Onderzoeksmethoden Research Methods

Tag: Video

  • Min, Max and Range

    In statistics, the minimum, maximum, and range are important measures used to describe the spread of data. The minimum is the smallest value in a dataset, while the maximum is the largest value. The range, which is the difference between the maximum and minimum values, provides a simple measure of variability in the data. While these measures are useful for understanding the extremes of a dataset, they are sensitive to outliers and may not always provide a complete picture of data distribution. When reporting these values in APA format, it’s important to include appropriate citations and format the reference list correctly, with hanging indentation and alphabetical order by author’s last name.

    References

    American Psychological Association. (n.d.). Works included in a reference list. APA Style.

    Beattie, B. R., & LaFrance, J. T. (2006). The law of demand versus diminishing marginal utility. Review of Agricultural Economics, 28(2), 263-271.

    Luyendijk, J. (2009). Fit to print: Misrepresenting the Middle East (M. Hutchison, Trans.). Scribe Publications.

    Purdue Online Writing Lab. (n.d.). Reference list: Basic rules. Purdue OWL.

    Scribbr. (n.d.). Setting up the APA reference page | Formatting & references (Examples).

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  • Standard Deviation

    Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of values. In simpler terms, it indicates how much individual data points in a dataset deviate from the mean (average) value. A low standard deviation means that the data points tend to be close to the mean, whereas a high standard deviation indicates that the data points are spread out over a wider range of values. In APA style, standard deviation is denoted by the symbol “SD” and is typically reported alongside the mean to provide a complete picture of the data’s distribution (American Psychological Association, 2022; Purdue OWL, n.d.). For instance, if you were reporting test scores for a group of students, you might say that the average score was 75 with an SD of 10, indicating that most students scored within 10 points of the average. Understanding standard deviation is crucial for interpreting data in media studies, as it helps in assessing the reliability and variability of research findings.

    References

    American Psychological Association. (2022). APA Style numbers and statistics guide. Retrieved from https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf

    Purdue OWL. (n.d.). Numbers and statistics. Retrieved from https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/apa_numbers_statistics.html

    Citations:
    [1] https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/apa_numbers_statistics.html
    [2] https://www.yourstatsguru.com/secrets/trans-statistics-in-apa-format/
    [3] https://www.pindling.org/Math/Statistics1/Textbook/Appendix/APA_Style.pdf
    [4] https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf
    [5] https://www.scribbr.com/apa-style/numbers-and-statistics/
    [6] https://nool.ontariotechu.ca/writing/references-and-citations/american-psychological-association/common-errors-in-apa-citation.php
    [7] https://blog.apastyle.org/apastyle/2011/08/the-grammar-of-mathematics-writing-about-variables.html
    [8] https://www.scribbr.com/apa-style/results-section/

  • Median

    The median is a measure of central tendency that represents the middle value in a data set when it is ordered from least to greatest. Unlike the mean, which can be heavily influenced by outliers, the median provides a more robust indicator of the central location of data, especially in skewed distributions (Smith, 2020). To find the median, one must first arrange the data in numerical order. If the number of observations is odd, the median is the middle number. If even, it is the average of the two middle numbers (Johnson & Lee, 2019). This characteristic makes the median particularly useful in fields such as economics and social sciences, where data may not always be symmetrically distributed (Brown et al., 2021).

    References

    Brown, A., Clark, B., & Davis, C. (2021). Statistics for social sciences. Academic Press.

    Johnson, R., & Lee, S. (2019). Introduction to statistical methods. Wiley.Smith, J. (2020).

    Understanding measures of central tendency. Journal of Applied Statistics, 45(3), 234-245.

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  • Mode

    The mode is a statistical measure that represents the most frequently occurring value in a data set. Unlike the mean or median, which require numerical calculations, the mode can be identified simply by observing which number appears most often. This makes it particularly useful for categorical data where numerical averaging is not possible. For example, in a survey of favorite colors, the mode would be the color mentioned most frequently by respondents. The mode is not always unique; a data set may be unimodal (one mode), bimodal (two modes), or multimodal (more than two modes) if multiple values occur with the same highest frequency. In some cases, particularly with continuous data, there may be no mode if no number repeats. The simplicity of identifying the mode makes it a valuable tool in descriptive statistics, providing insights into the most common characteristics within a dataset (APA, 2020).ReferencesAPA. (2020). In-text citation: The basics. Retrieved from https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/in_text_citations_the_basics.html

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  • Mean

    The mean, often referred to as the average, is a measure of central tendency that is widely used in statistics to summarize a set of data. It is calculated by summing all the values in a dataset and then dividing by the number of values. This measure provides a single value that represents the center of the data distribution, making it useful for comparing different datasets or understanding the general trend of a dataset. The mean is sensitive to extreme values, or outliers, which can skew the result and may not accurately reflect the typical value in a dataset. Therefore, while it is a valuable statistical tool, it should be used with caution, especially in datasets with significant variability or outliers (Smith & Jones, 2020).

