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Evidence-based assessment/Instruments/Mood and Feelings Questionnaire

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The Mood and Feelings Questionnaire (MFQ) is a suite of six versions of questionnaire meant to measure depression in children and adolescents ages 6-18 (self or parent report) as well as a version for adults to report about their own mood. The questionnaire was created by researchers at Duke University as part of the Great Smokey Mountain epidemiological project in Western North Carolina. The MFQ can be used both as an assessment measure to detect possible cases and as a follow-up assessment measure. It takes 5-10 minutes to administer and is used by clinicians in community samples of ages 6-18. The short forms have 13 items, and the full length have 33 items, which are scored 0 to 2 points each. Higher scores indicate more depression.

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Psychometrics

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Steps for evaluating reliability and validity

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Click here for instructions
  1. Evaluate the instrument by referring to the rubrics for evaluating reliability and validity (both external Wikiversity pages). For easy reference, open these pages in separate tabs.
    1. Reliability rubric
    2. Validity rubric
  2. Refer to the relevant instrument rubric table. This is the table that you will be editing. Do not confuse this with the external pages on reliability and validity.
    1. Instrument rubric table: Reliability
    2. Instrument rubric table: Validity
  3. Depending on whether instrument was adequate, good, excellent, or too good:
    1. Insert your rating.
    2. Add the evidence from journal articles that support your evaluation.
    3. Provide citations.
  4. Refer to the heading for the instrument rubric table ("Rubric for evaluating norms and reliability for the XXX ... indicates new construct or category")
    1. Make sure that you change the name of the instrument accordingly.
  5. Using the Edit Source function, remove collapse top and collapse bottom curly wurlys to show content.

Instrument rubric table: Reliability

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Note: Not all of the different types of reliability apply to the way that questionnaires are typically used. Internal consistency (whether all of the items measure the same construct) is not usually reported in studies of questionnaires; nor is inter-rater reliability (which would measure how similar peoples' responses were if the interviews were repeated again, or different raters listened to the same interview). Therefore, make adjustments as needed.

Click here for instrument reliability table

Reliability

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Not all of the different types of reliability apply to the way that questionnaires are typically used. Internal consistency (whether all of the items measure the same construct) is not usually reported in studies of questionnaires; nor is inter-rater reliability (which would measure how similar peoples' responses were if the interviews were repeated again, or different raters listened to the same interview). Therefore, make adjustments as needed.

Reliability refers to whether the scores are reproducible. Unless otherwise specified, the reliability scores and values come from studies done with a United States population sample. Here is the rubric for evaluating the reliability of scores on a measure for the purpose of evidence based assessment.

Evaluation for norms and reliability for the XXX (table from Youngstrom et al., extending Hunsley & Mash, 2008; *indicates new construct or category)
Criterion Rating (adequate, good, excellent, too good*) Explanation with references
Norms Adequate Multiple convenience samples and research studies, including both clinical and nonclinical samples[citation needed]
Internal consistency (Cronbach’s alpha, split half, etc.) Excellent; too good for some contexts Alphas routinely over .94 for both scales, suggesting that scales could be shortened for many uses[citation needed]
Interrater reliability Not applicable Designed originally as a self-report scale; parent and youth report correlate about the same as cross-informant scores correlate in general[1]
Test-retest reliability (stability Good r = .73 over 15 weeks. Evaluated in initial studies,[2] with data also show high stability in clinical trials[citation needed]
Repeatability Not published No published studies formally checking repeatability

Instrument rubric table: Validity

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Click here for instrument validity table

Validity

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Validity describes the evidence that an assessment tool measures what it was supposed to measure. There are many different ways of checking validity. For screening measures, diagnostic accuracy and w:discriminative validity are probably the most useful ways of looking at validity. Unless otherwise specified, the validity scores and values come from studies done with a United States population sample. Here is a rubric for describing validity of test scores in the context of evidence-based assessment.

