easypackages::packages("tidyverse", "here", "GGally", "ggthemes", "rstatix", "kableExtra", "ggthemes", "magrittr", "ggrepel", "tippy", "janitor", "ggpubr", "epoxy")

theme_alan <- function(base_size = 12 , base_family = "")
{
  half_line <- base_size/2
  colors <- ggthemes_data$few
  gray <- colors$medium["gray"]
  black <- colors$dark["black"]
  
  theme(
    line = element_line(colour = "black", size = 0.5, linetype = 1, lineend = "butt"),
    rect = element_rect(fill = "white", 
                        colour = "black", size = 0.5, linetype = 1),
    text = element_text(family = base_family, face = "plain", colour = "black", 
                        size = base_size, lineheight = 0.9, hjust = 0.5, vjust = 0.5,
                        angle = 0, margin = margin(), debug = FALSE),
    
    axis.line = element_blank(),
    axis.line.x = NULL,
    axis.line.y = NULL, 
    axis.text = element_text(size = rel(0.8), colour = "grey30"),
    axis.text.x = element_text(margin = margin(t = 0.8 * half_line/2), vjust = 1),
    axis.text.x.top = element_text(margin = margin(b = 0.8 * half_line/2), vjust = 0),
    axis.text.y = element_text(margin = margin(r = 0.8 * half_line/2), hjust = 1),
    axis.text.y.right = element_text(margin = margin(l = 0.8 * half_line/2), hjust = 0), 
    axis.ticks = element_line(colour = "grey20"), 
    axis.ticks.length = unit(half_line/2, "pt"),
    axis.title.x = element_text(margin = margin(t = half_line), vjust = 1),
    axis.title.x.top = element_text(margin = margin(b = half_line), vjust = 0),
    axis.title.y = element_text(angle = 90, margin = margin(r = half_line), vjust = 1),
    axis.title.y.right = element_text(angle = -90, margin = margin(l = half_line), vjust = 0),
    
    legend.background = element_rect(colour = NA),
    legend.spacing = unit(0.4, "cm"), 
    legend.spacing.x = NULL, 
    legend.spacing.y = NULL,
    legend.margin = margin(0.2, 0.2, 0.2, 0.2, "cm"),
    legend.key = element_rect(fill = "white", colour = NA), 
    legend.key.size = unit(1.2, "lines"), 
    legend.key.height = NULL,
    legend.key.width = NULL,
    legend.text = element_text(size = rel(0.8)), 
    legend.text.align = NULL,
    legend.title = element_text(hjust = 0),
    legend.title.align = NULL,
    legend.position = "right", 
    legend.direction = NULL,
    legend.justification = "center", 
    legend.box = NULL,
    legend.box.margin = margin(0, 0, 0, 0, "cm"),
    legend.box.background = element_blank(),
    legend.box.spacing = unit(0.4, "cm"),
    
    panel.background = element_rect(fill = "white", colour = NA),
    panel.border = element_rect(fill = NA, colour = "grey20"),
    panel.grid.major = element_line(colour = "grey92"),
    panel.grid.minor = element_line(colour = "grey92", size = 0.25),
    panel.spacing = unit(half_line, "pt"),
    panel.spacing.x = NULL,
    panel.spacing.y = NULL,
    panel.ontop = FALSE,
    
    strip.background = element_rect(fill = "NA", colour = "NA"),
    strip.text = element_text(colour = "grey10", size = rel(0.8)),
    strip.text.x = element_text(margin = margin(t = half_line, b = half_line)),
    strip.text.y = element_text(angle = 0, margin = margin(l = half_line, r = half_line)),
    strip.placement = "inside",
    strip.placement.x = NULL, 
    strip.placement.y = NULL,
    strip.switch.pad.grid = unit(0.1, "cm"), 
    strip.switch.pad.wrap = unit(0.1, "cm"), 
    
    plot.background = element_rect(colour = "white"),
    plot.title = element_text(size = rel(1.2), hjust = 0, vjust = 1, margin = margin(b = half_line * 1.2)),
    plot.subtitle = element_text(size = rel(0.9), hjust = 0, vjust = 1, margin = margin(b = half_line * 0.9)),
    plot.caption = element_text(size = rel(0.9), hjust = 1, vjust = 1, margin = margin(t = half_line * 0.9)), 
    plot.margin = margin(half_line, half_line, half_line, half_line),
    
    complete = TRUE)
}

# Wrapper Function for Long Graph Titles
wrapper <- function(x, ...) 
{
  paste(strwrap(x, ...), collapse = "\n")
}

#RDFZ Reds
RDFZReds <- c("#cf8f8d", "#ae002b", "#991815", "#78011e", "#4b0315")

#Grade Colors
GradeColors <- c("#8900df", "#0092df", "#00df76", "#94df00", "#BA9900", "#DF7800", "#C85500", "#B23200", "#9b0f00")

#Grade Fill Scale
scale_fill_lettergrades <- function(...){
  ggplot2:::manual_scale(
    "fill",
    values = setNames(GradeColors, c("A*", "A", "B", "C", "D", "E", "F", "G", "U")),
    ...
  )
}

pd <- position_dodge(width = 0.8)       #My standard dodging for graphs

#function for allowing inline code chunks to be shown verbatim
rinline <- function(code){
  html <- '<code  class="r">``` `r CODE` ```</code>'
  sub("CODE", code, html)
}

#This simply reads in the data : 
  #The list of questions and average performance by question; 
  #The list of answers from each student for each question, and their grades; 
  #The summary data for each student, showing their overall grade; 
  #The studentlist, complete with email addresses

# Read in the Student List with anonymised names
StudentList <-
  read.csv(here("StudentList.csv")) %>%
    mutate(Student = paste(First, Last, sep = " ")) 

# Read in All Questions
AllQuestions <- 
  read.csv(here(paste(params$Evaluation, "Questions.csv", sep = " - "))) %>%
    select(-X)

# Read in All Student Responses
AllResponses <- 
  read.csv(here(paste(params$Evaluation, "Responses.csv", sep = " - "))) %>%
    select(-X) %>%
    mutate_all(funs(str_replace_all(., "\\n]", "")) )

# Read in Summary Grades
AllGrades <- 
  read.csv(here(paste(params$Evaluation, "Grades.csv", sep = " - "))) %>%
    select(-X) %>%
    mutate(Letter = factor(Letter, levels =c("U", "E", "D", "C", "B", "A", "A*")))

A2 Psychology - Report on Mood Quiz

Basic Details of Evaluation

Mood Quiz covered a variety of materials from A2 Psychology Mood Disorders, and was administered in September 26, 2022.

