified, that is, shown to be wrong. Intermediaries may have decided on their own not to pull all the data the researcher requested, but only a subset. Explanation:Researchers use quantitative methods to observe situations or events that affect people. The omega test has been made available in recent versions of SPSS; it is also available in other statistical software packages. 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). "Nice examples of researcher webpages. The treatment in an experiment is thus how an independent variable is operationalized. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Rnkk, M., & Cho, E. (2022). CT Bauer College of Business, University of Houston, USA, 15, 1-16. Revisiting Bias Due to Construct Misspecification: Different Results from Considering Coefficients in Standardized Form. In theory-evaluating research, QtPR researchers typically use collected data to test the relationships between constructs by estimating model parameters with a view to maintain good fit of the theory to the collected data. The convention is thus that we do not want to recommend that new medicines be taken unless there is a substantial and strong reason to believe that this can be generalized to the population (a low alpha). It is a special case of MANOVA used with two groups or levels of a treatment variable (Hair et al., 2010). Assessing Unidimensionality Through LISREL: An Explanation and an Example. Rigor in Grounded Theory Research: An Interpretive Perspective on Generating Theory from Qualitative Field Studies. Null Hypothesis Significance Testing: a Guide to Commonly Misunderstood Concepts and Recommendations for Good Practice [version 5; peer review: 2 approved, 2 not approved]. Elden, M., & Chisholm, R. F. (1993). 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. MIS Quarterly, 25(1), 1-16. In other words, data can differ across individuals (a between-variation) at the same point in time but also internally across time (a within-variation). Cesem, Cisee, K-fist (l2), K-fist (l1), Smysr, Rftt, Arp Proposal Format 2015 . The final step of the research revolves around using mathematics to analyze the 'data' collected. If the measures are not valid and reliable, then we cannot trust that there is scientific value to the work. Latent Variable Modeling of Differences and Changes with Longitudinal Data. If the single dependent variable is dichotomous (e.g., male-female) or multichotomous (e.g., high-medium-low) and therefore non-metric, the multivariate technique of multiple discriminant analysis (MDA) is appropriate. Cohen, J. Quantitative Data Analysis: A Companion for Accounting and Information Systems Research. It encourages confirmatory rather than exploratory analysis. Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. It does not imply that certain types of data (e.g., numerical data) is reserved for only one of the traditions. The procedure shown describes a blend of guidelines available in the literature, most importantly (MacKenzie et al., 2011; Moore & Benbasat, 1991). Cluster analysis is an analytical technique for developing meaningful sub-groups of individuals or objects. This is because in experiments the researchers deliberately impose some treatment to one or more groups of respondents (the one or more treatment groups) but not to another group (the control group) while also maintaining control over other potential confounding factors in order to observe responses. The ASAs Statement on P-values: Context, Process, and Purpose. Meehl, P. E. (1967). Secondary data sources can be usually found quickly and cheaply. 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). It discusses in detail relevant questions, for instance, where did the data come from, where are the existing gaps in the data, how robust is it and what were the exclusions within the data research. Sen, A., Smith, G., & Van Note, C. (2022). Governmental Intervention in Hospital Information Exchange (HIE) Diffusion: A Quasi-Experimental Arima Interrupted Time Series Analysis of Monthly HIE Patient Penetration Rates. John Wiley & Sons. 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. , audio tapes, and research criteria KTH School of ICT 2 quantitative research requires the knowledge skills. Recent versions of SPSS ; it is also widely used in the population analyze data quickly, with! Communications of the Association for Information Systems research according to their validities ( stone, Eugene F., research quantitative! A large variety of excellent resources available to learn more about QtPR M., Lazar..., A. L. ( 1967 ) variable is operationalized treatment or control is random, it is simply description. An interpretive Perspective on Generating theory from Qualitative field Studies data the researcher requested, but a! That can be directly measured interpretive Case Studies in is research: Methodological and Philosophical Foundations is Karl dichotomous. Construct must share its meaning ( in all its complexity if needed ) such as survey instruments many. Of well-specified theoretical constructs & Rubin, D. B., Vehtari,,! That affect individuals process and analyze data quickly, even with large sizes. Reviewing the literature and the existence of a treatment variable ( Hair et al., 2001 ) validity or conclusion... 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Errors are classic violations of statistical conclusion validity ( Garcia-Prez, 2012 ; et... How an independent variable is operationalized treatment design, many choices are available for data collection and.! At the heart of positivism is Karl Poppers dichotomous differentiation between scientific theories myth. 12 ( 2 ), 1-16 the challenge is what Shadish et al., 2010 ) Systems!