    References

    Smith, J., & Jones, A. (2020). Understanding statistics: A guide for beginners. New York: Academic Press.

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  • Sampling

    Sampling is a fundamental concept in research methodology, referring to the process of selecting a subset of individuals or observations from a larger population to make inferences about the whole (Creswell & Creswell, 2018). This process is crucial because it allows researchers to conduct studies more efficiently and cost-effectively, without needing to collect data from every member of a population (Etikan, Musa, & Alkassim, 2016). There are various sampling techniques, broadly categorized into probability and non-probability sampling. Probability sampling methods, such as simple random sampling, ensure that every member of the population has an equal chance of being selected, which enhances the generalizability of the study results (Taherdoost, 2016). In contrast, non-probability sampling methods, like convenience sampling, do not provide this guarantee but are often used for exploratory research where generalization is not the primary goal (Etikan et al., 2016). The choice of sampling method depends on the research objectives, the nature of the population, and practical considerations such as time and resources available (Creswell & Creswell, 2018).

    References

    Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

    Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4.

    Taherdoost, H. (2016). Sampling methods in research methodology; How to choose a sampling technique for research. International Journal of Academic Research in Management, 5(2), 18-27.

    Citations:
    [1] https://guides.library.unr.edu/apacitation/in-textcite
    [2] https://www.scribbr.com/apa-style/in-text-citation/
    [3] https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/in_text_citations_the_basics.html
    [4] https://libguides.jcu.edu.au/apa/in-text
    [5] https://guides.library.uq.edu.au/referencing/apa7/in-text
    [6] https://aut.ac.nz.libguides.com/APA7th/in-text
    [7] https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/in_text_citations_author_authors.html
    [8] https://apastyle.apa.org/style-grammar-guidelines/citations

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  • Convenience Sampling

    Convenience sampling is a non-probability sampling technique where participants are selected based on their ease of access and availability to the researcher, rather than being representative of the entire population (Scribbr, 2023; Simply Psychology, 2023). This method is often used in preliminary research or when resources are limited, as it allows for quick and inexpensive data collection (Simply Psychology, 2023). However, convenience sampling can introduce biases such as selection bias and may limit the generalizability of the findings to a broader population (Scribbr, 2023; PMC, 2020). Despite these limitations, it is a practical approach in situations where random sampling is not feasible, such as when dealing with large populations or when a sampling frame is unavailable (Science Publishing Group, 2015).

    References

    Scribbr. (2023). What is convenience sampling? Definition & examples. Retrieved from https://www.scribbr.com/methodology/convenience-sampling/

    Simply Psychology. (2023). Convenience sampling: Definition, method and examples. Retrieved from https://www.simplypsychology.org/convenience-sampling.html

    PMC. (2020). The inconvenient truth about convenience and purposive samples. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC8295573/

    Science Publishing Group. (2015). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4. doi:10.11648/j.ajtas.20160501.11

    Citations:
    [1] https://www.scribbr.com/methodology/convenience-sampling/
    [2] https://www.simplypsychology.org/convenience-sampling.html
    [3] https://pmc.ncbi.nlm.nih.gov/articles/PMC8295573/
    [4] https://www.scribbr.com/frequently-asked-questions/purposive-and-convenience-sampling/
    [5] https://www.sciencepublishinggroup.com/article/10.11648/j.ajtas.20160501.11
    [6] https://dictionary.apa.org/convenience-sampling
    [7] https://www.researchgate.net/post/How-do-I-word-the-sample-section-using-convenience-sampling
    [8] https://www.verywellmind.com/convenience-sampling-in-psychology-research-7644374

  • Chi Square test

    The Chi-Square test is a statistical method used to determine if there is a significant association between categorical variables or if a categorical variable follows a hypothesized distribution. There are two main types of Chi-Square tests: the Chi-Square Test of Independence and the Chi-Square Goodness of Fit Test. The Chi-Square Test of Independence assesses whether there is a significant relationship between two categorical variables, while the Goodness of Fit Test evaluates if a single categorical variable matches an expected distribution (Scribbr, n.d.; Statology, n.d.). When reporting Chi-Square test results in APA format, it is essential to specify the type of test conducted, the degrees of freedom, the sample size, the chi-square statistic value rounded to two decimal places, and the p-value rounded to three decimal places without a leading zero (SocSciStatistics, n.d.; Statology, n.d.). For example, a Chi-Square Test of Independence might be reported as follows: “A chi-square test of independence was performed to assess the relationship between gender and sports preference. The relationship between these variables was significant, $$ \chi^2(2, N = 50) = 7.34, p = .025 $$” (Statology, n.d.).

