Aask new-coding sheet.xlsx
study characteristic
APA Citation Grade Gender Ethnicity Other demographic info Number of Participants (for each group) Research design (group based or single case based?) For group based, random assignment or non randome; for single case, which design? Features of the target Intervention/treatment Duration of Intervention (How long was the intervention implemented over time? e.g., 6 months, 5 weeks, etc.) Number of Trials (How many times did the students receive the intervention per week? How long was each instructional period? (For how many minutes was each lesson?) Instructional Group Size (small group, individual, whole group?) Who is implementing the intervention (e.g., teacher, researcher, graduate student, paraprofessional)? How was the individual implementing the intervention trained? Fidelity information (Y/N)
My Study Characteristic
Authors (APA) Number of Participants Gender Diagnosis Setting School Level Grade Type of Visual Support Type of Technology used Targeted Skills Research Design Type of Instructions Purpose Column3 Column4 Column5 Column6 Column7 Column8 Column9 Column10 Column11 Column12 Column13 Column14 Column15 Column16 Column17 Column18 Column19 Column20 Column21 Column22 Column23 Column24 Column25 Column26 Column27 Column28 Column29 Column30 Column31 Column32 Column33 Column34 Column35 Column36 Column37 Column38 Column39 Column40 Column41 Column42 Column43 Column44 Column45 Column46 Column47 Column48 Column49 Column50 Column51 Column52 Column53 Column54 Column55 Column56 Column57 Column58 Column59 Column60 Column61 Column62 Column63 Column64 Column65 Column66 Column67 Column68 Column69 Column70 Column71 Column72 Column73 Column74 Column75 Column76 Column77 Column78 Column79 Column80 Column81 Column82 Column83 Column84 Column85 Column86 Column87 Column88 Column89 Column90 Column91 Column92 Column93 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Column16310 Column16311 Column16312 Column16313 Column16314 Column16315 Column16316 Column16317 Column16318 Column16319 Column16320 Column16321 Column16322 Column16323 Column16324 Column16325 Column16326 Column16327 Column16328 Column16329 Column16330 Column16331 Column16332 Column16333 Column16334 Column16335 Column16336 Column16337 Column16338 Column16339 Column16340 Column16341 Column16342 Column16343 Column16344 Column16345 Column16346 Column16347 Column16348 Column16349 Column16350 Column16351 Column16352 Column16353 Column16354 Column16355 Column16356 Column16357 Column16358 Column16359 Column16360 Column16361 Column16362 Column16363 Column16364 Column16365 Column16366 Column16367 Column16368 Column16369 Column16370 Column16371 Column16372 Column16373
Duttlinger,M.Ayres,Bevill-Davis,H. Douglas(2012) 4 2M & 2F ASD/ID self- contained classroom Middle School 6,7&8 PAC book (picture symbols & 15.2 cm × 30 cm card stock served as a base for the pictures) Low-Tech School Routine Skills A-B-A-B withdrawal design (SCD) ve
al directions Picture Activity Schedule to complete a sequence of tasks inside school environment
C. Bryan,L. Gast(2000) 4 2M & 2F ASD students’ resource classroom Elementry School 1&2 PAC book (4×6" photo album & 2×2" picture card.) Low-Tech increase on-task and on-schedule behavior A-B-A-B withdrawal design (SCD) supervision and ve
