Lab Report(Psychology) Spss.


Lab Report(Psychology) Spss.

Lab Report(Psychology) Spss.


this is very hard and complex psychology lab report, it is need spss to do data analyze, and also need to watch the recording. the details are in the PDF.

2500 words and APA formal references and in-text citation

very careful about the APA format data analyze( in the PDF: trying the DAI data together)

and also follow the rubric to write, Reasonable allocation of words for each part

  • attachmentPSY3RPBTutorial5-TyingtheDAItogetherstudentnotes6pp.pdf
  • attachmentSuggestedsteps1.docx
  • attachmentQuestionnairesScoring1.docx
  • attachmentPSY3RPBTelehealth200927STUDENT.sav
  • attachmentPSY3RPBTutorial8-DDPFinalReportupdatedstudentnotes6pp.pdf
  • attachmentPSY3RPBAnalyses1.docx
  • attachmentFinalReportInstructionsRubricandChecklistLB1.pdf
  • attachment4.PSY3PRAQuestionnaireVersion1.1200730CLEAN2.docx
  • attachmentrecording.docx 4PL^*aR^9Hru

Week 2 Recording

Password: LLw_f4bv!9m1

2. 1. PSY3RPB Week 7 Recording

Week 7 recording (in two parts)

Password: QvozdH#F2*GU

Password: VxM8Lw+Cr4!0

Week 8 Recording

Password: 6G-PcSPIxb^2

4. Week 9 Recording

Week 9 Recording

Password: dwgZ^U*C!2ZI

5. Week 10 Recording

Zoom Link:

Password: QSncQ3z7y-qV

Suggested steps


Note: Participant section in the Methods will include brief summary information about the completers (as these are the ones you will be using in your analyses).

Include Planned analyses section in the method. This should include how you are going to do the following (don’t report the results of these steps here, they go in the results section)

· How you got to original dataset, how you defined completers, comparisons of completers and non-completers

· Demographic and descriptive analyses

· Data cleaning e.g., univariate and multivariate outliers

· Assumption testing relevant to your hypotheses

· Analyses used to test hypotheses



1. Explain how got from original 395 cases downloaded from RedCap to those that are included in the dataset.

2. Define completers as those who did the whole questionnaire or not (you’ll need to explain this in the report)

Variable: consumer_perspective_survey_complete

3. Include a table of demographics for – I suggest a column for each

a. Sample as a whole

b. Completers

c. Non completer

 SampleNon-CompletersCompletersSignificant Differences
Female76%80%75%Χ2 (1) = 0.289
Age33.0 (15.1)29.3 (13.4)34.7 (15.6)T (248.9) = -3.33, p = .001
Rural/Remote(MMM Collapsed)    
English First Language    
Relationship Status    
Australian Born(Birth Collapsed)    
Post-High School Education (Education Collapsed)    
Working(Employment Collapsed)    
Physical Health Condition    
Mental Health Condition    
Rate Physical Health    
Rate Mental Health    

4. Comparing completers and non-completers – add this to the last column

a. Continuous – Independent t-tests

b. Dichotomous/Nominal – Cross Tab and Chi-square analyses

5. In the discussion need to comment on

a. Representativeness of the sample

i. Therefore, who this data/findings apply to/doesn’t apply to

b. Differences between completers and non-completers

i. Therefore, who this data/findings apply to/doesn’t apply to

SUGGESTION: Create a new dataset (save with a different title) that only includes those that completed the questionnaire, and use this for all further analyses.

Explain that all subsequent analyses are conducted using those participants for whom we have full data.

Other Descriptives

6. Report on percentage who have heard about telehealth psychology pre and post COVID19

7. Report on percentage who have accessed psychological treatment (face to face and telehealth)

8. Which of the other descriptive type variables you include in your report is really dependent on your research questions? That is, if you are going to use the items in your analyses, OR, if the response to these items provides important background information relevant to your analyses.


a) Percentage who have seen mental health professionals face to face and telehealth pre and post COVID19

b) Percentage who have received psychological treatment for various conditions face to face and telehealth pre and post COVID19

c) Percentage who have heard about telehealth psychology and where they heard it from pre and post COVID19

d) Responses to attitudes to telehealth questions (you might want to compare some of these answers using repeated measures t-tests)

e) Responses to technology access questions

9. Of course, you will need to include descriptive information for any variables that you plan to use in the analyses testing any of your hypotheses. There may also be variables that you use to describe your data (to give context to your hypotheses) but that won’t be analysed as part of your hypotheses testing.

Assumption testing

1. Most tests are robust to violations of normality assumptions in large samples (ours is large enough), and transforming variables can make it difficult to interpret findings.

a. Therefore, my preferred approach is that you check normality assumptions and comment on the outcomes, but that you do not transform variable unless there are extreme violations .

2. Do however check for outliers (univariate and multivariate) and remove/adjust outliers.

a. Use the histograms as your main guide for univariate outliers. I suggest adjusting those values that are clearly not part of the distribution to the next highest/lowest (as appropriate) score within the distribution.

b. Check for multivariate outliers as per instructions in tutorial 4. I suggest removing multivariate outliers (assuming there are not too many). NOTE always correct for univariate outliers first.

3. Also do check for assumptions for any analyses that you do for your hypotheses testing.


1. Hypothesis 1 ESSENTIAL

a. More favourable perceptions of telehealth psychology (PCT_Q_TOTAL) for

· Younger (age)

· Rural/remote (MMM_COLAPSED)

· Comfortable with computer (COMP_CONFCONT)

· Poorer Mental Health (DASS_TOTAL)

· More motivated for treatment (URICA_TOTAL)

b. Correlations to examine bivariate relationships

c. Linear regression to combined prediction of PCT_Q_FU (based on significant correlations)

2. Hypothesis 2 OPTIONAL

a. More likely to intend to use telehealth psychology (PCT_Q_FU) if more favourable perceptions of telehealth psychology






b. Correlations to examine bivariate relationships

c. Linear regression to combined prediction of PCT_Q_FU (based on significant correlation)

3. Hypothesis 3 OPTIONAL

a. Higher intention to use telehealth psychology post-covid19 if rating barriers low and facilitators high

· Select barriers and facilitators based on your literature review

b. Correlations to examine bivariate relationships

c. Linear regression to combined prediction of PCT_Q_FU (based on significant correlation)

4. Hypotheses of your own

· Everyone to have 2- 3 hypotheses. Everyone has hypothesis 1.

· You can also have

· Hypotheses 2

· A version of hypotheses 3

· Hypotheses 2 and one of your own

· A version of hypotheses 3 and one of your own

· Hypotheses 2 and a version of hypotheses 3

· Just one of your own (with option of some exploratory analyses)

· Two of your own

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