Methods & Issues in Research and Practice : Quantitative Analysis Assignment: TPB Smoking & GP

Quantitative assignment

The quantitative assignment is worth 50 per cent of the module marks. Using the datasets provided, complete two analytic techniques from the three main techniques of Regression and ANOVA.

Produce an independent write-up of two of these types of analysis in a style that would be compatible with the results section of a peer-reviewed psychology journal (using APA 7th edition style).

It’s just the results section that you are asked to write in that style for a particular psychology journal. The object of the assignment is to allow you to demonstrate your competence and knowledge of using and reporting on these data analysis techniques and your understanding of some of the subtleties of reporting such analyses in the results section of a report or academic paper using APA academic referencing and reporting style.

Each analysis has a word limit of 750 words, excluding the assignment cover page, tables, figures, the reference section at the end and SPSS syntax appendices.

Some things to remember about the assignment – there is an expectation at this level (of study), that you will independently source information as well as use the information provided within the module content.

Points to note

• The assignment should be submitted as one document and include a reference list at the end of the whole assignment. The reference section should be in APA 7th edition referencing style.

1.REGRESSION ANALYSIS

Background

The Theory of Planned Behaviour (TPB) has received considerable attention in the psychological literature. The TPB posits that individual behaviour is driven by behaviour intentions and that behaviour intentions are in turn a function of three main determinants: an individual’s attitude toward behaviour, subjective norms, and perceived behavioural control (Ajzen, 1991).

Armitage & Conner’s 2001 review of the TPB stated that the theory accounted for 27% and 39% of the variance in behaviour and intention, respectively. In addition, they found that the perceived behavioural control construct accounted for significant amounts of variance in intention and behaviour, independent of the Theory of Reasoned Action variables.

The dataset

The data set for analysis is taken from an opportunistic survey of current adult smokers aged 18-69 years (N=274). The respondents were asked to complete a series of questions relating to each of the main TPB constructs. Each construct contained 5 items. The item scores were then used to compute summated TPB construct scores labelled (ATTITUDE, SUB_NORM, PERCEIVED_B_CONTROL, AND INTENTION). (Note: For simplicity, the original items are not included in the dataset).

Type of analysis

Conduct and report upon a hierarchical linear regression analysis using the demographic variables (age and sex) and all the TPB variables (ATTITUDE, SUB_NORM, PERCEIVED_B_CONTROL) as predictors of INTENTION scores (In this case, INTENTION refers to intentions to quit smoking).

You should also report briefly on whether the main assumptions of linear regression have been met (e.g. homoscedasticity, no collinearity among predictors, no extreme outliers).

*Also do this*
If you want to add interaction terms as predictors (e.g. then see the additional conceptual papers by LaBarbera & Azjen, 2021; Yzer & van den Putte, 2014).

Resources

• The SPSS data for analysis is called ‘Regression_1.sav’

Additional Guidance

A separate discussion section is not required for each analysis as this would substantially increase the word count for the assignment.

You will have some space to provide some commentary within the results section if you structure the Results section as follows:

1. A preliminary section describing the analysis carried out that would enable a reader to replicate the analysis using the same data. Such a description would typically be seen in the Method section under a Data Analysis subsection or in the Results under a similar Data Analysis sub-section.
2. The substantive results. (Summary tables are a good idea here as well and do not add to the word count).
3. A description of the data in terms of whether the data meets the main assumptions for that type of data analysis. You can also comment briefly on the limitations/strengths of the data and the analysis performed (so this bit technically would appear in a Discussion section in the context of a broader discussion about hypotheses, future research etc

2. ANOVA ANALYSIS

ANALYSIS OF VARIANCE ASSIGNMENT OUTLINE

Background

This study involves assessing the efficacy of a GP community intervention programme aimed at improving physical activity levels of patients aged 18-35 years with recorded BMI levels in the range 30-35.

The study design involved taking measurements from all participants (N=45) on two occasions (at baseline prior to offering any intervention and after 3 months).

Respondents were allocated to one of two conditions before baseline measurements were taken.

Group 1 (control group N=22) received general advice from their GPs along with information leaflets on the benefits of physical activity and regular appropriate exercise.

Group 2 (the intervention group) likewise received general advice from their GPs on weight loss along with information leaflets on the benefits of physical activity and regular appropriate exercise. In addition, group 2 were asked to attend a range of timetabled weekly exercise classes for a period of 3 months (N=23).

The patients who were allocated to the control condition were subsequently offered the intervention at the end of the three-month period.

Primary Measure

The International Physical Activity Questionnaire (IPAQ) is commonly used to assess reported levels of physical activity and inactivity (Papathanasiou et al., 2010]. The short-form version of the IPAQ is a self-administered questionnaire that asks respondents to recall physical activity in the previous seven days (Stanford et al., 2015). Responses are then converted to MET-minutes with higher scores indicating greater levels of energy expenditure.

The IPAQ responses were used to calculate MET-minutes scores for each respondent.

All respondents were invited to complete IPAQ-SF questionnaires before and immediately after the 3-month time period.

Type of analysis

Option 1: Use the Analyze > General Linear Model > Univariate command in SPSS.
Conduct and report upon an analysis of covariance using Post_MET scores at 3 months as the dependent variable. Use GROUP as the independent variable and Pre-MET scores as a covariate.

Option 2: Use the Analyze > General Linear Model > Repeated Measures… command in SPSS.
Conduct and report on a MIXED ANOVA using MET scores at baseline and MET scores at 3-months as a repeated measures variable and GROUP as the independent variable.

Notes:

• Either analysis option is acceptable but you should only report on one of them.
• Sex of respondent is also included in the datafile. You may use this variable if you wish but use of this variable is not strictly required.

Resources

• The SPSS data for analysis is called ‘METS_1.sav’

References to psychometric scale

• Papathanasiou, G., Georgoudis, G., Georgakopoulos, D., Katsouras, C., Kalfakakou, V., & Evangelou, A. (2010). Criterion-related validity of the short International Physical Activity Questionnaire against exercise capacity in young adults. European Journal of Preventive Cardiology, 17(4), 380-386.
• Stanford, F. C., Durkin, M. W., Blair, S. N., Powell, C. K., Poston, M. B., & Stallworth, J. R. (2012). Determining levels of physical activity in attending physicians, resident and fellow physicians and medical students in the USA. British Journal of Sports Medicine, 46(5), 360-364.