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PSYC 2113 Qualitative Research Methods

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PSYC 2113 Qualitative Research Methods

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PSYC 2113 Qualitative Research Methods

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Course Code: PSYC 2113
University: Humber College is not sponsored or endorsed by this college or university

Country: Canada


This is an individual assignment in which cooperation is not permitted. Reference all sources.Compare the advantages and disadvantages of histograms, kernel density plots and moving average plots for visual representation and analysis of time series data. Enclose examples of application of each method from literature. 


1. Moving average plots
This is a time series plot generated by separating the observations in the dataset into subsets and obtaining the averages of the subsets (Hastie, Tibshir, & Jerome, 2009). These averages are then plotted on a graph to produce the moving average plot. Since the plot largely relies on the means, it is often also referred to as the Moving Means plot. This type of plot is applied in the research on financial bubbles (Sam, David, Leybourne, Robert, & Taylor, 2018).
ADV antages

The moving average plot is fairly an easy plot to construct. This is especially true for the manual plotting of the observations. Instead of plotting the entire observations in the dataset, the dataset is divided into subsets of n observations. This produces fewer points for plotting.(Hastie, Tibshir, & Jerome, 2009) 
The moving average plot is efficient in generating the long term trends for the data. This plot for the time series is appropriate for cases where data has been collected over long periods.(Hastie, Tibshir, & Jerome, 2009)
This plot smoothens the observations of the dataset.(Hastie, Tibshir, & Jerome, 2009)


The moving average plot uses means, this makes it inefficient at identifying other properties of a dataset. These properties, such as the outlier observations, would not be noticed since the averaging tends to hide the extreme observations (Hastie, Tibshir, & Jerome, 2009).
The moving average plot is not appropriate for generating the short term trends (Hastie, Tibshir, & Jerome, 2009).
With the use of the means, the moving average plot does not represent the actual data observations (Hastie, Tibshir, & Jerome, 2009).

These are time series graphs that plot the frequencies of a variable in a dataset by dividing the variable into intervals (Barbara & Susan, 2014). The time series analysis in the case of the histogram involves simplification of the observations of the data variable. This is done by introducing intervals of a predetermined and normally equal lengths. These intervals are then plotted to produce the histogram. Histograms are used to describe the trend in the time series for the oil and gas reservoirs (Hodaway, Fabii, & Lozie, 2015).
ADV antages

The histogram can comfortably handle large datasets without its quality being affected. This is the scalability characteristic of the histogram. This makes the varying of the size of data being plotted (at least in an incremental sense) possible without compromising the quality of the histogram produced (Barbara & Susan, 2014).
In instances where the dataset being analyzed is significantly large (big data), the histogram proves to be the most efficient plotting tool for the trend. The histogram gives better visualization of the trend compared to other plotting tools for time series (Barbara & Susan, 2014).
The histograms provide more information about the observations in the dataset other than the trend. The statistics of the observations that can be derived from the histogram are outlier, abnormality, skewness, centrality and spread (Barbara & Susan, 2014).


Similar to the moving average plot, the histogram cannot be said to represent the actual data observations. This is since the frequency of the observations themselves are not plotted, but rather the frequency of the intervals (Barbara & Susan, 2014).
The histogram cannot give information on the type of distribution of the data (Barbara & Susan, 2014).
The histogram, unlike the bar graph, is limited to giving the frequency information for the non-categorical data variables. In cases the data variables are measured on the nominal or ordinal scale (hence categorical), the histogram cannot be plotted. This narrows the applications of the histograms to a large extent (Barbara & Susan, 2014).      

Kernel density plots
These graphs use the kernel density estimation as a basis for generating the plots for the time series (Martinez, Martinez, & Solka, 2010). The kernel density plots were used in testing whether financial returns exhibit predictability (Imanol & Ignacio, 2015).

The kernel density plot can be generated for data variables measured on nominal and ordinal scales. This is made possible by plotting the different categories of the data variables on a single graph. Hence allowing for the comparison of the various categories (Martinez, Martinez, & Solka, 2010).
The kernel density estimation smoothens the data, thus reducing the noise. This produces plots that are better for inferencing as well as in inspecting the type of distribution of the data (Martinez, Martinez, & Solka, 2010).


The application of the kernel density estimation process implies that the actual observations are not represented in the plot (Martinez, Martinez, & Solka, 2010).





Moving Average Plots

Data is smoothened

Efficient in long term trend analysis

Easy to plot


No other information can be drawn from the plot

Actual observations are not plotted




Good Scalability

Efficient with large datasets

More information can be drawn from the plot


Cannot analyze categorical data variables

Does not give information on the type of distribution

Actual observations are not plotted


Kernel Density Plots

Data is smoothened

Gives information on the type of distribution

Can analyze categorical data variables


Actual observations are not plotted





Barbara, I., & Susan, D. (2014). Introductory Statistics (1st ed.). New York: OpenStax CNX.
Hastie, T., Tibshir, R., & Jerome, H. (2009). The Elements of Statistical Learning (2nd ed.). New York: Springer.
Hodaway, K. R., Fabii, L., & Lozie, D. (2015). Unconventional Data-Driven Methodologies Forecast Performance in Unconventional Oil and Gas Reservoirs. SAS Istitutue, Inc, 1-12.
Imanol, A., & Ignacio, N. L. (2015). Testing for Predictability in Financial Returns Using Statistical Learning Procedures. Journal of Time Series Analysis, 36(5). 1-13.
Kabacoff, R. I. (2017, March 15). graphs. Retrieved from statmethods:
Martinez, W. L., Martinez, A. R., & Solka, J. (2010). Exploratory Data Analysis With MATLAB, 2nd Edition (1 ed.). London: CRC/Chapmann & Hall.
Sam, A., David, I. H., Leybourne, S. J., Robert, S., & Taylor, A. M. (2018). Real Time Monitoring for Explosive Financial Bubbles. Journal of Time Series Analysis, 39(8) 1-7.

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