The test consisted of a total of 4 questions, worth a total of 31 marks. The test was completed by a total of 36 students, with a mean score of 50%. You can see some descriptive statistics for the test in the table below:

Metrics <-  c("# Students", "Mean", "StDev", "Min", "Max", "# Min", "# Max", "Normality", "p val")
Values <- c(round(nrow(AllGrades),0),
            round(mean(AllGrades$Grade),1),
            round(sd(AllGrades$Grade),1),
            min(AllGrades$Grade),
            max(AllGrades$Grade),
            nrow(subset(AllGrades, Grade == min(AllGrades$Grade))),
            nrow(subset(AllGrades, Grade == max(AllGrades$Grade))),
            round(shapiro.test(AllGrades$Grade)$statistic,2),
            round(shapiro.test(AllGrades$Grade)$p.value,3)
              )

ReportKable <-
  cbind.data.frame(as.factor(Metrics), as.factor(Values)) %>%
    setNames(c("Metric", "Value")) %>%
    knitr::kable(caption = paste(params$Class, params$Evaluation, "Report", sep = " - "), row.names = F) %>%
    row_spec(0, bold = T, color = "white", background = RDFZReds[3])%>%
    kable_styling(full_width = FALSE, 
                  bootstrap_options = c("striped", "hover", "condensed"),
                  fixed_thead = TRUE) 

ReportKable

A2 Psychology - Mood Quiz - Report
Metric	Value
# Students	36
Mean	50
StDev	21.2
Min	4
Max	92
# Min	1
# Max	2
Normality	0.97
p val	0.48

Questions

The 4 questions from the Evaluation can be seen in the table below:

  AllQuestions %>%
    select(c(QNum, Question, Value)) %>%
    knitr::kable(caption = paste(params$Class, params$Evaluation, "All Questions", sep = " - "), row.names = F) %>%
    row_spec(0, bold = T, color = "white", background = RDFZReds[3])%>%
    kable_styling(full_width = FALSE, 
                  bootstrap_options = c("striped", "hover", "condensed"),
                  fixed_thead = TRUE)

A2 Psychology - Mood Quiz - All Questions
QNum	Question	Value
1	Discuss the strengths and weaknesses of using self-report questionnaires (D) or psychometric tests (E) to measure depression. You should include a conclusion in your answer	5
2	Describe what psychologists have learned about the Explanations of Depression	8
3	Describe what psychologists have learned about the Treatment and Management of Depression	8
4	Evaluate the treatment and management of mood disorders, including a discussion of efficacy/quality of evidence (D) or longitudinal research (E).	10

Between Class Differences

Differences between the two classes can be seen in the table and histogram below:

if(length(unique(AllGrades$Section)) >1){
  
# Subset out the grades by section
  SectionDGrades <- subset(AllGrades, Section == "D") 
  SectionEGrades <- subset(AllGrades, Section == "E")

# Calculate a t-test comparing the results of the two classes
  
  SectionComparison <- t.test(SectionDGrades$Grade, SectionEGrades$Grade, arning = FALSE, message = FALSE)    

# Assemble a Table of the results of the comparison 
  Metrics <- c("Section D", "Section E", "Difference", "T-test Value", "df", "p value")
  Values1 <- c(round(mean(SectionDGrades$Grade),1),
             round(mean(SectionEGrades$Grade),1),
             round(round(mean(SectionDGrades$Grade),1) - round(mean(SectionEGrades$Grade),1),2),
             round(SectionComparison$statistic, 2),
             round(SectionComparison$parameter, 1),
             round(SectionComparison$p.value, 3)
              )

# Fancy format the table for output as a Kable
  cbind.data.frame(as.factor(Metrics), as.factor(Values1)) %>%
    setNames(c("Metric", "Value")) %>%
    knitr::kable(caption = paste(params$Class, params$Evaluation, "Comparison", sep = " - "), row.names = F) %>%
    row_spec(0, bold = T, color = "white", background = RDFZReds[3])%>%
    kable_styling(full_width = FALSE, 
                  bootstrap_options = c("striped", "hover", "condensed"),
                  fixed_thead = TRUE) 

# Create an overlapping Histogram Comparing Performance between the two classes

  BetweenTitle <- paste(params$Class, params$Evaluation, "Histogram of Grades by Section", sep = " - ")

  ggplot(data=AllGrades, aes(x=Grade, fill = Section)) +
    geom_histogram(aes(y=..density..), alpha = 0.75, position = "identity") +
    labs(x="Grade (Percentage)", y="Density") +
    stat_function(fun=dnorm, args = list(mean=mean(SectionDGrades$Grade), sd=sd(SectionDGrades$Grade)), color=RDFZReds[1], size = 1.2) +
    stat_function(fun=dnorm, args = list(mean=mean(SectionEGrades$Grade), sd=sd(SectionEGrades$Grade)), color=RDFZReds[4], size = 1.2) +
    scale_fill_manual(values= c(RDFZReds[4], RDFZReds[1])) +
    scale_x_continuous(limits = c(0,100)) +
  theme_alan() +
  ggtitle(wrapper(BetweenTitle, width = 45))


}

Individual Student Report - Funny Religion

# Test Data
#FocalGrades <- filter(AllGrades, Student == "Elina Mi")
#FocalResponses <- filter(AllResponses, Student == "Elina Mi")

#Real parameterised data
FocalGrades <- filter(AllGrades, Anon == params$Anon)
FocalResponses <- filter(AllResponses, Anon == params$Anon)

Hello Funny Religion,

In the sections below, you can find analysis of your performance on Mood Quiz, including comments (where applicable), model answers from the teacher, and even other models answers from students.

You scored a total of 23 out of 31 possible marks, for a grade of 92%, and a letter grade of A*. You can see where you fall relative to the rest of the class in the tables and graphs below.

Anonymised Grade List

See the list of anonymised grades for all students in the course below. Your score is highlighted in yellow. You should take note of your codename, which you may need to retrieve your grades from future results posted in group chats or via email.

AnonTitle <- paste(params$Class, params$Evaluation, "Anonymised Grade List", sep = " - ")

AllGrades %<>% arrange(-Grade)
  
color.me <- which(AllGrades$Anon == params$Anon)
#color.me <- which(AllGrades$Student == "Elina Mi")

AllGrades %>%
  select(-c(Section, Student, First, Last, Assessment)) %>%
  setNames(c("Codename", "Score", "Grade", "Letter")) %>%
  arrange(-Grade) %>%
  knitr::kable(caption = AnonTitle, row.names = F) %>%
  row_spec(0, bold = T, color = "white", background = "#991815")%>%
  row_spec(color.me, bold = T, color = "white", background = "#eea917") %>%
  kable_styling(full_width = FALSE, 
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE)

A2 Psychology - Mood Quiz - Anonymised Grade List
Codename	Score	Grade	Letter
Funny Religion	23	92	A*
Poor Foxtail	23	92	A*
Helpless Hammer	21	84	A
Distant Baroness	19	76	B
Thoughtless Writing	19	76	B
Glamorous Need	18	72	B
Candid Mercury	17	68	C
Fast Windigo	17	68	C
Polite Stone	16	64	C
Subsequent Jewel	16	64	C
Deep Bagpipe	15	60	C
Grieving Religion	15	60	C
Flawless Lizard	14	56	D
Faint Celery	14	56	D
Meaty Stove	14	56	D
Confused Packer	14	56	U
Many School	14	56	D
Gleaming Bee	13	52	D
Alleged Crayon	13	52	D
Cool Lightning	13	52	D
Cagey Actor	12	48	E
Yawning Avalanche	11	44	E
Neighborly Kid	10	40	E
Grim Serpent	10	40	E
Tremendous Wheel	9	36	U
Zany Screw	8	32	U
Attractive Bread	8	32	U
Bitter Angler	8	32	U
Orange Waste	8	32	U
Rough Angel	7	28	U
Brave Phantom	6	24	U
Puzzled Starlight	6	24	U
Wicked Knot	6	24	U
Secretive Thing	6	24	U
Feline Hammer	6	24	U
Odd God	1	4	U

Histogram of Grades

Below you can see the histogram of performance on the test, including your performance (in yellow), which gives a visual representation of where your performance is relative to the rest of the class.