al prompts Teaching On-Task and On-Schedule Behaviors to high functioning children with autism via picture activity schedule
D. Spriggs, L. Gast, and M. Ayres(2007) 4 F ID self- contained classroom Middle School 6&8 PAC book (10 cm x 15 cm photo album & 5 cm x 5 cm Board Maker pictures) Low-Tech increase on-task and on-schedule behavior A-B-A-B withdrawal design (SCD) Graduatedguidance, system of least prompts, and ve
al prompting Using Picture Activity Schedule Books to Increase on schedule behaviour and on task behaviou
Shepley, S. B., Spriggs, A. D., Samudre, M. D., & Sartini, E. C XXXXXXXXXXInitiation and Generalization of Self-Instructed Video Activity Schedules for Elementary Students With Intellectual Disability. The Journal of Special Education, 53(1), 51–62. doi: XXXXXXXXXX/ XXXXXXXXXX 4 2M & 2F ASD/ID students’ resource classroom Elementry School N/A iPad (My Pictures Talk application) High-tech generalization of SI behaviors to new environments single-case multiple probe across participants design self-instruction acquiring the self-instruction behavior in the target environments via video Activity schedules
Douglas, K. H., & Uphold, N. M XXXXXXXXXXIpad® or iPod touch®: Evaluating self-created electronic photographic activity schedules and student preferences. Journal of Special Education Technology, 29(3), 1–14. 5 2M & 3F ID outside and inside the classroom high- school 9, 10 & 11 iPad® or iPod touch (First Then Visual Schedule application) High-tech teaching students self-management skills A-B-A-B withdrawal design (SCD) controlling, gesture and direct ve
al prompt. evaluating the effectiveness of a self-created electronic photographic activity schedule
Spriggs, A. D., Knight, V., & She
ow, L XXXXXXXXXXTalking Picture Schedules: Embedding Video Models into Visual Activity Schedules to Increase Independence for Students with ASD. Journal of Autism and Developmental Disorders, 45(12), 3846–3861. doi: XXXXXXXXXX/s XXXXXXXXXX 4 3M & 1F ASD special education resource room & general classrooms. high- school N/A iPad (My Pictures Talk application) High-tech independently transition within and between tasks multiple probe across participants design system of least prompts Embedding Video Models into Visual Activity Schedules to Increase Independence for Students with ASD
coding book
Moderator Detail Type Categorized Coding
Moderator_1 Session total hours (STH) Categorical 1 throu 5 1
6 though 10 2
11 through 15 3
Moderator_2 Grade Categorical elementary 1
middle 2
high 3
postsecondary 4
Moderator_3 Fidelity Categorical Yes 1
No 2
Moderator_4 Meeting CEC standards Categorical Yes 1
No 2
My Project
Moderator_4 Kind of VS Categorical high-tech 1
low-tech 2
coding by outcomes
Author (year) Target outcomes Total_N Group1_n Group1_M Group1_SD Group1_SE Group1_t Group1_F Group1_r Group1_coefficient Group2_n Group2_M Group2_SD Group2_SE Group2_t Group2_F Group2_r Group2_coefficient
ABC (2013) PA
ABC (2013) Voca
ABC (2013) Fluency
DEF (2013) PA
DEF (2013) Voca
DEF (2013) Fluency
GHI (2013) PA
GHI (2013) Voca
GHI (2013) Fluency
coding by moderators
Author (year) STH Grade Fidelity CEC
ABC (2013) 1 1 1 1
DEF (2013) 2 2 2 1
GHI (2013) 3 3 1 1
2=Duttlinger, Med et al. 2012
Participent 1 Participent 2 Participent 3 Participent 4