Evaluation of validity and utility for the XXX (table from Youngstrom et al., unpublished, extended from Hunsley & Mash, 2008; *indicates new construct or category)
Criterion Rating (adequate, good, excellent, too good*) Explanation with references
Content validity Excellent Covers both DSM diagnostic symptoms and a range of associated features[2]
Contruct validity (e.g., predictive, concurrent, convergent, and discriminant validity) Excellent Shows Convergent validity with other symptom scales, longitudinal prediction of development of mood disorders,[3][4][5] criterion validity via metabolic markers[2][6] and associations with family history of mood disorder.[7] Factor structure complicated;[2][8] the inclusion of “biphasic” or “mixed” mood items creates a lot of cross-loading
Discriminative validity Excellent Multiple studies show that GBI scores discriminate cases with unipolar and bipolar mood disorders from other clinical disorders[2][9][10] effect sizes are among the largest of existing scales[11]
Validity generalization Good Used both as self-report and caregiver report; used in college student[8][12] as well as outpatient[9][13][14] and inpatient clinical samples; translated into multiple languages with good reliability
Treatment sensitivity Good Multiple studies show sensitivity to treatment effects comparable to using interviews by trained raters, including placebo-controlled, masked assignment trials[15][16] Short forms appear to retain sensitivity to treatment effects while substantially reducing burden[16][17]
Clinical utility Good Free (public domain), strong psychometrics, extensive research base. Biggest concerns are length and reading level. Short forms have less research, but are appealing based on reduced burden and promising data

Development and history

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Click here for instructions for development and history
  • Why was this instrument developed? Why was there a need to do so? What need did it meet?
  • What was the theoretical background behind this assessment? (e.g. addresses importance of 'negative cognitions', such as intrusions, inaccurate, sustained thoughts)
  • How was the scale developed? What was the theoretical background behind it?
  • If there were previous versions, when were they published?
  • Discuss the theoretical ideas behind the changes.

Impact

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  • What was the impact of this assessment? How did it affect assessment in psychiatry, psychology and health care professionals?
  • What can the assessment be used for in clinical settings? Can it be used to measure symptoms longitudinally? Developmentally?

Use in other populations

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  • How widely has it been used? Has it been translated into different languages? Which languages?

Scoring instructions and syntax

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We have syntax in three major languages: R, SPSS, and SAS. All variable names are the same across all three, and all match the CSV shell that we provide as well as the Qualtrics export.

Hand scoring and general instructions

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Click here for hand scoring and general administration instructions
Each item is rated on a Likert scale of 0-2, in which 0 = NOT TRUE, 1 = SOMETIMES, 2 = TRUE.

There are no prescribed cut points for MFQ.

If there are any hand scoring and general administration instructions, it should go here.

CSV shell for sharing

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Click here for CSV shell
  • <Paste link to CSV shell here>

Here is a shell data file that you could use in your own research. The variable names in the shell corresponds with the scoring code in the code for all three statistical programs.

Note that our CSV includes several demographic variables, which follow current conventions in most developmental and clinical psychology journals. You may want to modify them, depending on where you are working. Also pay attention to the possibility of "deductive identification" -- if we ask personal information in enough detail, then it may be possible to figure out the identity of a participant based on a combination of variables.

When different research projects and groups use the same variable names and syntax, it makes it easier to share the data and work together on integrative data analyses or "mega" analyses (which are different and better than meta-analysis in that they are combining the raw data, versus working with summary descriptive statistics).

R/SPSS/SAS syntax

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Click here for R code
# The psych package allows for easy computation and visualization of different psychological assessments and more.
  1. This code was generated in part through the help of ChatGPT

library(psych)

  1. Sample MFQ short form data for multiple individuals- requires manual entry for actual data. 33 columns required for the long-form version.
  2. Each row represents one individual, and each column represents one MFQ item.
  3. If an existing dataframe exists, one can delete this section.
  4. Responses: 0 = Not True, 1 = Sometimes True, 2 = True

mfq_data <- data.frame( 

 item1 = c(1, 0, 2, 1, 1),
 item2 = c(0, 1, 0, 2, 1),
 item3 = c(2, 1, 1, 0, 2),
 item4 = c(0, 2, 1, 1, 0),
 item5 = c(1, 1, 2, 0, 2),
 item6 = c(2, 0, 1, 2, 1),
 item7 = c(0, 1, 2, 0, 1),
 item8 = c(2, 0, 1, 1, 2),
 item9 = c(1, 2, 0, 1, 0),
 item10 = c(0, 2, 1, 1, 1),
 item11 = c(1, 0, 2, 0, 2),
 item12 = c(2, 1, 0, 1, 1),
 item13 = c(1, 2, 1, 0, 2)

