Replicability and generalizability are important considerations when analyzing research findings. Result replicability measures the extent to which results will remain the same when a new sample is drawn, while generalizability refers to the ability to generalize the results from one study to the population (Guan, Xiang, & Keating, 2004). If results are not replicable they will not be generalizable. Replicability is important because it determines whether results are true or a fluke. Measures of replicability can be obtained using either external or internal methods. External replicability analysis requires redrawing a completely new sample and replicating the study. Internal replicability analysis involves procedures used to investigate replicability within the current study sample (Zientek & Thompson, 2007). Although only external analysis can provide definitive answers regarding result replicability, a flawed assessment of result replicability via internal analysis is still better than conjecture (Thompson, 1994). One of the most popular procedures of internal replicability analysis is the bootstrap method. Created by Efron in the 1970s, the bootstrap is a computationally intensive procedure that can be used for a variety of purposes (Beard, Marsh, & Bailey, 2002). The present paper provides an overview of the bootstrap procedure and the method’s advantages and limitations. Bootstrap Methodology Purposes and Approaches of the Bootstrap The bootstrap procedure can be used for inferential or descriptive purposes (Thompson, 1999). When used inferentially, the bootstrap estimates a sampling distribution from which a p-calculated or test statistic can be derived (Thompson, 1999). In inferential bootstrapping, the focus is on the ... ... middle of paper ... ... measures that can be analyzed mathematically, such as the mean or standard deviation, and move to more complex statistical questions or measures (Diaconis & Bradley, 1983). Because bootstrapping procedures can provide statistical estimates for a variety of purposes and questions, bootstrapping is used in numerous fields, such as, biology, chemistry, engineering, psychology, economics, and education, among others (Chernick, 2008). Provides Estimates of Replicability Finally, although the bootstrap does not provide direct evidence of replicability, the procedure can provide an adequate estimate of replicability and generalizability if the original sample is representative of the population (Thompson, 1994). Because redrawing new samples is time consuming and expensive, methods of internal analysis, like the bootstrap, are more feasible (Boos & Stefanski, 2010).
...ed on statistical variance. In today’s scientific world Hare is not wrong to want to establish an empirically significant and testable tool.
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
...s and the GLM model, thus showing an adequate measure for the different variables. The study notes the small sample size. This brings up an issue of external validity, and being able to generalize the results to a wider population outside of their college students (Cozby, 2009).
The sampling procedures that can be utilized in evaluation research is vast. The selected sampling procedure is important in the consideration of external validity. External validity generalizes the findings to individuals in the study sample with characteristics that are alike (DiClemente et al., 2013). Although, not all research studies will require a sampling procedure that would deliver an external validity.
McKinney & Jones’ (1993) six hypotheses are clearly stated in a declarative form and expected differences between groups could be tested thr...
The father of quantitative analysis, Rene Descartes, thought that in order to know and understand something, you have to measure it (Kover, 2008). Quantitative research has two main types of sampling used, probabilistic and purposive. Probabilistic sampling is when there is equal chance of anyone within the studied population to be included. Purposive sampling is used when some benchmarks are used to replace the discrepancy among errors. The primary collection of data is from tests or standardized questionnaires, structured interviews, and closed-ended observational protocols. The secondary means for data collection includes official documents. In this study, the data is analyzed to test one or more expressed hypotheses. Descriptive and inferential analyses are the two types of data analysis used and advance from descriptive to inferential. The next step in the process is data interpretation, and the goal is to give meaning to the results in regards to the hypothesis the theory was derived from. Data interpretation techniques used are generalization, theory-driven, and interpretation of theory (Gelo, Braakmann, Benetka, 2008). The discussion should bring together findings and put them into context of the framework, guiding the study (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). The discussion should include an interpretation of the results; descriptions of themes, trends, and relationships; meanings of the results, and the limitations of the study. In the conclusion, one wants to end the study by providing a synopsis and final comments. It should include a summary of findings, recommendations, and future research (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). Deductive reasoning is used in studies...
...the data did not involve member checking thus reducing its robustness and enable to exclude researcher’s bias. Although a constant comparative method was evident in the discussion which improved the plausibility of the final findings. Themes identified were well corroborated but not declared was anytime a point of theoretical saturation Thus, the published report was found to be particularly strong in the area of believability and dependability; less strong in the area of transferability; and is weak in the area of credibility and confirmability, although, editorial limitations can be a barrier in providing a detailed account (Craig & Smyth, 2007; Ryan, Coughlan, & Cronin, 2007).
According to Jimenez-Buedo (2011), it is difficult to make a valid reference that there is a causal relationship when conducting an experiment in a laboratory-style setting. Jimenez-Buedo (2011) also states that both internal and external validity are being inferred without adequate evidence to support the claims being made in many cases. Jimenez-Buedo (2011) also states that generalization of results in the case of external validity should not be taken lightly. In other words, it appears that she feels that neither internal nor external validity should be inferred in many cases associated with experiments that are done in a laboratory setting versus the real world. This appears to mean that in all circumstances Jimenez-Buedo (2011) favors conducting experiments that are as representative as possible of the real world in order to be able to validate the results and in order to infer a causal or generalizable relationship.
However, since the two differ in their overall goal, their primary interests and methods of receiving a non-random sample differ. In case studies, emphasis is placed on obtaining a representative sample. As MacNealy states, “if several subjects are [being] studied, then the researcher may want to consider how to best achieve a representative sample” (201). A representative sample is key within case studies, because case studies are designed to help build upon preexisting theories and help generate new ones, so it is important that the subjects providing insight actually have some relevance to the study. MacNealy makes this clear when she states, “a researcher will want to select a subject who is typical of some area of interest to begin to collect insights which, when combined with other insights from other empirical projects, could be used to build a general theory” (201). For example, in Engineering Writing/Writing Engineering, Winsor chose her subject, John Phillips, because he is an Engineer, and therefore relevant to her study; her case study can help frame future research within the scope of engineering and writing only because Phillips represents the sample of people within this field. However, in case studies, researchers cannot generalize beyond their representative sample. On the other hand, in quasi-experiments, pretests are of high importance and “research design hypotheses,” in which researchers make generalizations in order to “account for ineffective treatments and threats to internal validity” are crucial (179). Lauer and Asher state that the “quasi-experiment must have at least one pretest or prior set of observations on the subjects in order to determine whether the groups are initially equal or unequal on specific variables tested in the pretest” (179). This practice is seen in Kroll’s Explaining How to Play a Game,”
... tested in the same manner for a specified purpose in order to maintain consistency and validity within results.
Probability and Statistics most widespread use is in the arena of gambling. Gambling is big all over the world and lots of money is won and lost with their aid. In horse racing especially the statistics of a horse in terms of its physical condition and winning history sway numbers of persons into believing that the mathematical evidence that is derived can actually be a good indicator of a race’s outcome. Usually it is if the odds or probability are great in favor of the desired outcome. However the future is uncertain and races can turn out any of a number of different ways.
Often uses random sampling to select a large statistically representative sample from which generalizations can be drawn.
Replicability means that the results that were obtained from an experiment, are able to be duplicated consistently (Lilienfeld et al., 2013, p.24). An experiment or study should be able to be replicated to see if there are correctly or incorrectly done. Any area of study or subject that deals with experiments must have the principle of replicability because in order to make an experiment appear correct, multiple replications of the experiment have to be done.