3003PSY-Individual Differences and Frequency of Technology – Statistics Assignment Help


Investigation of the relationships between attitudes toward technology, individual differences and frequency of technology use 

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Context for the research:
Society utilizes technology in almost all aspects of living including social interactions, medicine, education and vocation. Engaging with technology has almost become a requirement of daily living. Given how pervasive technology has become, individuals that are reluctant to engage with technology, or avoid technology may be subject to a host of negative outcomes. For example, individuals who refuse to utilize technology may experience greater social isolation and loneliness as they are unable to engage with friends, peers and colleagues via technology-mediated communications (e.g., phone calls, text messaging).

Additionally, individuals who avoid technology may experience difficulty in their education and workplace given that communication between students, and between colleagues is heavily mediated by technology. Finally, the avoidance of technology may be related to adverse mental and physical health outcomes, particularly given the recent increase in telehealth and internet-delivered psychotherapies and medical interventions. Some researchers have become keenly interested in the factors associated with attitudes toward technology. Their primary motivations have been to identify these factors so that they may develop educational materials targeted at fostering more positive attitudes toward technology. Technology in this instance refers to devices such as computers (e.g., PC or Mac), smartphones (e.g., iPhone, Android phones), tablets (e.g., iPad, Kindle) and other electronics including Smart TVs. Technology also refers to software and/or apps, for example, Microsoft Word, Safari, Google Chrome, and Instagram.

Much of the current literature looking at attitudes toward technology is characterized by a reliance on correlational studies. Research has found attitudes toward technology to be associated with gender, age, technology use and personality. For example, there is evidencethat age is negatively correlated with technology use and attitudes toward technology (CruzCárdenas et al., 2019). This relationship suggests that older individuals engage with technology to a lesser degree and have more negative attitudes toward technology. There is also research to suggest that negative attitudes toward technology are more common in women (Reich-Stiebert, & Eyssel, 2015). From a behavioural perspective, the frequency with which individuals engage with technology has also been shown to be an important correlate with attitudes toward technology (Nimrod, 2018). Research suggests that Individuals who frequently engage with technology tend to have more positive attitudes toward technology.

With respect to specific personality differences, there is research demonstrating that the personality factor openness to experience from the Five Factor model of personality is correlated with attitudes toward technology. Individuals high in openness tend to have active imaginations, have a preference for variety, are intellectually curious and seek novel experiences. The correlation between Openness to Experience and attitudes toward technology has been positive (Anthony et al. 2000). That is to say, individuals high in openness to experience tend to have more positive attitudes toward technology. Another aspect of personality associated with attitudes toward technology is Technology Self-Efficacy (TSE). TSE is the belief in one’s ability to perform novel technologically complex tasks successfully. Research suggests that individuals with high TSE typically hold more positive attitudes toward technology and tend to engage with technology more than individuals with low TSE (Holden & Rada, 2011).

The current study
While the existing literature has begun to identify individual factors correlated with attitudes toward technology, no research exists testing a larger model, where these factors are brought together to examine differences in attitudes toward technology. By examining these factors together in their association with attitudes toward technology, we will gain a better understanding of the unique and shared associations between each factor and the outcome variable. To that end, a researcher has run a study collecting data from a sample of adults in the community and asked them to complete an online survey. The survey comprised a number of questions, and as is clear in the coding book (attached to this .pdf) and the associated data file, not all variables are of interest to the immediate research question. 

In this assignment you will be tasked with testing the following research question:
What are the associations between gender, age, technology self-efficacy, intellect/imagination and daily technology use on self-reported attitudes toward technology?

To do this you will work with the dataset provided. You will need to identify the variables thatare relevant to answering your research question and use the procedures and analyses as taught in this course. You will respond to a series of questions that you would work throughwhen running a research study.