DistributionTitle <- paste(params$Class, params$Evaluation, "Histogram of Grades", sep = " - ")

  ggplot(data=AllGrades, aes(x=Grade)) +
    geom_histogram(aes(y=..density..), alpha = 1, position = "identity", fill = RDFZReds[2]) +
    labs(x="Grade (Percentage)", y="Density") +
    stat_function(fun=dnorm, args = list(mean=mean(AllGrades$Grade), sd=sd(AllGrades$Grade)), 
                color=RDFZReds[4], size   = 1.4) +
    scale_x_continuous(limits = c(0,100)) + 
    geom_vline(xintercept = mean(AllGrades$Grade), col = RDFZReds[4], size = 1.5) +
    annotate(x = mean(AllGrades$Grade), y = +Inf, label = "Mean Grade", vjust = 2, geom = "label") +
    geom_vline(xintercept = FocalGrades$Grade, col = "#eea917", size = 1.5) +
    annotate(x = FocalGrades$Grade, y = +Inf, label = FocalGrades$Anon, vjust = 2, geom = "label") +
    theme_alan() +
    theme(legend.position = "none") +
    ggtitle(wrapper(DistributionTitle, width = 45))

Letter Grade

Below you can see the distribution of letter grades, including your performance (in yellow).

LetterTitle <- paste(params$Class, params$Evaluation, "Distribution of Letter Grades", sep = " - ")

ggplot(data=AllGrades, aes(x=Letter, fill = Letter)) +
  geom_bar(stat = "count", position = pd, width = 0.8) +
  scale_x_discrete(drop = FALSE) +
    scale_fill_lettergrades() +  
  labs(x="Letter Grade", y="Count") +
  geom_vline(xintercept = FocalGrades$Letter, col = "#eea917", size = 1.5) +
  annotate(x = FocalGrades$Letter, y = +Inf, label = FocalGrades$Anon, vjust = 2, geom = "label") +
  theme_alan() +
  ggtitle(wrapper(LetterTitle, width = 45))

By Question Performance

In the sections below you can see your answer for each question, the grade you were given, comments from the teacher, and model answers

QFeedbackeR <- function(FocalNum) 
{
  # Sample some full mark answers from other students
  
  StudentModelAnswers <- 
    AllResponses %>%
      subset(QNum == FocalNum) %>%            
      arrange(-as.numeric(Grade)) %>%
      head(3)
 

  
  # Put together table 
  QuestionFeedback <-
    c(paste(FocalResponses$Question[FocalNum],"[", FocalResponses$Value[FocalNum], "]", sep = ""), 
      "this is dummy text to create a buffer",
      
      "Your Answer [Your Grade]",                                                                               
      paste(FocalResponses$Answer[FocalNum],"[", FocalResponses$Grade[FocalNum], "]", sep = ""),
      "Teacher's Comment",
      FocalResponses$Comment[FocalNum],
      "this is dummy text to create a buffer",
      
      "Average Grade",
      subset(AllQuestions, QNum == FocalNum)$Mean,
      "General Comments on Question",                                                                           #8
      plyr::mapvalues(FocalResponses$Question[FocalNum], from = AllQuestions$Question, to = AllQuestions$General.Comment), 
      "this is dummy text to create a buffer",
      
      "Teacher's Model Answer",                                                                                            #10
      FocalResponses$Model.Answer[FocalNum],                                                                               #11
      "Model Answers from Other Students",                                                                                 #12
      paste(StudentModelAnswers$Answer[1], " (", StudentModelAnswers$Grade[1], "marks)", sep = ""),                        #13
      paste(StudentModelAnswers$Answer[2], " (", StudentModelAnswers$Grade[2], "marks)", sep = ""),                       #14
      paste(StudentModelAnswers$Answer[3], " (", StudentModelAnswers$Grade[3], "marks)", sep = "")                       #15
      ) %>%
    data.frame() %>%
    setNames(paste("Question", FocalNum, sep = " "))
  
  QuestionFeedback %>%
      knitr::kable(row.names = F) %>%
        row_spec(0, bold = T, color = "white", background = "#991815", font_size = 20) %>%
        row_spec(c(2, 7, 12), background = "white", color = "white")  %>%   #buffers   
        row_spec(c(3,5,8,10,13,15), bold = T, color = "white", background = "#991815" ) %>%
        kable_styling(full_width = FALSE, 
          fixed_thead = TRUE) 
}


#Function to create graphs

QGrapheR <- function(FocalNum) 
{
  
GraphData <- 
  subset(AllResponses, QNum == FocalNum) %>%
  mutate(GradeF = factor(Grade, levels = c(0:Value[1])))

FocalTitle <- paste("Distribution of Grades - Question", FocalNum, sep = " ")
  
QPlot <-
  ggplot(data=GraphData, aes(x=GradeF)) +
    geom_bar(stat = "count", position = pd, width = 0.8, fill = RDFZReds[3]) +
    scale_x_discrete(drop = FALSE) +
    labs(x="Question Score", y="Count") +
    theme_alan() +
    ggtitle(wrapper(FocalTitle, width = 50))