X Y Phase X Y Phase X Y Phase X Y Phase
1 0 G-pre 1 0 G-pre 1 0 G-pre 1 0 G-pre
2 0 2 0 2 0 2 30
3 0 3 0 3 30 3 0
4 40 B 4 40 B 4 60 B 4 43 B
5 60 5 20 5 40 5 25
6 60 6 60 6 60 6 10
7 60 7 40 7 80 instruction 7 70 instruction
8 60 instruction 8 40 instruction 8 100 8 73
9 80 9 20 9 100 9 85
10 100 10 80 10 100 10 85
11 100 11 80 11 100 I 11 100 I
12 100 I 12 80 I 12 100 12 100
13 100 13 100 13 100 13 100
14 100 14 100 14 60 B 14 100
15 100 15 100 15 60 15 42 B
16 100 16 60 B 16 40 16 40
17 60 B 17 40 17 100 I 17 10
18 60 18 40 18 100 18 65
19 40 19 100 I 19 100 19 100 I
20 100 I 20 100 20 100 20 100
21 100 21 100 21 100 G-post 21 100
22 100 22 100 22 100 22 100 G-post
23 100 G-post 23 100 G-post 23 100 23 100
24 100 24 100 24 24 100
2=Linley Bryan& David Gast 2000
Participent 1 Participent 2 Participent 3 Participent 4
X Y Phase X Y Phase X Y Phase X Y Phase
1 20 G-pre 1 20 G-pre 1 13 G-pre 1 0 G-pre
2 25 2 12 2 13 2 13
3 20 3 12 3 13 3 0
4 20 4 12 4 12 4 0
5 20 B 5 15 B 5 13 B 5 0 B
6 12 6 0 6 0 6 0
7 25 7 15 7 5 7 13
8 25 8 15 8 5 8 0
9 65 Guide 9 45 Guide 9 53 Guide 9 43 Guide
10 80 10 100 10 71 10 72
11 100 11 93 11 92 11 100
12 100 12 100 12 92 12 95
13 100 13 100 13 92 13 95
14 100 14 100 14 100 14 95
15 100 15 100 15 100 15 95
16 100 I 16 100 I 16 100 I 16 100 I
17 100 17 100 17 100 17 100
18 100 18 100 18 100 18 100
19 40 B 19 15 B 19 0 B 19 25 B
20 5 20 20 20 5 20 25
21 52 21 25 21 7 21 25
22 0 22 15 22 0 22 13
23 100 I 23 100 I 23 100 I 23 100 I
24 100 24 100 24 100 24 100
25 95 25 100 25 100 25 100
26 100 26 100 26 100 26 100
27 100 27 100 27 100 27 100
28 100 G-post 28 G-post 28 100 G-post 28 100 G-post
29 100 29 100 29 100 29 100
30 100 30 100 30 100 30 100
31 `100 31 100 31 100 31 100
32 32 32 32
33 33 33 33
34 34 34 34
35 35 35 35
2=Spriggs, Gast & Ayres 2007
Participent 1 Participent 2 Participent 3 Participent 4
X Y Phase X Y Phase X Y Phase X Y Phase
1 0 G-pre 1 0 G-pre 1 0 G-pre 1 15 G-pre
2 5 2 15 2 5 2 5
3 25 3 5 3 5 3 20
4 5 4 5 4 0 4 15
5 0 B 5 0 B 5 0 B 5 0 B
6 0 6 15 6 0 6 5
7 0 7 30 7 0 7 10
8 5 8 5 8 0 8 0
9 40 instruction 9 85 instruction 9 65 Instruction 9 40 Instruction
10 65 10 95 10 80 10 75
11 90 11 100 11 80 11 80
12 90 12 100 12 100 12 100
13 95 13 100 13 75 13 100
14 100 14 100 I 14 85 14 85
15 95 15 100 15 100 15 95
16 90 I 16 95 16 85 16 90
17 100 17 100 17 95 17 100
18 95 18 B 18 100 18 95
19 100 19 0 19 90 19 100 I
20 0 B 20 15 20 100 20 90
21 5 21 20 21 100 I 21 100
22 0 22 0 22 90 22 95
23 0 23 100 I 23 95 23 95
24 95 I 24 24 100 24
25 100 25 100 25 0 B 25 0 B
26 100 26 100 26 5 26 0
27 100 27 100 27 0 27 0
28 90 G-post 28 95 G-post 28 0 28 0
29 100 29 100 29 85 I 29 80 I
30 95 30 100 30 95 30 90
31 100 31 100 31 90 31 100
32 100 32 95
33 100 33 100
34 95 G-post 34 90 G-post
35 90 35 95
36 100 36 95
37 95 37 100
1=Shepley et al., 2019
Participent 1 Participent 2 Participent 3 Participent 4
X Y Phase X Y Phase X Y Phase X Y Phase
1 0 B 1 0 B 1 0 B 1 0 B
2 0 2 0 2 0 2 0
3 0 3 0 3 0 3 0
4 0 4 0 4 0 4 0
5 0 5 0 5 0 5 0
6 0 I 6 6 6
7 10 7 7 7
8 20 8 8 8
9 50 9 9 9
10 50 10 10 10
11 75 11 11 11
12 90 12 12 12
13 70 13 13 13
14 70 14 14 14
15 80 15 15 15
16 70 16 16 16
17 90 17 17 17
18 100 18 18 18
19 100 19 19 19
20 90 20 20 20
21 90 21 21 21
22 80 22 22 22
23 90 23 23 23
24 90 24 24 24
