Experimental research has become complex and thus a challenge to science education. Only very few students can typically be trained on advanced scientific equipment. It is therefore important to find new tools that allow all students to acquire laboratory skills individually and independent of where they are located. In a design-based research process we have investigated the feasibility of using a virtual laboratory as a photo-realistic and scientifically valid representation of advanced scientific infrastructure to teach modern experimental science, here, molecular quantum optics. We found a concept based on three educational principles that allows undergraduate students to become acquainted with procedures and concepts of a modern research field.
Laboratory settings have often been recommended for training students in practical applications of science, but appropriate learning environments for modern research are rare. Today, science is highly interconnected, often multidisciplinary and complex; it therefore requires adequate new learning tools. While the expenses for laboratory infrastructure often prevent its use for training purposes, computational technologies now allow the realization of virtual laboratories that can serve learning equally well. These tools can also provide access to experiments that would be too hazardous to be used in a classroom5. How to create and use such learning tools is a growing field of educational research. Here, we present the concept of Simulated Interactive Research Experiments (SiReX) that have been developed in a design-based research. SiReX combine photo-realistic real-time simulations with a comprehensive series of cognitive tools11. We demonstrate the feasibility of this approach for the example of quantum diffraction and interferometry with large molecules. This realization of the concept has been evaluated with regards to learning outcomes of undergraduate students.
When the effect of one factor depends on the level of the other factor. You can use an interaction plot to visualize possible interactions.
Parallel lines in an interaction plot indicate no interaction. The greater the difference in slope between the lines, the higher the degree of interaction. However, the interaction plot doesn’t alert you if the interaction is statistically significant.
Basic concepts in experimentation
Dependency: Experiments allow marketing researchers to study the effects of an independent variable on a dependent variable. The researcher is able to manipulate the independent variable (i.e. he/she is able to change the value of the independent variable) and observe what effect, if any, this has upon the value of the dependent variable. Put another way, an independent variable is one which can be manipulated independently of other variables. Independent variables are selected for inclusion in an experiment on the basis of an assumption that they are in some way related to the dependent variable being studied. It is for this reason that independent variables are on occasion referred to as explanatory variables. The dependent variable is the one under study. The researcher begins from the premise that changes in the value of the dependent variable are at least in part caused by changes in the independent variable. The experiment is designed to determine whether or not this cause and effect relationship actually exists.
Causality: A causal relationship is said to exist where the value of one variable is known to determine or influence the value of another. Green et al.3 draw a distinction between two types of causation: deterministic and probabilistic.
Where the independent variable (X) wholly explains changes in the value of the dependent variable (Y) and the researcher is able to establish the functional relationship between the two variables then this can be expressed as follows:
y = f(x)
In this case, it is said that X is both a necessary and a sufficient condition for Y to occur. The value of Y is determined by X, and X alone. Thus it can be said, in these circumstances, that X is a deterministic cause of Y. An illustrative example would be where the demand for agricultural commodities, say sugar, is dependent upon the world price. Further suppose that the functional relationship between sugar demand and world prices is known, then the formula becomes:
Changes in demand for sugar (grade No. 6) = f(World Price)
Whilst this example serves to illustrate the point it is rare to find such relationships when studying marketing problems. In most instances, the value of the dependent variable will be a function of several variables. For instance, only in exceptional cases would the demand for a product, even a commodity, depend solely upon price movements. Factors such as the reputation of the supplier, terms of sale, promotional activities, packaging etc., are likely to have an impact on demand as well. A more common causal model is one where the value of the dependent variable is a function of several independent variables.
Marketing problems are more often multivariate than univariate and so the relationship between dependent and independent variables is more often probabilistic than deterministic. A probabilistic relationship could be expressed as:
y = f(x1, x2,…xn).
What is depicted here is a situation where the dependent variable (y) is a function of several variables (x1, x2,…xn). If marketing research can establish the form of the relationship (f) between the independent variables and also between the independent and dependent variables then the value of y can be predicted. In this instance x1, for example, is a necessary but not sufficient condition for y to occur. The same is true of each of the other independent variables. Rather, each individual independent variable is said to be a probabilistic cause of the value of y
Example of an interaction plot
For example, cereal grains must be dry enough before the packaging process. Lab technicians collect moisture data on grains at several oven times and temperatures.

This plot indicates an interaction between the oven temperature and oven time. The grain has a lower moisture percentage when baked for a time of 60 minutes as opposed to 30 minutes at 125 and 130 degrees. However, when the temperature is 135 degrees, the grain has a lower moisture percentage when baked for 30 minutes.
Interaction plots are most often used to visualize interactions during ANOVA or DOE.
Minitab draws a single interaction plot if you enter two factors, or a matrix of interaction plots if you enter more than two factors.
Which interaction plots are available in Minitab?
Minitab provides interaction plots to accompany various analyses. Use the interaction option available through:
• Stat > DOE > Factorial > Factorial Plots to generate interaction plots specifically for factorial designs.
• Stat > DOE > Mixture > Factorial Plots to generate interaction plots specifically for process variables in mixture designs.
• Stat > ANOVA > General Linear Model > Factorial Plots to generate interaction plots for the fitted values from doing an analysis of variance.
• Stat > Regression and then choose either Regression > Factorial Plots, Binary Logistic Regression > Factorial Plots, or Poisson Regression > Factorial Plots to generate interaction plots from a regression model.