QPlot
}

Question 1

Question 1
Discuss the strengths and weaknesses of using self-report questionnaires (D) or psychometric tests (E) to measure depression. You should include a conclusion in your answer [5]
this is dummy text to create a buffer
Your Answer [Your Grade]
self-report questionnaire usually asks participants to reflect & report their personal feelings/behaviors through questions. clinicians usually use BDI to diagnose patients with depression. one strength of using the self-report questionnaire is that the clinicians would be able to collect data very fast, and they could obtain a large sample comparing to conducting interviews (because they could just simply lend the questionnaire to participants and ask them to finish it). therefore, if they are doing some experiments like “how many people in this city has depression”, this could help them to be relatively fast. However, one weakness of using self-report questionnaire is the demand characteristics. intention of scales and options on BDI is quite easy to be identified, so if participants with depression don’t want to reveal their emotional state, they could simply choose “0” for all rating scales. eg. 0 for feeling unhappy, 0 for unable to sleep, etc. This could largely lower down the validity of the questionniare, which may lead to a false result.[2]
Teacher’s Comment
You don’t need to tell me what a self report is. You’re wasting time. You need multiple strengths and multiple weaknesses You need a conclusion
this is dummy text to create a buffer
Average Grade
2.72
General Comments on Question
Remember that you must have PLURAL strengths and weaknesses - i.e. two of each You must also have a conclusion that is evaluative - i.e. that weighs the strengths relative to the weaknesses and arrives at a conclusion, rather than just telling me what you talked about. Your answers should be quick and dirty here, and have absolutely no more discussion than mine do in the sample answer.
this is dummy text to create a buffer
Teacher’s Model Answer
One strength of using self-report questionnaires to measure depression is that it is quick and easy to collect the data; for example the BDI can be completed in approximately ten minutes and can be administered to large groups of people concurrently. A second strength is that self-report allows access to the internal states of participants in ways that wouldn’t be possible using other methods.; e.g. because people can easily mask their symptoms in day to day life it would be very hard to measure depression through observation. One weakness of self-report questionnaires is the possibility of demand characteristics/social desirability; patients may give untrue answers because they want to be perceived in a more positive light or to avoid medical treatment. A second weakness is that these self-report questionnaires produce mostly quantitative data; this kind of data is convenient in that it is easy to analyse, but it may fail to capture the actual lived experience of a person with a mood disorder. In sum, the strengths of self-report questionnaires outweigh the weaknesses; they are not perfect ways to measure anything, but what they lack in precision they make up for in ease of use. However final measurement and diagnosis of mood disorders should always be conducted by a trained clinician, rather than through the use of self-report measures alone.
Model Answers from Other Students
The strengths are that it has a high validity and the data are easy to analyse. For example the BDI questionnair, a test given by rating scale. It has 21 questions and each question have multiple choice of level 0-3 to select. Since it’s a psychological test in a force choosed rating scale, it has a high validity. And since all of the question are in quantitative data, it’s easy to analyse. However it has some weaknesses too. One weakness is that a self-report questionnaire is lack of details. Take the BDI scale for another example, since all we need to do to answer the questionnaire is to select the level, the questionnaire can’t show what exactly what our symptoms are. Like, two patients may select the same level for a question, but they have got totally different symptoms in a different time period. If we only look at the questionnaire, they have got the same score, but if we ask them for detail, they may have totally different disorder. Therefore, because of those strengths and weakness, a self-report questionnair can only used to test if a person may have got depression, rather than a treatment of depression or a diagnose criteria. (5marks)
The strength is that use standardized measure which is more objective and scientific, like BDI which is 4-point scale questionnaires. That means we can used it again and again to see whether we can get similar results to test reliability. In addition, standardized test is used to have many controlled, therefore it have high validity and reliability. Since the reliability of BDI is around 0.90. It also allows to have comparison and generalizability to be made with other psychometric tests. The weakness is that the measure it not always valid, since participants may have demand characteristics or socially desirable answer, which they may not tell the truth. Because patients may not want to express their feeling when they have depression, in order to get rid of it, they may look good. In addition, once the patients are labeled by the test, it is difficult to remove that label, that means people may think those patients have depression is wired. From my point of view, it is still useful when diagnosis the symptoms of disorders, and can help those potential patients to see the doctor immediately, since the score in BDI may be a signals to them. That means psychometric test have widely application to disorders. (5marks)
one of the strengths for using self-report questionnaire to measure the depression is that you can get a great statistical data. For examples in the mood disorder study like in While et al., the most common self-report questionnaire is BDI, this is a self-report questionnaire for choosing the number from 0-3, about 21 questions in total. for examples the question like ‘do you think it is all your fault’, as the participants choose 0, which means the participants don’t think is their fault while choose 3 means it’s all their fault. According to the examples like BDI, this kind of self-report questionnaire can help the researchers to select the one with depression quickly. What’s more, the self-report is a good tool for analyzing the data quickly. the self-report not only get the statistical results but also the descriptive results, at this time the researchers can be given a in-depth data of participants’ self-claiming of their symptoms and behavior and cognition, which is a helpful way for diagnosis their mental illness, this can give a high validity. one weakness for using self-report questionnaire is the participants is easy to lie to the researchers, as what mentioned in the Whiles et al., although from using the BDI self-report shows that accepting the CBT treatment can decrease their BDI score dramatically, however, in this longitudinal study, the participants keep filling the similar self-report and can easily give a high demand characteristics and social desirability, which means in the self-report, the results collected may not be true, at this time the self-report is not that valid. In a way of conclusion, the self-report questionnaire can select both qualitative and quantitative data rapidly, but it is also possible that the data collected is fake as there’s a possibility that demand characteristics exist. (4marks)