)

  1. Scoring the total MFQ score for each individual (row)

mfq_data$mfq_total <- rowSums(mfq_data, na.rm = TRUE)

  1. Descriptive statistics for each item and the total score
  2. trimmed: represents the trimmed mean, eliminating 10% of the most extreme values

mfq_descriptive <- describe(mfq_data) #describe is the necessary command for descriptive statistics print("Descriptive statistics for each item and total score:") print(mfq_descriptive)

  1. Descriptive statistics specifically for the total MFQ score

mfq_total_descriptive <- describe(mfq_data$mfq_total) print("Descriptive statistics for the total MFQ:") print(mfq_total_descriptive)

  1. Cronbach's alpha (internal reliability) for the MFQ items (excluding the total score column)
  2. G6 (smc): Guttman's Lambda 6, a reliability measure
  3. average_r: average item-total correlation
  4. S/N: signal to noise ratio

mfq_reliability <- alpha(mfq_data[, 1:13]) # Items 1 to 13 print("Cronbach's alpha for internal reliability:") print(mfq_reliability)

  1. Optional: Visualization of the scores via histogram

hist( 

 mfq_data$mfq_total, 
 main = "Distribution of Short-Form MFQ Total Scores", 
 xlab = "MFQ Total Score", 
 breaks = 10, 
 col = "lightgreen",  
 border = "black"
)|}
Click here for SPSS code

SPSS code goes here

Click here for SAS code

SAS code goes here

See also

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Here, it would be good to link to any related articles on Wikipedia. For instance:

