Your Paper was well written, however; I need you to follow the f

Your Paper was well written, however; I need you to follow the
following Analysis Guidance for Intervention Data. I will give
you a passing grade when you submit with these by the 26th of
April at 1pm EST
This document is designed to provide a summary of the key
steps for analysing intervention data. The main analysis is
conducted using the general linear model function in SPSS. This
document does not cover how to clean data for analysis. (Data
for the PARS module has already been cleaned so students do
not have to undertake this part of the analysis.) This document
is written with the PARS assignment in mind, so please refer to
statistical texts for details on how to check assumptions, and a
broader overview of how to interpret the output of intervention
analyses in SPSS.
Preparing Scales
When using scales, ensure you compute scale reliabilities
(Cronbachs Alpha using the function Analyse>Scale>Reliability
analysis). Make sure scales are recoded as required by the
specific scale you’re using. If you find poor reliability, that
might indicate scale items have not been coded as required (e.g.
a scale item may need reverse coding). If scale reliability is
poor, then you may want to exclude it from the analysis, remove
a low-loading item, or report why you think the reliability is
poor and justify why you decided to include it. Scale items
should be aggregated or averaged using the compute variable
function in SPSS (Transform>Compute variable) for the main
analysis, as directed by the scale authors. (For the PARS
assignment, scale reliability statistics can be reported in the
appendix.)

Calculating Means and Standard Deviations
It is useful at this stage to calculate the means and standard
deviations for the data using the function Analyse>Descriptive
Statistics. For intervention data comparing more than one
condition, you need to isolate a condition in the dataset before
generating the means and standard deviations for that condition.
The analyses testing the effect of an intervention with
individuals in different conditions (i.e. between-subject) are
essentially testing whether there is a significant difference in
the means of groups in different conditions. The means for the
different conditions show whether levels are increasing or
decreasing, and this is useful for interpreting the results of the
analysis.
Isolate study conditions using the function Data>Select cases,
and use the function ‘If condition satisfied’. In the PARS data,
use cohort as the variable in the rule (i.e. ‘Cohort = 1’ for the
intervention group, or ‘Cohort = 2’ for the control group). When
you have either of these rules applied, SPSS will only run the
analysis on the cases selected by that rule. For example, if the
rule applied is ‘Cohort = 1’ only cases with the value 1 in the
cohort variable will be included in the analysis.
Bivariate Correlations
As part the analysis, you need to run bivariate correlations. Use
the function Analyse>Correlate>Bivariate. (For the PARS
assignment, this can be reported in the appendix). Significant
bivariate correlations highlight relationships between the
variables. This in itself may be of interest, but it also identifies
variables to include as control variables in the main analysis.
Demographic variables that correlate significantly with outcome
variables can be included as covariates to partition out variance
they account for.

Testing the Overall Effects of an Intervention
The first step in the main analysis is to conduct an overall
MANOVA for all study items (and control variables) using the
function Analyse>General Linear Model>Repeated Measures.
(In the PARS assignment, the data have two conditions, with
two main outcome variables with measures taken at four time
points. Note that other study designs will require a different
analysis, i.e. within-subjects designs vs. between-subjects
designs, or repeated measures vs. cross-sectional.) What this
analysis is telling you is whether there is an overall effect from
the intervention across all time points and all outcomes.
The within-subject factor is the condition that all participants
experienced. In the PARS assignment data this is time, with
four levels to represent the four different time points in the
data. Each outcome measure should be added. The between-
subject factor is the condition that differed in participant
groups. In the PARS assignment data this is cohort. To control
for any demographic variables that correlated significantly with
the outcomes, add them as covariates.
As well as reporting the F statistic and the p-value, it is
becoming increasingly important to report and discuss the effect
size when interpreting data. This is especially true when
looking at the impact of interventions, as a significant result
does not necessarily indicate a meaningful effect size. SPSS
does not automatically calculate this for you, so ensure you
select estimates of effect size from the options tab. You can
also generate a plot in the output. Click on the plots tab and
have your between-subject factor as separate lines (to represent
the changes in different conditions) and your within-subject
factor (in the PARS case this represents the different time
points) as the horizontal axis. Don’t forget to click add when
doing this, otherwise your plot will not be generated. When
looking at the output, the main points of note can be found in

