Statistics for experimental psychology

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Newcastle University Mathematics and Statistics

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From users who are members of Content created by Newcastle University :

Christian Lawson-Perfect

said

Doesn't work

1 day, 17 hours ago

From users who are not members of Content created by Newcastle University :

Aleksandra Romanowska

said

Ready to use

3 years, 10 months ago

History

○

Christian Lawson-Perfect 1 day, 17 hours ago

Gave some feedback: Doesn't work

○

Aleksandra Romanowska 3 years, 10 months ago

Gave some feedback: Ready to use

○

Newcastle University Mathematics and Statistics 9 years, 3 months ago

Created this.

There is only one version of this exam that you have access to.

Question groups

Group 33 questions

1.
draft

Calculate the Pearson correlation coefficient on paired data and comment on the significance.
2.
draft

Spearman rank correlation calculated. 10 paired observations.
3.
Ready to use

Find a regression equation.
4.
draft

Multiple correlation question. Given the correlation coefficent of $Y$ with $X_1$ is $r_{01}$ , the correlation coefficent of $Y$ with $X_2$ is $r_{02}$ and the correlation coefficent of $X_1$ with $X_2$ is $r_{12}$ then explain the proportion of variablity of $Y$ . Also find the partial corr coeff between $Y$ and $X_2$ after fitting $X_1$ and find how much of the remaining variability in $Y$ is explained by $X_2$ after fitting $X_1$ .
5.
draft

Paired t-test to see if there is a difference between times take in a task.
6.
Ready to use

Two sample t-test to see if there is a difference between scores on questions between two groups when the questions are asked in a different order.
7.
draft

LSD and Tukey yardsticks on three treatments. Also one-way Anova test on same set of data.
8.
draft

LSD and Tukey yardsticks on five treatments. Also two-way Anova test on same set of data.
9.
draft

One-way ANOVA example
10.
draft

Two-way ANOVA example, 5 subjects, 4 treatments.
11.
Has some problems

Friedman test, 5 subjects, 4 treatments.
12.
draft

Kruskal-Wallis test with ties.
13.
draft

Two factor ANOVA example
14.
draft

Calculate the Pearson correlation coefficient on paired data and comment on the significance.
15.
draft

Calculate the Pearson correlation coefficient on paired data and comment on the significance.
16.
draft

Spearman rank correlation calculated. 10 paired observations.
17.
draft

Spearman rank correlation calculated. 8 paired observations.
18.
Has some problems

Friedman test, 5 subjects, 4 treatments.
19.
draft

Entering numbers in Numbas, Part 1.
20.
draft

Details on inputting numbers into Numbas.
21.
draft

Kruskal-Wallis test with ties.
22.
Has some problems

Friedman test, 5 subjects, 4 treatments.
23.
draft

Kruskal-Wallis test with ties.
24.
draft

Multiple correlation question. Given the correlation coefficent of $Y$ with $X_1$ is $r_{01}$ , the correlation coefficent of $Y$ with $X_2$ is $r_{02}$ and the correlation coefficent of $X_1$ with $X_2$ is $r_{12}$ then explain the proportion of variablity of $Y$ . Also find the partial corr coeff between $Y$ and $X_2$ after fitting $X_1$ and find how much of the remaining variability in $Y$ is explained by $X_2$ after fitting $X_1$ .
25.
draft

One-way ANOVA example
26.
Ready to use

Find a regression equation.
27.
Ready to use

Find a regression equation.
28.
draft

Two factor ANOVA example
29.
draft

Paired t-test to see if there is a difference between times take in a task.
30.
Ready to use

Two sample t-test to see if there is a difference between scores on questions between two groups when the questions are asked in a different order.
31.
draft

Two-way ANOVA example, 5 subjects, 4 treatments.
32.
draft

LSD and Tukey yardsticks on three treatments. Also one-way Anova test on same set of data.
33.
draft

LSD and Tukey yardsticks on five treatments. Also two-way Anova test on same set of data.

Topics

No topics have been defined.

Diagnostic algorithm

Extend built-in algorithm?

Base diagnostic algorithm

state (Produces the initial value of the state object): // should be renamed "initial_state"
    [
        "topics": map(
            [
                "topic": topic,
                "status": "unknown" // "unknown", "passed", or "failed"
            ],
            topic,
            values(topics)
        ),
        "retries": 3,
        "finished": false,
    ]

topics_by_objective (A dictionary mapping a learning objective name to a list of indices of topics):
    dict(map(
        let(
            ltopics, values(topics),
            indices, filter(lo["name"] in ltopics[j]["learning_objectives"], j, 0..len(ltopics)-1),
            [lo["name"],indices]
        ),
        lo,
        learning_objectives
    ))

unknown_topics (Which topics are still unknown?): 
    map(x["topic"],x,filter(x["status"]="unknown",x,state["topics"]))

first_topic (The first topic to pick a question on):
    unknown_topics[floor(len(unknown_topics)/2)]["name"]

first_question (The first question to show the student):
    random(topics[first_topic]["questions"])

get_dependents (An expression which gets the topics to update after answering a question):
    expression("""
        [target] + flatten(map(eval(get_dependents,["target":t,"correct":correct]),t,topics[target][if(correct,"depends_on","leads_to")]))
    """)

correct (Did the student get the current question right?):
    current_question["credit"]=1