25 100 25 0 25 0 25 0
26 26 0 26 26
27 27 0 I 27 27
28 28 0 28 28
29 100 29 30 29 29
30 30 50 30 30
31 31 70 31 31
32 32 80 32 32
33 33 90 33 33
34 100 34 100 34 34
35 35 100 35 35
36 36 80 36 36
37 37 90 37 37
38 38 100 38 38
39 80 39 100 39 39
40 100 40 40
41 100 41 0 41 0
42 42 0 42 0
43 43 0 43 0
45 45 30 I 45
46 100 46 60 46
47 47 65 47
48 48 90 48
49 49 100 49
50 50 100 50 0
51 100 51 100 51 0
52 52 52 0
53 53 53 30 I
54 54 54 70
55 55 55 100
56 100 56 30 56 80
57 57 57 80
58 58 58 90
59 59 59 70
60 60 60 70
61 100 61 40 61 80
62 62 62 100
63 63 63 100
64 64 64
65 65 65
66 100 66 40 66
67 67
68 68 30
69 69
70 70
71 30 71
72
73 30
74
75
76
77
78 37
1=Douglas & Uphold 2014
Participent 1 Participent 2 Participent 3 Participent 4 Participent 5
X Y Phase X Y Phase X Y Phase X Y Phase X Y Phase
1 50 Pre-G 1 50 Pre-G 1 0 Pre-G 1 0 Pre-G 1 50 Pre-G
2 0 2 25 2 0 2 25 2 50
3 0 3 60 3 25 3 25 3 40
4 20 B 4 40 B 4 25 B 4 50 B 4 20 B
5 40 5 20 5 25 5 50 5 0
6 20 6 40 6 25 6 50 6 25
7 100 I 7 100 I 7 100 I 7 100 I 7 100 I
8 100 8 100 8 100 8 100 8 100
9 100 9 100 9 100 9 100 9 100
10 100 10 100 10 100 10 100 10 100
11 100 11 100 11 100 11 100 11 100
12 100 12 100 12 100 12 100 12 100
13 100 13 100 13 100 13 100 13 100
14 100 14 100 14 50 B 14 100 14 100
15 50 B 15 100 15 50 15 100 15 100
16 25 16 25 B 16 50 16 100 16 25 B
17 40 17 75 17 100 I 17 100 17 40
18 100 I 18 40 18 100 18 100 18 20
19 100 19 100 I 19 100 19 75 B 19 100 I
20 100 20 100 20 100 Post-G 20 50 20 100
21 100 Post-G 21 100 21 100 21 50 21 100
22 100 22 100 Post-G 22 100 22 100 I 22 100 Post-G
23 100 23 100 23 100 2nd device 23 100 23 100
24 100 2nd device 24 100 24 100 24 100 24 100
25 100 25 100 2nd device 25 100 25 100 Post-G 25 100 2nd device
26 100 26 100 26 100 26 100
27 100 27 100 27 100
28 100 28 100 2nd device 28 100
29 100
30 100
1= Spriggs et al., 2015
Participent 1 Participent 2 Participent 3 Participent 4
X Y Phase X Y Phase X Y Phase X Y Phase
1 0 Pre-G 1 0 Pre-G 1 0 Pre-G 1 0 Pre-G
2 0 2 0 2 0 2 0
3 0 3 0 3 0 3 0
4 0 B 4 0 B 4 0 B 4 0 B
5 0 5 0 5 0 5 0
6 0 6 0 6 0 6 0
7 90 Training 7 7 7
8 75 8 8 8
9 100 9 9 9
10 100 10 10 10
11 100 11 11 11
12 20 I 12 0 12 0 12 0
13 25 13 0 13 0 13 0
14 43 14 0 14 0 14 0
15 70 15 70 Training 15 15
16 40 16 80 16 16
17 65 17 100 17 17
18 67 18 100 18 18
19 63 19 100 19 19
20 67 20 30 I 20 0 20 0
21 60 21 50 21 0 21 0
22 67 22 55 22 0 22 0
23 68 23 50 23 25 Training 23
24 65 24 50 24 80 24
25 85 25 70 25 90 25
26 70 26 50 26 90 26
27 60 27 70 27 95 27
28 73 28 65 28 95 28
29 70 29 70 29 100 29
30 75 30 100 30 100 30
31 74 chunk 31 100 31 100 31
32 70 32 100 32 25 I 32 0
33 75 33 100 33 25 33 0
34 85 34 100 34 25 34 0
35 80 35 100 35 27 35 43 T
36 68 36 100 36 30 36 95
37 80 37 100 37 25 37 100
38 90 38 95 G- static 38 30 38 100
39 85 39 100 39 75 chunk 39 100
40 76 40 95 40 80 40 55 I
41 90 41 80 G- skill 41 90 41 65
42 85 42 80 42 95 42 93
43 75 G-static 43 93 43 95
44 80 44 90 44 95
45 80 45 95 45 95
46 83 G-skill 46 95 46 100
47 80 47 100 47 100
48 83 48 100 48 100
49 100 49 95
50 100 G- static 50 100
51 100 51 95 G- static
52 100 52 95
53 85 Post-G 53 95
54 90 54 80 G- skill
55 95 55 80
56 80
2 Zimmerman, Ledford & Barton
Participent 1 Participent 2 Participent 3
X Y Phase X Y Phase X Y Phase
1 0 B 1 10 B 1 0 B