the between-subject effects shown in the multivariate tests
table. If there is no overall significant effect, you would have
little justification to continue with the analysis past this point,
as you have no evidence that the intervention has had an overall
effect.
Testing the Effects of the Intervention on Specific Outcomes
The second step is to conduct separate MANOVAs for each of
the study outcome variables. This essentially replicates the
procedure above, but specific to each outcome. What this
analysis is telling you is whether there is an overall effect from
the intervention in this outcome across all time points and
conditions. Use the function Analyse>General Linear
Model>Repeated Measures, but include only one of the outcome
variables as your measure. Include as covariates any
demographic variables that significantly correlated with that
outcome. When looking at the output, the main points of note
are shown in the multivariate tests table in the factor*cohort
section.
Testing for Simple Effects
For outcomes that have a significant overall effect, you can then
run simple effects tests to isolate where significant changes
occur. You can run simple effects tests for between-subjects
effects (comparing different groups at the same point in time),
and within-subjects effects (for each group separately
comparing changes over time). Don’t forget to use a Bonferroni
corrected p-value when interpreting these statistics. This is
because you are running multiple analyses, and to avoid inflated
error you need to use a more stringent test of significance for
the results.
To explore between-subjects effects, use the function
Analyse>General Linear Model>Univariate to run a one-way

repeated-measures ANOVA. (We discussed in class whether to
use t-tests or ANOVA, as they do a similar analysis. Both are
acceptable, but ANOVA is more robust.) Enter your time point
as the DV and your between-subject factor (cohort) as the fixed
factor. If you are using a time point after time 1, then enter the
previous time point as a covariate to isolate the effects that
occur after that point in time. For example, if you are looking at
effects at T3, then add T2 as a covariate to isolate in the
analysis changes between T2 and T3 only. The line of interest
in the output is that of your fixed factor (in this case cohort).
To explore within-subject effects, you need to isolate a
condition in the dataset before running the analysis. Do this
using the Data>Select cases function, and use the ‘If condition
satisfied’ function. In the PARS data, use cohort as the variable
in the rule (i.e. ‘Cohort = 1’ for the intervention group, or
‘Cohort = 2’ for the control group).
To run the within-subject simple effects tests, use the function
Analyse>General Linear Model>Repeated measures. The
within-subject factor is time. The PARS data has four levels, to
represent the four time points. As with the prior MANOVAs, the
between-subject factor in the PARS assignment data is cohort.
In this part of the analysis we will use the Contrasts function.
Once you have clicked on this, you will notice a section titled
‘Change Contrast’. Click on ‘contrast’ and you will have a list
of options. Each compares time points in a different way. The
two for this analysis are ‘simple’ and ‘repeated’. For the simple
contrasts, you can then swap between ‘first’ and ‘last’. Don’t
forget to click ‘change’ when swapping between any of the
contrast options. A summary of these contrasts is below:
· Simple(First) compares the first time point (T1) to all the
following time points
· Simple(Last) compares the last time point (T4) to all the

preceding time points
· Repeated compares the time points sequentially (i.e. T1 to T2,
T2 to T3, and T3 to T4).
The line of interest in the output is factor in the tests of within-
subjects contrasts table.
*****************************************************
*****************************************************
*****
End of feedback
Institute of Management Studies (IMS)
Postgraduate Assignment Information Sheet
Module title:
Professional and Applied Research Skills – Assignment 2
Assignment information (e.g., background info, assignment
question, further advice):
This assignment involves an evaluation of an intervention run
in an organisation. In this assignment you are asked to
critically evaluate an intervention using a strategic and
evidence-based approach to evaluation.