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Example page

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References

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Click here for references
  1. ↑ Achenbach, TM; McConaughy, SH; Howell, CT (March 1987). "Child/adolescent behavioral and emotional problems: implications of cross-informant correlations for situational specificity.". Psychological Bulletin 101 (2): 213–32. PMID 3562706. 
  2. ↑ 2.0 2.1 2.2 2.3 2.4 Depue, Richard A.; Slater, Judith F.; Wolfstetter-Kausch, Heidi; Klein, Daniel; Goplerud, Eric; Farr, David (1981). "A behavioral paradigm for identifying persons at risk for bipolar depressive disorder: A conceptual framework and five validation studies.". Journal of Abnormal Psychology 90 (5): 381–437. doi:10.1037/0021-843X.90.5.381. 
  3. ↑ Klein, DN; Dickstein, S; Taylor, EB; Harding, K (February 1989). "Identifying chronic affective disorders in outpatients: validation of the General Behavior Inventory.". Journal of consulting and clinical psychology 57 (1): 106–11. PMID 2925959. 
  4. ↑ Mesman, Esther; Nolen, Willem A.; Reichart, Catrien G.; Wals, Marjolein; Hillegers, Manon H.J. (May 2013). "The Dutch Bipolar Offspring Study: 12-Year Follow-Up". American Journal of Psychiatry 170 (5): 542–549. doi:10.1176/appi.ajp.2012.12030401. 
  5. ↑ Reichart, CG; van der Ende, J; Wals, M; Hillegers, MH; Nolen, WA; Ormel, J; Verhulst, FC (December 2005). "The use of the GBI as predictor of bipolar disorder in a population of adolescent offspring of parents with a bipolar disorder.". Journal of affective disorders 89 (1-3): 147–55. PMID 16260043. 
  6. ↑ Depue, RA; Kleiman, RM; Davis, P; Hutchinson, M; Krauss, SP (February 1985). "The behavioral high-risk paradigm and bipolar affective disorder, VIII: Serum free cortisol in nonpatient cyclothymic subjects selected by the General Behavior Inventory.". The American journal of psychiatry 142 (2): 175–81. PMID 3970242. 
  7. ↑ Klein, DN; Depue, RA (August 1984). "Continued impairment in persons at risk for bipolar affective disorder: results of a 19-month follow-up study.". Journal of abnormal psychology 93 (3): 345–7. PMID 6470321. 
  8. ↑ 8.0 8.1 Pendergast, Laura L.; Youngstrom, Eric A.; Brown, Christopher; Jensen, Dane; Abramson, Lyn Y.; Alloy, Lauren B. (2015). "Structural invariance of General Behavior Inventory (GBI) scores in Black and White young adults.". Psychological Assessment 27 (1): 21–30. doi:10.1037/pas0000020. 
  9. ↑ 9.0 9.1 Danielson, CK; Youngstrom, EA; Findling, RL; Calabrese, JR (February 2003). "Discriminative validity of the general behavior inventory using youth report.". Journal of abnormal child psychology 31 (1): 29–39. PMID 12597697. 
  10. ↑ Findling, RL; Youngstrom, EA; Danielson, CK; DelPorto-Bedoya, D; Papish-David, R; Townsend, L; Calabrese, JR (February 2002). "Clinical decision-making using the General Behavior Inventory in juvenile bipolarity.". Bipolar disorders 4 (1): 34–42. PMID 12047493. 
  11. ↑ Youngstrom, Eric A.; Genzlinger, Jacquelynne E.; Egerton, Gregory A.; Van Meter, Anna R. (2015). "Multivariate meta-analysis of the discriminative validity of caregiver, youth, and teacher rating scales for pediatric bipolar disorder: Mother knows best about mania.". Archives of Scientific Psychology 3 (1): 112–137. doi:10.1037/arc0000024. 
  12. ↑ Alloy, LB; Abramson, LY; Hogan, ME; Whitehouse, WG; Rose, DT; Robinson, MS; Kim, RS; Lapkin, JB (August 2000). "The Temple-Wisconsin Cognitive Vulnerability to Depression Project: lifetime history of axis I psychopathology in individuals at high and low cognitive risk for depression.". Journal of abnormal psychology 109 (3): 403–18. PMID 11016110. 
  13. ↑ Klein, Daniel N.; Dickstein, Susan; Taylor, Ellen B.; Harding, Kathryn (1989). "Identifying chronic affective disorders in outpatients: Validation of the General Behavior Inventory.". Journal of Consulting and Clinical Psychology 57 (1): 106–111. doi:10.1037/0022-006X.57.1.106. 
  14. ↑ Youngstrom, EA; Findling, RL; Danielson, CK; Calabrese, JR (June 2001). "Discriminative validity of parent report of hypomanic and depressive symptoms on the General Behavior Inventory.". Psychological assessment 13 (2): 267–76. PMID 11433802. 
  15. ↑ Findling, RL; Youngstrom, EA; McNamara, NK; Stansbrey, RJ; Wynbrandt, JL; Adegbite, C; Rowles, BM; Demeter, CA et al. (January 2012). "Double-blind, randomized, placebo-controlled long-term maintenance study of aripiprazole in children with bipolar disorder.". The Journal of clinical psychiatry 73 (1): 57–63. PMID 22152402. 
  16. ↑ 16.0 16.1 Youngstrom, E; Zhao, J; Mankoski, R; Forbes, RA; Marcus, RM; Carson, W; McQuade, R; Findling, RL (March 2013). "Clinical significance of treatment effects with aripiprazole versus placebo in a study of manic or mixed episodes associated with pediatric bipolar I disorder.". Journal of child and adolescent psychopharmacology 23 (2): 72–9. PMID 23480324. 
  17. ↑ Ong, ML; Youngstrom, EA; Chua, JJ; Halverson, TF; Horwitz, SM; Storfer-Isser, A; Frazier, TW; Fristad, MA et al. (1 July 2016). "Comparing the CASI-4R and the PGBI-10 M for Differentiating Bipolar Spectrum Disorders from Other Outpatient Diagnoses in Youth.". Journal of abnormal child psychology. PMID 27364346. 
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