Question 2

Question 2
Describe what psychologists have learned about the Explanations of Depression [8]
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Your Answer [Your Grade]
Oruc et al. tested whether the genes play a part in the cause of the bipolar/depression symptoms. they investigated this through studying serotonin receptors and transporters. Based their results, psychologists learned that genes and neurochemical explanation could explain the onset of bipolar depression–certain alleles are related with BPI, and between women, appearance of certain genes is positively correlated with the BDI score. Beck’s study provides an insight for psychologists into the cognitive causes of the depression. Beck proposed that there’s faulty cognition–in the process of taking in the information from the environment, process it, then produce the outputs–the cognition part isn’t only a symptom, but also a cause of the depression. according to the feedback loop, negative view about oneself could lead to negative views about the future and negative views about the world. psychologists learned that these could all self-reinforce each other. for example, black and white thinking. you get a bad score on an exam, then you think this must be your fault, you are not suitable for studying psychology (neg view about yourself). then, you think you’re so bad that you can’t even go to a college (neg view about the future). next, you may complain about the world: why the world is so unfair that all things should be measured in grades? (neg view about the world), this will then, in turn, lead to further negative view about yourself. this forms a spiral. Seligman’s study proposed that depression is learned and reinforced, and we could use behavioral explanation & therapy to look at the cause of the experiment. psychologist learned about his attribution style: if there’s a negative events happen, then people will make it a Internal vs External, Global vs Specific, and stable vs unstable thing. patients with depression will choose more Internal + stable + global choices. the result discovered that patients with higher BDI choice mainly have more negative attribution style (there’a strong correlation between them), and after the CBT treamtent, patients improved their attributional style while their BDI score improved.[6]
Teacher’s Comment
Oruc et al. doesn’t use BDI scores. Don’t make things up, teachers hate that Your Beck explanation is longer than it needs to be and thus not very well focused Focus on the theory for Seligman - the results of the experiment are meaningless without explaining these things (which you haven’t really done)
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Average Grade
4.08
General Comments on Question
I stood in front of the class several times and told you that you should write this describe question for every single question as one of the most important aspects of studying for A2 Psychology. Hopefully you will be able to carry this forward into the future - preparing these 8 and 10 point questions is 60% of the Paper 3 grade, and also helps you prepare for many of the other question types in Paper 3/4
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Teacher’s Model Answer
Psychologists have proposed many explanations for the causes of mood disorders. First, biological explanations suggest that mood disorders arise because of problems with the brain and neurotransmitters, which are likely to be at least partially inherited from our parents. Oruc et al. conducted a study to determine to what extent mutations of the genes for two important parts of the serotonergic system were implicated in depression. Specifically, they looked at heritable mutations of the genes for 5-HTR2c (a serotonin receptor) and 5-HTT (a serotonin transport protein) and found that, when looking within female participants in their study, having certain mutations to these genes made one significantly more likely to develop bipolar disorder during one’s life. Instead of focusing on biology, Beck’s explanations for depression focus on a causal role for cognition - to Beck, the faulty thinking of depressed patients is not merely a symptom, but a cause of its own. To explain this, Beck proposed the idea of the ‘cognitive triad’, which explains the relationship between perceptions of the self, the world, and the future. Beck suggested that various types of faulty cognition, including personalisation and negativity bias, could create a self-reinforcing feedback loop in the cognitive triad that would cause symptoms to worsen over time. For example if I fail a test it might lead me to think that I’m stupid (view of self), which might lead me to think that I won’t get into a good university (view of the future), and that the world is an unfair place where I will never be happy (view of the world), which would continue to feed back into increasing bad views of myself (I’m worthless and life is pointless). Seligman’s explanations of depression share some features with Beck’s, but are ultimately more behavioral in nature. Seligman suggests that one of the crucial factors in depression is learned helplessness, which is acquired through experience by simple learning processes like reinforcement. When bad things happen to us, Seligman says, we can feel that we are powerless to have done anything about them. This is normal in some cases, but Seligman points out that this becomes problematic when we generalise too much and feel that we are powerless to change anything about ourselves or the world. Seligman links this to the idea of attributional styles and suggests that depressed patients have fundamentally incorrect interpretations of the causes of events in the world that lead to depression.
Model Answers from Other Students
the abnormal level of neurotransmitters is said to be the cause of depression in the biological explanation. neurotransmitters that affect people’ mood include dopamine, norepinephrine and serotonin. Oruc et al is specifically interested in whether polymorphism of serotonin receptor 2c and serotonin transporter is related to the cause of depression. they conducted a study using 42 participants with mood disorders and 40 age, gender matched controls. result shows no difference in the serotonin receptor 2c and serotonin transporter between two groups. however, the system of serotonin is sexually dimorphic, that means it is different between male and female because of the difference in sex chromosome, thus female have polymorphism are slightly more likely to have mood disorders compared to male. the cognitive explanation indicates that the depression is caused by the negativity bias and the faulty thinking process. to be more specific, Beck thinks there are three factors (the negative view of self, the world and the future) interact with each other and formed a negative feedback loop that can spiral out of control so depression is caused. for example, I failed in my psychology test and considered myself as a failure, I think I will never be able to be good at psychology, and the world is so unfair for psychology students. the negative feedback loop generates depression can be caused by faulty thinking, whether personalisation(the negative event happens because of my own fault) or black and white thinking(all events can be considered only positive and negative). learned helplessness could be formed from a childhood trauma. this makes people think there is no hope of escape the current situation and might find the future is too uncontrollable. this then cause different attribution style among different people. for example, it could be internal, stable and global, means that the event is caused by oneself, would last long and have a wild effect. (7marks)
As for the explanations about the depression, in the biological aspects, the abnormal neurotransmitter content level is a main cause for the depression. It has been proved that the low amount of serotonin will lead to a depression, this lead to a study for Oruc et al. Based on what mentioned above, they aim to detect the relationship between depression and serotonin receptors (5-HTR2c) and serotonin transporters (5-HTT), to see whether the mutation genes between BPI and control groups (~40 participants with similar age and gender groups in randomization) is essential for associating with the depression. Initially to the overall ~40 participants give no difference in serotonin receptors & transporters with depression, then they considered the genetic explanation——due to the sexually dimorphic, the females genes and chromosome shows a huge difference with that in males, so that they separate the groups of participants based on the different genders. The results II shows that, there’s a suggestive p values in the serotonin receptors, while there’s a ‘significant p values’ found only in the females group in transporters, which means that the serotonin transporters can be associated with depression in female groups. As for the cognitive explanation, Beck considered that the depression is based on the faulty thinking, cognition is not only a symptoms but also a cause. He mentioned that the faulty thinking or the negative bias is a self-reinforcing process, it let the feedback loop worsen the depression state. At the same times, the cognitive triad is also mentioned that it’s a loop that the consideration in themselves can also affected the cognition in the world, the future of themselves. For examples if a person fail in a exam, he or she would give a wrong and negative feedback loop, due to the faulty thinking they may think is all their fault, at this time they may be disappointed to the whole outside like they consider the world is treating he/she in this detrimental way on purpose, and think they will fail each exam in the future and cannot pass anymore. Beck also mentioned a cognitive reconstructing method of CBT. For the behavioral explanation, all the behaviors can be gained from leaning and reinforcement, also the depression can be ‘unlearned’, the learned helplessness is an explanation for the depression that the patients learnt how to react in the negative way as they feel extremely sadness, like give a suicidal reaction, they may consider this is a positive reaction that they deserve it, which made them feel better. Seligman use a study to find the association between the attribution styles and the depression, the results shows that there’s a high association relationship (correlation), and as there’s a high BDI score, the more negative attribution style it would be. (7marks)
There are several way to explain the depression, which can be biochemical, cognitive and behavioral factors. Biochemical factors focus on two ways, genetic shows that the closer a person is to a sufferer, the more genes they share, the more likely the person is to develop a mood disorder. That means depression will run in families and more likely in first-degree relatives. Biochemical refers to lack or excess of certain chemicals like dopamine or serotonin affects the functioning of the brain, influencing the emotion regulation. Simply means imbalance in neurotransmitters serotonin and norepinephrine can cause depression, depressed individuals will have low activity of serotonin and norepinephrine neurons. However individuals with bipolar depression show a reverse pattern. Cognitive factors refers to internal thoughts form a real world that explains the behavior of people with depression is due to faulty thinking. It can be personalization and black and white thinking. The cognitive model states that depression is the result of consistent negative bias in thinking processes. Beck state that negative views of things are “self-reinforcing” through the cognitive triad of depression, which is the negative views on themselves, on the world and on the future. Behavioral factors is that all behaviour happen through learning an reinforcement. Depression is a behavior like anything else is, and can be ‘unlearned’ the same way that it was learned. Learned Helplessness association between certain stimuli and negative emotional state is learned and remembered, early negative experiences can lead people to believe that future events will always go the same way. Therefore they may no longer to have confident in the future. (7marks)