One of the specialist skills of occupational psychologists is
that of evaluating the effectiveness of interventions or
processes that they, or others, have delivered. Psychologists
rely on evaluation work to inform many of the choices they
make in their practice. You are expected to demonstrate that
you can conduct a robust evaluation of practice. Evaluation
can be carried out during a project, at the end of a project and
some considerable time after it has finished (i.e. as a long-
term follow-up). In this assignment, data are provided for you
to analyse, based on a real-world intervention.
In this assignment, you will analyse data measuring the impact
of a coaching intervention designed to improve employee
performance and wellbeing. The audience is the organisations
senior team, who are highly competent and intelligent, but
have little psychology-specific knowledge. Therefore, think
about how to communicate effectively with this audience.
You will need to include some psychological theory in your
report, and reference appropriately in APA format, but think
carefully about balancing this with the other information
required in the report.
About the Intervention
Data were collected from an ACT-based coaching intervention
for senior managers in the organisation. The study design was
a randomised controlled trial (RCT) design, which compared
ACT-informed coaching to a waitlist control group. Surveys
were distributed by email. Survey 1 was sent by email one week
before the first coaching session (Time 1) and provided a
baseline measure. Survey 2 was sent one week before the
second coaching session (five weeks after the baseline
measure; Time 2). Survey 3 was sent one week before the third
coaching session (nine weeks after the baseline measure; Time

3). Survey 4 was sent four weeks after the third coaching
session (14 weeks after the baseline measure; Time 4). An
online research randomiser tool (www.randomizer.org) was
used to randomly allocate coachees to either the experimental
group or the waitlist control group.
Of the 127 participants in the sample, 90 (71%) were female,
and 94 (74%) participants described their ethnicity as white.
There was an age range of 26 to 60 years (mean age of 41.47
years). On average, coachees had worked in their job for
between 3-4 years (mean of 3.27 years). Of the 127 coachees,
116 (91%) were educated to degree level or above.
Coachees’ had three face-to-face 90-minute coaching sessions
delivered over a period of nine weeks. The aim of the first
session was to (a) introduce the coachee to ACT-informed
coaching, and the strategies ACT approaches employ, (b)
identify core work values for the coachee, (c) identify goals
for the coachee to work on during the coaching programme,
and (d) introduce the coachee to mindfulness practice. The
three main exercises used in the session were a values
clarification exercise, a goal-setting process, and a short
mindfulness practice. Participants were asked to practice
mindfulness between coaching sessions: Two mindfulness
practices were discussed in the coaching session, and then
emailed to participants following the session.
The aim of the second coaching session was to (a) review
progress towards the coachee’s goals, (b) review the use of
mindfulness since the previous session, and (c) introduce
defusion and acceptance as ways of moving past psychological
blocks to progress. There were three main exercises used in
the session: A mindfulness exercise focused on defusing the
coachee from their thoughts, feelings and physical sensations;
a defusion and acceptance exercise focused on moving beyond
psychological barriers to coachees goal progress; and a

metaphor designed to increase the coachees willingness to
experience difficult thoughts and emotions in relation to their
goals. Participants were asked to use mindfulness practices
between sessions, and practice using the defusion, acceptance,
and willingness exercises if they noticed psychological blocks
to progress. Copies of these exercises were emailed to
participants after the coaching session.
The aim of the final session was to (a) review progress
towards the coachee’s goals, (b) introduce the observing
perspective (i.e. self-as-context perspective), and (c)
encourage coachees to keep working towards their goals and
increase their values consistent actions. There were two main
exercises used in the session: A mindfulness exercise focusing
on the observing perspective; and a values consistency
exercise, which asked coachees to reflect on what they are
doing day-to-day to live their values, where the
inconsistencies with their values are, and what else they might
be able to do to bring their values to life. Copies of these
exercises were emailed to participants after the coaching
session. Following completion of the final survey, participants
were emailed a handout with information to help participants
move forward with their gaols and values following the
coaching programme. This included (a) a short mindfulness
practice; (b) a life values clarification exercise; (c) tips and
suggestions for facilitating values-based living; (d) a
resilience enhancing exercise; and (e) resources for learning
more about ACT.
Performance was measured using the individual performance
items from the Model of Positive Work Role Behaviours
(Griffin et al., 2007). This scale is based on a theoretically
derived model of performance, focusing on an individual’s
proficiency, adaptivity, and proactivity at work. All items are
rated based on how often participants have carried out the
behaviour over the past month on a scale ranging from 1 (very