  • The total marks for this assignment are 80, and equal to 40% of the course grade
  • You do not need to do any research in this area. The references in the questionnaires are provided at the end of this document in the coding book section
  • The assignment does not require a literature review
  • The assignment does not require an abstract or reference list
  • You have a maximum word limit of 2000 words, but may not need this many to complete the assignment
  • Use APA 7th format where appropriate, including tables
  • You will need to use the skills developed in the tutorials (weeks 2 to 8) to run the analyses
  • Consultation with the online resources provided will also be helpful for conceptually understanding the analyses required to answer the research question

You will need to:

  • Identify the relevant variables in the data set required to answer your research question
  • Recode the variables into a meaningful format to use in your selected analysis
  • Compute the relevant scale scores to use in your selected analysis
  • Compute relevant dummy coded variables
  • Conduct logic checks on data to identify the presence of data entry errors
  • Answer the questions below

Answer the following questions: 

1. What are the constructs of interest in the RQ? 
2. How are each construct operationalised? 
3. Based on the information provided on page 1 state the hypothesised associations ?
Write a sentence for each variable hypothesizing the expected effects for each predictor and the outcome variable in the model
Write this as you would at the end of an introductory paragraph.

4. What are the internal reliabilities (Cronbach alphas) of the scale scores?
You will need to calculate these as per the tutorial exercise

5. Do the scales have acceptable reliability?
What criteria have you based your decision on?

6. Write a brief statement describing each of the measures used
This is effectively a measures section of a psychological research report and is to be presented in sentence/paragraph format as you would in a report.
Report each variable in your dataset and how they are measured by stating:

  1. the measure (with reference if applicable),
  2. scoring details,
  3. Cronbach’s alpha (from this dataset as calculated above), and
  4. Did you create a composite scale or summed total? provide possible min and max scores.

7. Report the following information in the appropriate table:
Mean and SD of the continuous variables
The correlations between all predictor variables, and the outcome variable
Make sure to follow APA 7 formatting

8. Perform the appropriate checks of the relevant assumptions and address the following:
a) Report on the distribution of the residuals
Would they suggest any problems? why or why not?
What criteria did you use to determine if there were any problems?

b) Are there any univariate outliers (only look for these on continuous level data – ignore frequency data)?
Provide details of any univariate outliers (e.g., which variables have outliers and how many)
What criteria did you use to determine univariate outliers?

c) Check for multivariate outliers
Were there any multivariate outliers?
What criteria did you use to determine multivariate outliers?

9. Are any of the scales considered significantly skewed?
a) Report the skewness for all continuous variables in the dataset (including direction; use the computed scale scores not the raw scores)
What criteria did you use to determine skew?

b) Perform the appropriate transformations on relevant variables
Which variables require transformations?
Which transformations did you use?
What was the effect of the transformation/s (e.g., did the transformation help with skew? Were the transformed variables kept in the model, or are the original variables used)

10. Write a paragraph outlining your testing of assumptions and how you addressed any violations
Write this section as taught in the tutorials and is to be in sentence/paragraph format

11. Perform the appropriate analysis to answer the RQ and answer the following:
a) How many participants were in the final analysis?
Did you remove any data entry errors?
Did you need to remove any influential datapoints?
What was your justification for your decision for removing (or keeping) datapoints?

b) Did you use the transformed or non-transformed data in the final analysis?
State and justify your decision for each variable

12. Report the results from your analysis
Write a paragraph reporting your results addressing the RQ.
Use the example covered in the tutorials
Use the appropriate table to report the multiple regression

13. What were the variances explained?
report the overall variance explained
report the unique variance for each variable in the analysis
report the shared variance

14. Make a conclusion of your results
Write a paragraph interpreting your results in the context of the RQ
Were the hypotheses supported?
Note the variance accounted incl unique and shared – what does this mean?
Discussion any limitations as to the data? Of the analyses?
What can the researcher conclude?

15. Adherence to APA formatting (where relevant, e.g., Tables, reporting of measures, results)

16. Output from final analysis attached as Appendix 

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