Sequential experimentation is the application of statistical experimental design methods to improving processes when many experimental factors must be studied. It emphasizes the sequential use of small two-level designs and steepest ascent to identify critical factors and improved settings, and the sequential assembly of second-order designs to elucidate the nature of the response surface in the improved operational region when necessary.
Applications of sequential analysis
Clinical trials
In a randomized trial with two treatment groups, group sequential testing may for example be conducted in the following manner: After n subjects in each group, i.e., a total of 2n subjects, are available, an interim analysis is conducted. That means, a statistical test is performed to compare the two groups, if the null hypothesis is rejected, the trial is terminated. Otherwise, the trial continues. Another n subjects per group are recruited. The statistical test is performed again, now including all 4n subjects. If the null is rejected, the trial is terminated. Otherwise, it continues with periodic evaluations until a maximum number of interim analyses have been performed. At this point, the last statistical test is conducted, and the trial is discontinued.
Other applications
Sequential analysis also has a connection to the problem of gambler’s ruin that has been studied by, among others, Huyghens in 1657.
Step detection is the process of finding abrupt changes in the mean level of a time series or signal. It is usually considered as a special kind of statistical method known as change point detection. Often, the step is small and the time series is corrupted by some kind of noise, and this makes the problem challenging because the step may be hidden by the noise. Therefore, statistical and/or signal processing algorithms are often required. When the algorithms are run online as the data is coming in, especially with the aim of producing an alert, this is an application of sequential analysis.
A method based on chance alone by which study participants are assigned to a treatment group. Randomization minimizes the differences among groups by equally distributing people with particular characteristics among all the trial arms. The researchers do not know which treatment is better. From what is known at the time, any one of the treatments chosen could be of benefit to the participant.
Randomization is a technique used to balance the effect of extraneous or uncontrollable conditions that can impact the results of an experiment. For example, ambient temperature, humidity, raw materials, or operators can change during an experiment and inadvertently affect test results. By randomizing the order in which experimental runs are done, you reduce the chance that differences in experimental materials or conditions strongly bias results. Randomization also lets you estimate the inherent variation in materials and conditions so that you can make valid statistical inferences based on the data from your experiment.
Suppose you work for an offset printing company interested in maximizing the effectiveness of their bookbinding technique. You can control factors such as glue temperature, paper type, and cooling time. However, you cannot control humidity, which can affect how quickly the glue sets. Or, perhaps there are other “unknowns” that cannot be easily controlled or measured. For example, the bookbinding machine might not be applying consistent pressure.

Interactive nature of Experimentation offers the possibility of establishing a cause and effective relationship between variables and this makes it an attractive methodology to marketing researchers. An experiment is a contrived situation that allows a researcher to manipulate one or more variables whilst controlling all of the others and measuring the resultant effects on some independent variable.
Experiments are of two types: those conducted in a laboratory setting and those which are executed in natural settings; these are referred to as field experiments. Laboratory experiments give the researcher direct control over most, if not all, of the variables that could affect the outcome of the experiment. The evidence for drawing inferences about causal relationships takes three forms: associative variation, consistent ordering of events and the absence of alternative causes.
There are a number of potential impediments to obtaining valid results from experiments. These may be categorised according to whether a given confounding factor has internal validity, external validity, or both. Internal validity is called into question when there is doubt that the experimental treatment is actually responsible for changes in the value of the dependent variable. External validity becomes an issue when there is uncertainty as to whether experimental findings can be generalised to a defined population. The impediments to internal validity are history, pre-testing, maturation, instrumentation, sampling bias and mortality. Impediments to external validity are: the interactive effects of testing, the interactive effects of sampling bias and errors arising from making use of contrived situations.

Board of Directors of the National Science Teachers Association. The use of computers in science education (1999, downloaded 2015-06-01). URLhttp://www.nsta.org/about/positions/computers.aspx.
Rocard, M. et al. Science education now: A renewed pedagogy for the future of Europe. ISBN: 978-92-79-05659-8 (Office for Official Publications of the European Communities, 2007).
Sabelli, N. H. Complexity, technology, science, and education. J. Learn. Sci. 15, 5–9 (2006).
Nedic, Z., Machotka, J. & Nafalski, A. Remote laboratories versus virtual and real laboratories. vol. 1, T3E–1–T3E–6 (IEEE Frontiers in Education Conference, 2003).

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