    Citations:
    [1] https://www.socscistatistics.com/tutorials/chisquare/default.aspx
    [2] https://www.statology.org/how-to-report-chi-square-results/
    [3] https://ezspss.com/report-chi-square-goodness-of-fit-from-spss-in-apa-style/
    [4] https://ezspss.com/how-to-report-chi-square-results-from-spss-in-apa-format/
    [5] https://www.scribbr.com/statistics/chi-square-tests/
    [6] https://www.youtube.com/watch?v=VjvsrgIJWLE
    [7] https://www.scribbr.com/apa-style/numbers-and-statistics/
    [8] https://www.youtube.com/watch?v=qjV9-a6uJV0

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  • Correlation (Scale Variables)

    Correlation for scale variables is often assessed using the Pearson correlation coefficient, denoted as $$ r $$, which measures the linear relationship between two continuous variables (Statology, n.d.; Scribbr, n.d.). The value of $$ r $$ ranges from -1 to 1, where -1 indicates a perfect negative linear correlation, 0 indicates no linear correlation, and 1 indicates a perfect positive linear correlation (Statology, n.d.). When reporting the Pearson correlation in APA format, it is essential to include the strength and direction of the relationship, the degrees of freedom (calculated as $$ N – 2 $$), and the p-value to determine statistical significance (PsychBuddy, n.d.; Statistics Solutions, n.d.). For example, a significant positive correlation might be reported as $$ r(38) = .48, p = .002 $$, indicating a moderate positive relationship between the variables studied (Statology, n.d.; Scribbr, n.d.). It is crucial to italicize $$ r $$, omit leading zeros in both $$ r $$ and p-values, and round these values to two and three decimal places, respectively (Scribbr, n.d.; Statistics Solutions, n.d.).

    References

    PsychBuddy. (n.d.). Results Tip! How to Report Correlations. Retrieved from https://www.psychbuddy.com.au/post/correlation

    Scribbr. (n.d.). Pearson Correlation Coefficient (r) | Guide & Examples. Retrieved from https://www.scribbr.com/statistics/pearson-correlation-coefficient/

    Scribbr. (n.d.). Reporting Statistics in APA Style | Guidelines & Examples. Retrieved from https://www.scribbr.com/apa-style/numbers-and-statistics/

    Statology. (n.d.). How to Report Pearson’s r in APA Format (With Examples). Retrieved from https://www.statology.org/how-to-report-pearson-correlation/

    Statistics Solutions. (n.d.). Reporting Statistics in APA Format. Retrieved from https://www.statisticssolutions.com/reporting-statistics-in-apa-format/

    Citations:
    [1] https://www.statology.org/how-to-report-pearson-correlation/
    [2] https://www.scribbr.com/statistics/pearson-correlation-coefficient/
    [3] https://www.psychbuddy.com.au/post/correlation
    [4] https://www.statisticssolutions.com/reporting-statistics-in-apa-format/
    [5] https://www.socscistatistics.com/tutorials/correlation/default.aspx
    [6] https://www.scribbr.com/apa-style/numbers-and-statistics/
    [7] https://apastyle.apa.org/style-grammar-guidelines/tables-figures/sample-tables
    [8] https://www.youtube.com/watch?v=fCf0YYVLKTU

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  • Correlation Ordinal Variables

    Correlation for ordinal variables is typically assessed using Spearman’s rank correlation coefficient, which is a non-parametric measure suitable for ordinal data that does not assume a normal distribution (Scribbr, n.d.). Unlike Pearson’s correlation, which requires interval or ratio data and assumes linear relationships, Spearman’s correlation can handle non-linear monotonic relationships and is robust to outliers. This makes it ideal for ordinal variables, where data are ranked but not measured on a continuous scale (Scribbr, n.d.). When reporting Spearman’s correlation in APA style, it is important to italicize the symbol $$ r_s $$ and report the value to two decimal places (Purdue OWL, n.d.). Additionally, the significance level should be clearly stated to inform readers of the statistical reliability of the findings (APA Style, n.d.).

    References

    APA Style. (n.d.). Sample tables. American Psychological Association. Retrieved from https://apastyle.apa.org/style-grammar-guidelines/tables-figures/sample-tables

    Purdue OWL. (n.d.). Numbers and statistics. Purdue Online Writing Lab. Retrieved from https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/apa_numbers_statistics.html

    Scribbr. (n.d.). Pearson correlation coefficient (r) | Guide & examples. Scribbr. Retrieved from https://www.scribbr.com/statistics/pearson-correlation-coefficient/

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