Likert Scale Assignment – understanding limitations of data collected !

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    ITECH 5500 Professional Research and Communication

    Likert scale: Likert scale is an ordered scale in which the responder selects an option according to their point of view. It is widely used and easy to use approach. It is used in survey research.

    Format for the Likert scale:

    Strongly disagree / Disagree / Unsure / Agree / strongly agree

    Now consider a situation in which 100 customers provide a response to this statement and they are distributed as follows

     1 – Strongly Disagree 10 (10%)

    2 – Disagree 25 (25%)

    3 – Unsure 17 (17%)

     4 – Agree 32 (32%)

    5 – Strongly agree 16 (16%)

    Let’s summaries this data into a single score. They calculate this score by multiplying the number of responses by the number assigned, adding the five together and dividing by the number of respondents.

    In this case, (10 + 50 + 51 + 128 + 80) / 100 = 319/100 = 3.19

    This 3.19 value is not acceptable because the value means the result between the “unsure” and “agree”. But there is huge difference between the result “Unsure” and “Agree”. So it is not acceptable. Or we can say limitation of Likert scale. There is less difference between the strongly disagree and disagree similarly with the case agree and strongly agree. But the result unsure and agree having huge difference. It is not an optimal interpretation. Same as with the case “Disagree” and “Unsure”. In this case we only report the number of response to the each option.

    There are four levels of measurement. From lower to highest NOIR (Nominal, ordinal, interval, ratio)

    Nominal scale: nominal level is measured and distinguished by the name. The binary form 0 and 1 is the measurements of the nominal scale. Examples: Gender, religion etc.

    Ordinal scale: if we want the measurements in order then ordinal scale is used. In this scale one value is greater than other. Size is not specified in this. Examples: ranking scale, likert scale etc.

    Interval scale: if we want to measure in terms of equal intervals or in terms of degrees. Examples: temperature in Celsius and Fahrenheit.

    Ratio scale: it is same as interval scale except it takes the absolute zero point. Example: temperature in Kelvin, bank account details.

    Gender is collected on the nominal scale because it will have only two responses 0 for male and 1 for female.

    Fahrenheit thermometers: interval scale

    Kelvin thermometers: ratio scale. Because it has the 0 value as well and ration scale takes the zero value.

    The number of items a customer buys: ordinal scale

    Bank account details: ratio scale because the bank account can be negative and positive it can also be 0 so it comes under the ratio scale.

    Experimental study: it is a clear hypothesis; the purpose of this research is the clear result with valid experiments. It has an independent variable, a dependent variable and a control group.these experiments are performed in the laboratory.

    Non experimental study: this research is done in naturally. It gives an overall picture of examing degree or type of this relation

    Type of non experimental

    1 Pure descriptive
    2 Correlation descriptive.
    3 Others
         -Needs assessment
         -Historical research

    In case of drinking orange juice three times per day, for four days each week, might make the players perform better in the game at the weekend is an experimental research study, because it is defined under control conditions and keen observation. It experiments the effect of independent variable on the dependent variable.


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