after_answering (Update the state after the student answers a question):
    let(
        ntopics, eval(get_dependents,["target":current_topic,"correct":correct])
    ,   nstate, state + ['topics': map(
                    if(tstate["topic"]["name"] in ntopics, tstate + ["status":if(correct,"passed","failed")], tstate),
                    tstate,
                    state["topics"]
                )]
    , nstate
    )

action_retry (Use up one retry and visit the same topic again):
    [
        "label": translate("diagnostic.use retry"),
        "state": state + ["retries": state["retries"]-1],
        "next_question": random(topics[current_topic]["questions"])
    ]

action_stop (Stop the exam):
    [
        "label": translate("diagnostic.end test"),
        "state": state,
        "next_question": nothing
    ]

action_move_on (Move to the next topic, or end the exam if there are no more):
    let(
        state, after_answering,
        immediate_next_topics, topics[current_topic][if(correct, "leads_to", "depends_on")],
        unknown_topics, map(x["topic"],x,filter(x["status"]="unknown",x,state["topics"])),
        unknown_immediate_topics, filter(x["name"] in immediate_next_topics,x,unknown_topics),
        next_topics, if(len(unknown_immediate_topics), unknown_immediate_topics, unknown_topics),
        finished, len(next_topics)=0 or state["finished"],
        topic,
            if(not finished,
                next_topics[floor(len(next_topics)/2)]["name"]
            ,
                nothing
            ),
        [
            "label": translate("diagnostic.move to next topic"),
            "state": after_answering,
            "next_question": if(not finished, random(topics[topic]["questions"]), nothing)
        ]
    )

can_move_on:
    action_move_on["next_question"]<>nothing

next_actions (Actions to offer to the student when they ask to move on):
    let(
        feedback, retries_feedback+"\n\n"+translate("diagnostic.next step question")
    ,   [
            "feedback": feedback,
            "actions": if(not correct and state["retries"]>0, [action_retry], []) + if(can_move_on,[action_move_on],[action_stop])
        ]
    )

after_exam_ended (Update the state after the exam ends):
    let(
        state, after_answering,
        ntopics, map(t+["status": if(t["status"]="unknown","failed",t["status"])],t,state["topics"]),
        state+["finished": true]
    )

finished (Is the test finished? True if there are no unknown topics):
    len(unknown_topics)=0 or state["finished"]

total_progress:
    let(
        num_topics, len(state["topics"]),
        known, filter(tstate["status"]<>"unknown",tstate,state["topics"]),
        passed, filter(tstate["status"]="passed",tstate,known),
        num_known, len(known),
        num_passed, len(passed),
        [
            "name": translate("control.total"),
            "progress": if(num_topics>0,num_known/num_topics,0), 
            "credit": if(num_known>0,num_passed/num_topics,0)
        ]
    )

learning_objective_progress:
    map(
        let(
            tstates, map(state["topics"][j],j,topics_by_objective[lo["name"]]),
            known, filter(tstate["status"]<>"unknown",tstate,tstates),
            passed, filter(tstate["status"]="passed",tstate,known),
            num_topics, len(tstates),
            num_known, len(known),
            num_passed, len(passed),
            ["name": lo["name"], "progress": if(num_topics>0,num_known/num_topics,0), "credit": if(finished,num_passed/num_topics,if(num_known>0,num_passed/num_known,0))]
        ),
        lo,
        learning_objectives
    )

progress (Progress on each of the learning objectives, plus total progress):
    learning_objective_progress+
    total_progress

retries_feedback:
    translate("diagnostic.now assessing topic", ["current_topic": current_topic]) + " " +
    let(
        retries, state["retries"], 
        pluralise(retries, translate("diagnostic.one retry left"), translate("diagnostic.retries left", ["retries": retries ]))
    )
    + " " +
    let(
        p,total_progress["progress"],
        percentage, dpformat(100p, 0),
        translate("diagnostic.percentage completed", ["percentage": percentage])
    )

weak_objective_threshold (The amount of credit below which a learning objective is considered weak):
    0.6

finished_feedback:
    let(
        weak_objectives, filter(p["credit"]<weak_objective_threshold, p, learning_objective_progress),
    //
        translate("diagnostic.test is over") + " "
        +
        if(len(weak_objectives)=0,
            translate("diagnostic.passed all lo")
        ,
            translate("diagnostic.more work on lo", ["los": 
                join(map(lo["name"], lo, weak_objectives),", ")])
        )
    )

feedback:
    if(finished,
        finished_feedback,
        retries_feedback
    )

Custom diagnostic algorithm

Interface theme

Upload a new theme

Interface language

Allow students to print question content?

Show student's name?

Enter review mode immediately on ending the exam?

	Always	In submitted mode	Only in review mode	Never
Show answer correctness
Show the student's score
Show the maximum score
Show part feedback messages
Show expected answers
Show question advice

Pass threshold

Feedback messages

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Taxonomy: Context

Contributors

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History

Comment

Checkpoint description

Christian Lawson-Perfect 1 day, 17 hours ago

Aleksandra Romanowska 3 years, 10 months ago

Newcastle University Mathematics and Statistics 9 years, 3 months ago

Question groups

Topics

Learning objectives

Base diagnostic algorithm

Custom diagnostic algorithm

On leaving a question

On timeout

5 minutes before timeout