Question 3

Question 3
Describe what psychologists have learned about the Treatment and Management of Depression [8]
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Your Answer [Your Grade]
there are several treatments used by clinicians in different fields. specifically, mainly biochemical treatment and cognitive treatment. biochemical treatment tries to fix the internal imbalance of neurotransmitters. specpfically, there’s lower levels of monoamine and serotonin inside human bodies when there’s onset of depression. patients take in drugs like MAOIs and SSRI to change their internal neurotransmitter levels. MAOI works by blocking the MAO. this could prevent monoamine from being breakdown. this could ensure that neurotransmitter last longer inside the body, allowing it to bounce more times, which could increase the level of it. SSRI works by inhibiting the reuptake of serotonin. therefore, there would be more serotonin inside of bodies. then, depression symptoms could turn better. Cognitive reconstructuring focus on changing the false feedback loop inside people. usually, clinicians will inform patients what they’re going to do, then they would ask patients to record their thoughts and have the reality testing. next, they would challenge patients’ negative cognition and have the reattribution of their thoughts (eg. this failure isn’t because of you, it’s inevitable). therefore, in the end, patients will have the ability to apply this cognitive tools into interpreting other events in their lives. REBT proposes that you can’t control what happens to you, you can only control how you react to what happens to you. It Believes that we become depressed because of how we interpret events, not because of the events themselves. there’s A B C process–A: activating event eg. havign bad scores on an exam B: belief–that’s because I’m bad C:consequences–feelings of worthlessness. there’s something going wrong with the B part (belief) for pateints with depression. there was a Meta-analysis of 70 studies comparing REBT to other therapies. they found that REBT patients showed more overall improvements than other group. but a later study discovered that this is wrong–it’s similar to the effect of antidepressants. ECT uses electricity to stimulate your brain. this may be able to treat the depression by break the original false feedback loop and making new neurons to wire together and fire together. as they fire together and wire together, there’s usually new feedback loop forms, which could treat depression in some ways. [7]
Teacher’s Comment

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Average Grade
3.14
General Comments on Question
See above. Notably my answer should actually probably mention more about specific studies - i.e. my answer is too heavy on theory and should be trimmed back on that front to include some results.
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Teacher’s Model Answer
Many approaches have been suggested for the treatment and management of depression and other mood disorders. The biochemical treatment of mood disorders mostly focuses on the idea that depression is caused by insufficient activity of the serotonergic system and proposes multiple ways to treat this problem. The first of these are MAOIs, which are chemicals that prevent the breakdown of serotonin and other monoamine neurotransmitters (e.g. dopamine, norepinephrine) in the synapse. SSRIs on the other hand work by blocking serotonin transporters, which means that the presynaptic neuron is unable to reuptake serotonin from the synapse. Both of these results in an increase in serotonergic signaling by causing more serotonin to stay in the synapse for longer. It is less clear how ECT works for the treatment of depression, but one theory is that it acts like a ‘reset switch’ to cause the body to restore normal levels of neurotransmitters throughout the brain. The idea of cognitive restructuring is closely tied to Beck’s theories of depression, and suggests that depression can be treated and managed by directly tackling its faulty cognitions. For example, if a patient is experiencing personalisation, they will constantly blame themselves for things that are out of their control, which Beck suggests can be tackled through reattribution. If my parents have gotten a divorce and I blame this on myself, my therapist might point out that many parents get a divorce even before they have a kid, or that their love of me might actually have kept them together - both of these would allow me to reattribute the source of the problem away from myself and to break the feedback loop of the cognitive triad. REBT shares many features with cognitive restructuring, as it focuses on maladaptive beliefs as the core problem of patients with depression, and thus as the target for treatment. REBT is based on the ABC model, where activating events (A) are interpreted in the light of beliefs about the cause of the event (B) and produce consequences for the individual (C). The fundamental insight of REBT is that people do not have control over activating events, so if they want the best consequences, then they must form appropriate beliefs. In REBT a therapist will engage in the ‘disputing’ process directly against their client - challenging irrational beliefs that lead to negative consequences in an attempt to shape a more rational and healthy mode of thinking.
Model Answers from Other Students
there are several treatments used by clinicians in different fields. specifically, mainly biochemical treatment and cognitive treatment. biochemical treatment tries to fix the internal imbalance of neurotransmitters. specpfically, there’s lower levels of monoamine and serotonin inside human bodies when there’s onset of depression. patients take in drugs like MAOIs and SSRI to change their internal neurotransmitter levels. MAOI works by blocking the MAO. this could prevent monoamine from being breakdown. this could ensure that neurotransmitter last longer inside the body, allowing it to bounce more times, which could increase the level of it. SSRI works by inhibiting the reuptake of serotonin. therefore, there would be more serotonin inside of bodies. then, depression symptoms could turn better. Cognitive reconstructuring focus on changing the false feedback loop inside people. usually, clinicians will inform patients what they’re going to do, then they would ask patients to record their thoughts and have the reality testing. next, they would challenge patients’ negative cognition and have the reattribution of their thoughts (eg. this failure isn’t because of you, it’s inevitable). therefore, in the end, patients will have the ability to apply this cognitive tools into interpreting other events in their lives. REBT proposes that you can’t control what happens to you, you can only control how you react to what happens to you. It Believes that we become depressed because of how we interpret events, not because of the events themselves. there’s A B C process–A: activating event eg. havign bad scores on an exam B: belief–that’s because I’m bad C:consequences–feelings of worthlessness. there’s something going wrong with the B part (belief) for pateints with depression. there was a Meta-analysis of 70 studies comparing REBT to other therapies. they found that REBT patients showed more overall improvements than other group. but a later study discovered that this is wrong–it’s similar to the effect of antidepressants. ECT uses electricity to stimulate your brain. this may be able to treat the depression by break the original false feedback loop and making new neurons to wire together and fire together. as they fire together and wire together, there’s usually new feedback loop forms, which could treat depression in some ways. (7marks)
For biochemical treatment, psychologists have managed a kind of medicine to treat depression , which is called antidepressants. One is MAOI, which is used to inhibit an enzyme called MAO that will broke neurotransmitter. MAOI can inhibit it, so that more neurotransmitter will accumulate and be received by the second neuron. SSRI is more selective than MAOI, which means it will have less side effect than MAOI ( but still have headache…etc). It will block the serotonin transporter, to inhibit the re-uptake of serotonin by the presynaptic neuron. It allows more serotonin stay in the clef and be received by the second neuron. Hence it can transport more information. Cognitive resturcturing is CBT, which is a cognitive therapy that the researcher aims to change the negative interpretation of the external world by patients. The procedure is divided into 5 steps: first, the researcher will tell patients the theory of depression. Then, they will give patients some homework to let them to record their negative emotions and thoughts. Thirdly, the researcher will give them reality testing to broke their false interpretation in the real world, it followed by a reattribute that the researcher will give them a new and real attribution. In the last step, patients will be able to use this cognitive tool by themselves. REBT is very similar with CBT that they both based on a same theory. However, REBT based a philosophy : Stocism, which indicates depression is caused by any external factors, it is due to the way people seen them. REBT researchers use ABC model to explain depression: A: the event that triggers the negative emotion, B: the belief about why it happened . C: consequence: maybe the change in behavior or emotions. REBT therapist thinks depression is caused by an irrational thinking. They will forcefully disputing with patients to let them know that they have the ability to decide what they will feel. And holding a negative interpretation can only let them feel worse. Different with BIochemical treatment, both CBT and REBT are reported as very effective. However, SSRI is only reported that will extend the time for producing the thought of suicide. REBT is reported are as the same effective as antidepressants . (7marks)
There are several way to treat and manage of depression in terms to trigger biochemical and cognitive explanations. Biochemical approaches attempt to deal with problems with the brain and neurotransmitters like dopamine, serotonin and norepinephrine, it tackle the physical root of the problem. It usually done by drug which usually is MAOIs and SSRIs, but it done in different way to treat mood disorder. Because there have two way when transporter give neurotransmitters to receptors whether transport to neuron or reuptake back to former neuron. MAOIs inhibit the work of an enzyme (monoamine oxidase) that is responsible for breaking down and removing the neurotransmitters serotonin, dopamine and norepinephrine. It prevent those neurotransmitters from being broken down, and also allow then to stay at higher lever in the brain. While SSRIs act on the neurotransmitter serotonin, and stop it being reabsorbed and in the brain. It is quicker and more acceptable way by patients, since they only need is to take drugs everyday. However it may have side effect, since the MAOIs will broadly diffuse to all part of the body and system since it trigger three neurotransmitters. Therefore it may have problem of headache and insomnia. Cognitive approaches can be cognitive reconstructing that attempts to get at this by tackling the symptoms of depression. It used when the antidepressants are no longer useful or effective. It is a form of Cognitive Behavioral Therapy to modify patients’ thought patterns in order to change moods and behavior. It is based on the idea that negative feels will change our thought processes and beliefs. There have several steps, firstly explain the cognitive theory of depression to patient in order to help patients know more about themselves, secondly educate patients to observe and record their thoughts, thirdly challenge their dysfunctional thoughts, fourthly reframe thinking, then patients can repeat the process on their own. Based on the Stoucism, you cannot control what happens to you, you can only control how you react to what happens to you. REBT is concerns with three components, A is activating Event (the event cause a negative reaction or response), B is belief (the thoughts they have when explain the acting event) and C is consequences (the distressing emotions). Patient need to see the C they experience are only one factors result of A, and believes that B is not the truth. (7marks)