little) to 5 (a great deal). Responses were collected as a self-
report from participants. The scale consists of nine items.
Participants were asked to rate how often they had carried out
each behaviour over the past month on a scale ranging from 1
(“very little”) to 5 (a “great deal”). An example self-report
item for this scale is “Completed your core tasks well using
the standard procedures”. Higher scores indicate higher
performance.
General mental health was measured using the General Health
Questionnaire (GHQ-12; Goldberg, 1992). This scale is a
measure of current mental health; specifically the inability to
carry out normal functions, and the appearance of new and
distressing experiences. It consists of 12 items. An example
item from this scale is “have you recently felt capable of
making decisions about things?” Items are scored 0 (more so
than usual) to 3 (much less than usual). Scores have been coded
so higher scores indicate increased general mental health.
The Report
You need to submit a report evaluating the coaching
intervention. Specifically within this report you need to:
Specify the purpose and aim of the intervention being
evaluated.
Identify and critically evaluate the quality of the data
available for the evaluation.
Identify the practical constraints impacting on the
evaluation process.
Select appropriate evaluation processes and techniques.

Critically evaluate the quality of the evaluation
methodology (e.g. reliability, validity and sensitivity of the
data, sample size, timing of evaluations, rigour and
appropriateness of analysis etc.).
Evaluate outcomes systematically against objectives using
appropriate tools and analytical techniques.
Reach appropriate evidence-based conclusions about the
outcome of the intervention (e.g. what worked, in what
ways, for whom).
Reflect on the outcomes of the evaluation and the
implications of these for future evidence-based and
evidence-informed practice.
Provide advice and guidance to stakeholders based on the
results of the evaluation.
The report should follow this structure below:
· Title
· Executive summary
· Contents page
· Introduction
· Main body
· Conclusions and recommendations
· References

· Appendices
Key/suggested references:
Barends, E., & Rousseau, D. M., (2018).
Evidence-Based Management: How to Use Evidence to Make
Better Organisational Decisions.
Kogan Page Limited; New York, NY.
Books on statistical analysis, such as Field (2017) will be a
helpful guide to running statistical analysis for this assignment.
Field, A. (2009). Discovering statistics using SPSS. London:
SAGE Publications.
Support for this assignment:
Support for this assignment will be provided throughout the
course, with the following specific support sessions:
· 3x workshops on evidence-based practice and intervention
evaluation, including two case studies.
· 1x workshop on progressing your report, with the
opportunity for feedback from tutors on your progress so far.
This formative workshop is aimed at helping students to
progress their ideas, and get helpful feedback before
submitting their final reports
Word limit:

1,500 words
.
This
includes
the main body of text, in text citations [e.g. (Eisen et al.,
2008)], quotations and footnotes. However, the word limit
excludes
your title page, tables, figures, illustrations, reference list and
appendices.
Referencing style
APA
Submission date and time:
11th March at noon
Marking scheme:
Marking will be in accordance with the general IMS
postgraduate marking rubric:
1. Answer. (Does the work answer the question or address the
issue?)

2. Structure. (Is the general structure of the work coherent?)
3. Flow. (Does each statement follow sensibly from its
predecessor?)
4. Argument. (Is there a convincing quality of argument in the
work?)
5. Evidence. (Are claims supported by relevant evidence from
the literature?)
P.S. The other file can be opened by IBM SPSS Statistics
Software

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