Psychology Research Methods: Attitude Measurement and Errors Analysis

This report delves into the intricacies of research methods, specifically examining the challenges associated with attitude measurement and the different types of errors that can occur in research. The report begins by exploring the complexities of measuring attitudes, acknowledging their transient nature and the impact this has on data accuracy. It highlights the significance of accurate attitude measurement, especially in fields like marketing, where changes in attitude can significantly impact research outcomes. The report then differentiates between sampling and non-sampling errors, explaining their causes and effects on research validity. Sampling errors are attributed to the selection of the sample size while non-sampling errors are caused by inaccuracies in data collection. The report emphasizes that the errors significantly affect the accuracy of the research work. The report concludes by suggesting the use of advanced automation processes to minimize the errors, ultimately affecting the accuracy level within the research work.

Running head: RESEARCH METHODS
Research Methods
Name of the Student
Name of the University
Author Notes

1RESEARCH METHODS
a) Are attitudes too complex and transient to measure?
The purpose of the attitude scale is to accurately measure the values of individual outlook
in better accurate levels. The research methods are highly dependent on the behavior of the
sample groups, which are considered to be the essential elements in getting the added
information. The responses that are provided by the people within a community can vary
depending upon the attitude of individual. The complex nature of the attitude is mainly due to the
frequent changes that occur within the due course. High level of accuracy is needed in order to
measure the transient nature of social attitude. It will therefore be possible to bring about changes
in the data of the research work that will help to improve the authenticity and validity of the
same.
Investigation quality within the project depends upon the ability of the investigators to
predict the changes in the attitude. For marketing or business research the data analysis is mainly
dependent on the attitudes that are being measured in each case. This is believed to be highly
critical as the change in the behavior or attitude can have significant impact on to the marketing
research work. Hence, it is also not possible to have a generalized statistical parameter of
measuring the research attitude due to its frequent changing attitude. It is important to measure
the change in the attitude, which is also considered difficult. This is one of the challenging tasks
as the attitude of the people within the same sample group can vary hugely. The different type of
errors that includes the sampling and non-sampling errors are mainly caused due to the high level
of variance in the attitude of the samples.
b) Sampling errors V non-sampling errors

2RESEARCH METHODS
The sampling errors are the one that are caused due to estimation of result from the subset
within a population. This is mainly caused due to the fact that the investigators do not include all
types of people of community within the sample. This can result in higher level of errors within
the different research works that are conducted within the same community. This is due to the
difference in the attitude of public within the community. This error is mainly caused due to the
mistake in selection of the sample size. There is also chance that within the chosen sample many
of the respondents do no chose to provide any form of answers. Hence, with less volume of data,
the chance of error is quite high.
On the other hand, the non-sampling errors are caused due to difference within the value
of the data that are collected with that of the actual values. Unlike the sampling errors, the non-
sampling ones are not dependent on the sample size and the choice of sample. The sampling
error can be minimized but increasing the size of the sample. The non-sampling ones can be
decreased by improving the level of accuracy in the process of data collection. The chance of
detecting the non-sampling errors is quite low as it is not possible to measure the parameters of
accuracy. It is also virtually impossible for the investigators to eliminate the chance of
occurrence the non-sampling errors. Nevertheless, both types of errors ultimately affect the
accuracy level within the research work. It is also essential to make use of the advanced
automation process that is required minimize the errors that are caused due to non-sampling
ones.