Question 4

Question 4
Evaluate the treatment and management of mood disorders, including a discussion of efficacy/quality of evidence (D) or longitudinal research (E). [10]
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Your Answer [Your Grade]
there are several treatments and managements of mood disorders, and each has some strenghts and weaknesses. here, I’m going to discuss the efficacy and quality of evidence and the longitudinal study to distinguish them, and to find out which kind of therapy is the best comparatively. efficacy of evidence efficacy of evidence is important for us to consider beacsue it usually validates the study of falsifies it. this could demonstrate whether this kind of therapy is really useful, or the experiment is just tryhing to make up a ‘result’. Some studies provide really good strnegth of evidence. take Wiles et al. for example, their study used the cognitive reconstructuring therapy, and their results really demonstrates BDI scores were originally matched, but after treatment were significantly lower in the CBT group, and there’s a really strong effects of this therapy–those who receive CBT is three times more likely to respond to the treatment. However, some studies also provide quite weak support for their result. take the Rucci et al. for example. they tested whether SSRI is useful. but for their results, they didn’t find really evident support. there’s file drawer problem– they just simply kicked out tresults that didn’t conform to their expectations, and there was only 32 participants left. this was a small sample size, and the validity was really low beacsue of the file drawer problem. therefore, the evidence was quite weak. COnsequently, in comparison between these two therapies, I believe that the cognitive treatment is far much better than the SSRI in this group. Longitudinal research Longitudinal research is really useful becase most teratments in their nature are longitudinal. so if we wnat to investigate whether htey are really useful, we need to conduct longitudnial stuyd. some experiments are longitudinal studies. for example, Wiles et al.. they followed patients for twelve months, and found that decreases in depressive symptoms persisted over an extended period. this could demonstrates that patients are able to apply the cognitive method into their real life. However, other experiments were relatively short. Like Rucci or other biological treatments , they mainly just follow patients for 6-8 months, which is relatively short comparing to the time they will be taking drugs. also, accordingto Pratt et al., 60% of people take their antidepressants for at least 2 years, and over 10% of people remain on an antidepressant for over 12 years. therefore, it is very important for all patients to knwo the drugs’ long term effectiveness instead of just 6-8 months of study and followup. In conclysion, I belive that in this part, cognitive therapy is also better than the biological treatments. In conclusion In conclusion, I belive that considering both longitudinal study and efficacy of evidence, it’s all stronger for allc ognitive therapy.[8]
Teacher’s Comment
Why are there three “in conclusion”s here? You don’t need to try to memorise my answer�
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Average Grade
2.36
General Comments on Question
Evaluation still needs some work. I understand that many/most of you ran out of time here. This test was intentionally long, with the idea being that it would force you all to practice some time management/grade maximization. This, unfortunately, is not what happened!
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Teacher’s Model Answer
The treatment and management of mood disorders can be very tricky, as suggested by large amounts of conflicting evidence in the literature and the many patients who are classified as ‘treatment resistant’. Below, I evaluate the approaches to treatment based on both the quality of evidence and the application of these treatments to everyday life. The quality of evidence for the efficacy of treatments is incredibly mixed, which can make it very difficult to determine which approaches are appropriate for a patient. Given that many approaches also come with side effects or other substantial costs, it is important to have access to the best possible evidence. On the one hand, there is a great deal of research into the treatment of depression, which is a disorder that affects approximately 10% of people at some time in their life. Some of this evidence is quite strong; for example the study by Wiles et al. argues compellingly in favor of the use of CBT for the treatment of depression: twelve months after the start of treatment patients who were given CBT were there times more likely to have experienced a significant reduction of their depressive symptoms than controls. Similarly, the study by Rucci et al. demonstrates a significant reduction in suicidal ideation for patients using SSRIs to manage their depression - an effect that may be large enough to save lives. On the other hand, even the best evidence for the efficacy of various treatments is mixed. Even in the study by Wiles et al., which offers some of the best evidence in favor of a treatment, over half of participants experienced minimal or no reductions in their symptoms. Rucci et al.’s study was even worse: ~90% of the data was excluded from the final analysis because no differences could be seen in reduction of overall BDI scores between SSRIs and Interpersonal Therapy. And these are just the studies that get published - many have pointed out that there is a substantial file drawer problem as well as perverse incentives that cause studies with negative results to go unpublished, so the overall quality of evidence is very low. Overall, the quality of evidence for the treatments for depression is very poor: even studies that are published provide conflicting evidence, and the many that are not almost certainly show no evidence of the efficacy of these treatments, or in some cases argue against certain treatments. This is especially problematic when you consider how it interacts with the fact that patients need to actually live with the outcomes of these disorders for extended periods of time. Patients must deal with a variety of issues including side effects and tolerance, as well as the costs (both economic and personal) of engaging in treatment. Side effects and tolerance are a problem for all biochemical treatments: as patients take the drug for a longer time their body will habituate to it and they will come to require increasingly large doses. As the dosage increases, so will the chances, and strength, of developing side effects. For drugs that act diffusely in the body like MAOIs, these side effects can be considerable and can even put a patient into a more dangerous medical state than the one caused by their depression. All treatments have substantial economic and personal cost, and this places many of them out of the reach of normal people in most countries. New drugs can be quite expensive, and this cost is at least matched and often exceeded by various talk therapies like CBT that can cost well over 100 dollars per session. As somewhat of a saving grace, CBT at least offers generalised tools that patients can apply to other areas of their life: some of the tools of reframing learned in the context of treating depression for example can be applied to managing stress before an important exam. Overall the evidence for the treatment of mood disorders is not favorable: evidence for the efficacy of various treatments is cloudy at best and is made worse by the fact that it often ignores costs that must be borne by patients. Better designed studies with long-term follow-up and improved research ethics around publication are crucially required to improve the treatments offered to patients.
Model Answers from Other Students
there are several treatments and managements of mood disorders, and each has some strenghts and weaknesses. here, I’m going to discuss the efficacy and quality of evidence and the longitudinal study to distinguish them, and to find out which kind of therapy is the best comparatively. efficacy of evidence efficacy of evidence is important for us to consider beacsue it usually validates the study of falsifies it. this could demonstrate whether this kind of therapy is really useful, or the experiment is just tryhing to make up a ‘result’. Some studies provide really good strnegth of evidence. take Wiles et al. for example, their study used the cognitive reconstructuring therapy, and their results really demonstrates BDI scores were originally matched, but after treatment were significantly lower in the CBT group, and there’s a really strong effects of this therapy–those who receive CBT is three times more likely to respond to the treatment. However, some studies also provide quite weak support for their result. take the Rucci et al. for example. they tested whether SSRI is useful. but for their results, they didn’t find really evident support. there’s file drawer problem– they just simply kicked out tresults that didn’t conform to their expectations, and there was only 32 participants left. this was a small sample size, and the validity was really low beacsue of the file drawer problem. therefore, the evidence was quite weak. COnsequently, in comparison between these two therapies, I believe that the cognitive treatment is far much better than the SSRI in this group. Longitudinal research Longitudinal research is really useful becase most teratments in their nature are longitudinal. so if we wnat to investigate whether htey are really useful, we need to conduct longitudnial stuyd. some experiments are longitudinal studies. for example, Wiles et al.. they followed patients for twelve months, and found that decreases in depressive symptoms persisted over an extended period. this could demonstrates that patients are able to apply the cognitive method into their real life. However, other experiments were relatively short. Like Rucci or other biological treatments , they mainly just follow patients for 6-8 months, which is relatively short comparing to the time they will be taking drugs. also, accordingto Pratt et al., 60% of people take their antidepressants for at least 2 years, and over 10% of people remain on an antidepressant for over 12 years. therefore, it is very important for all patients to knwo the drugs’ long term effectiveness instead of just 6-8 months of study and followup. In conclysion, I belive that in this part, cognitive therapy is also better than the biological treatments. In conclusion In conclusion, I belive that considering both longitudinal study and efficacy of evidence, it’s all stronger for allc ognitive therapy. (8marks)
There are many studies about management and treatment of mood disorders, some of them will be discussed below. One way when considering which treatment and management of mood disorder is suitable for individual, the evidence they provide is an important thing to consider. For example, study such as Wiles et al. It try to find out the effectiveness of cognitive restructuring using a large sample size of 469 participants and strong statistical effects. Even though that Wiles tested a group of participants that are difficult to treat, but their is still significant result showing that CBT have a better result comparing to other drugs. Participants who receive CBT are three times more likely to respond to a treatment. However, evidence for other studies is shakier, for example the study by Rucci et al. It found no differences between SSRI and interpersonal psychotherapy, so use the “time length to suicidal ideation” as a measure standard. This is a problem because in this way the study exclude all participants beside the 32 participants to have suicidal ideation at least one during the study, this could show very low ecological validity because of the small sample size. Longitudinal strides are vert important when considering depression. Studies shows that CBT and other cognitive treatments of depression have long-lasting effects. For example, the study by Wiles et al, its researchers followed its participants for a total of 12 months, showing that decreases in depressive symptoms is consistent over an longer period. This is because CBT is believed to create long lasting changes in functioning and symptoms. While studies of biochemical treatments rarely use longitudinal methods of appropriate length. Many studies of the effectiveness of drugs (e.g., Rucci et al.) or other biological treatments (e.g., ECT) follow participants for only 6-8 weeks, meaning they are limited to showing only short-term improvements. This is especially problematic given that over 60% of people take their antidepressants for at least 2 years, and over 10% of people remain on an antidepressant for over a decade (Pratt et al.). In conclusion, above discussion show the importance of longitudinal studies in depression studies, and overall CBT is a better solution. (7marks)
Longitudinal research improves the reliability of the results. Longitudinal keep tracking patients’ level of illness for a period of time. It can prove the long-term effect of some types of therapy. For example, Whiles et al tracked patients for 12 months from the beginning of its research. It collected strong data evidence that CBT worked for depression that is hard to treat. Patients kept improving after they stopped seeing the counsellor. This indicates that CBT provides patients useful cognitive tools that they can use in daily lives. However, longitudinal research bears the risk of high dropout rate. Patients may move to other places or feel tried to come in a long period of time. In Whiles et al, around 20 people didn’t make to the end. In real life treatment, talking therapies like CBT or psychoanalysis requires 6 weeks and longer. Patients have to pay a lot to support their therapy, despite that therapy may not work well at first. Those longitudinal research is consuming for all the participants. The reductionism in therapies can often make complex problems easier to target at. If the element picked is surely a part of the bigger image, therapy for it may can be generalized to the illness. For example, in Beck’s cognitive theory, patients’ depression was due to their pessimistic cognitive triad, including experience, self-image, and vision of future. Depression people are trapped in their negative response loop and lost their hope. All the therapists need to do is to breaks the fixed schema in patients’ mind. They help patients dig deeper into their thoughts. Then cognitive restructuring helps patients see their world in a better way. It has been proved to be effective in Whales, even on the patients who are identified as “resilience to treatment”. However, reductionism often make us lose the whole picture of a mental illness. If we ignore too many factors, external validity of the results may decrease. For example, in Oruc et al, experimenter carefully chose participants from a specific population pool. They screen out those who don’t get enough points depression test and those who get too much score in psychotic test. Also, they only chose two genes that surely related to depression. But the resulted correlation was too insignificant to guide any change in medication of depression. Also, ignoring patients’ mental factors and their environment, the biochemical treatment itself only work of 1/3 of the patients. (7marks)

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A2 Psychology - Mood Quiz - Individual Student Report - Funny Religion

Alan Nielsen