2 0 2 0 2 0
3 0 3 0 3 0
4 0 4 0 4 0
5 95 VAS 5 80 5 0
6 100 6 0 6 0
7 90 7 0 7 0
8 100 8 0 8 100 VAS
9 100 9 0 9 100
10 100 10 95 VAS 10 100
11 0 B 11 90 11 100
12 0 12 90 12 95
13 0 13 95 13 100
14 0 14 15 B 14 60 B
15 0 15 0 15 0
16 0 16 0 16 40
17 100 VAS 17 0 17 0
18 100 18 90 VAS 18 0
19 100 19 95 19 100 VAS
20 100 20 95 20 100
21 100 21 100 21 100
22 100 22 95 22 100
23 98 23 95
24 100
1 Linda C. Mechlin
Participent 1 Participent 2 Wanda Participent 3 Andy
X Y Phase X Y Phase X Y Phase
1 15 B 1 10 B 1 30 B
2 15 2 15 2 30
3 15 3 10 3 30
4 4 I 4
5 I 5 5 70 I
6 6 6 90
7 7 7 100
8 8 8 95
9 9 9 100
10 10 B 10 100
11 B 11 15 11 85 B
12 30 12 65 I 12
13 I 13 80 13 I
14 14 90 14
15 15 90 15 100
16 16 90 16
17 40 B 17 100 17
18 90 I 18 100 18 B
19 100 19 100 19 I
20 100 20 90 B 20
21 90 21 I 21
22 100 22 22 95
23 85 B 23 100 23
24 i 24 24 B
25 90 25 B 25 95 I
26 26 90 I 26
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Handout-what to code how to code.docx
Meta analysis practice: How and What to code
1. How to Code
· Once you have collected your sample of studies you need to code their characteristics and calculate effect sizes.
· The steps of a good coding procedure are
1. Decide which characteristics you want to code.
2. Decide exactly how you will measure each characteristic. If you decide to use a continuous scale, specify the units. If you decide to use categories, specify what groups you will use.
note: In theory, a meta-analysis model can contain both continuous and categorical moderator variables. In theory, a model can contain an unlimited number of independent variables – statistical control for IVs.
For example, for the continuous scale (i.e., you want to see the role of # of sessions in intervention effect), you can either code the # as it is or based on ranges (1 for 1-10 sessions, 2 for 11-20 sessions). For the categorial scale (i.e., you want to see the difference in intervention effect regarding the grade level or disability category), you can code, grade 1 as 1, grade2 as 2. Or, lower graders as 1, and upper graders as 2. Similarly, you can code learning disability as 1 and deaf student as 2, or high incidence disabilities as 1 or low incidence disabilities as 2.
Again, all these will depend on your research questions (for example, would you like to see the effect of sessions, effect of disability category, or effect of grade, etc.) and your moderators will be coded based on your research questions.
3. Write down the specifics of your coding scheme in a code book. The code book should contain explicit instructions on how to code each characteristic, including specific examples where necessary.
4. Pilot the coding scheme and train the coders. You should probably code 2-4 studies between training sessions.
5. Once you have a stable coding scheme you code the studies. The coders should work independently, with only occasional meetings to co
ect ambiguities in the scheme.
6. Calculate the reliability of the coding for each item in your scheme. You should not include the studies you used for training in your calculation of reliability.
7. You should always have a second coder when performing a meta-analysis. Not only does this let you report a reliability on your coding of moderators, but it also provides a check on your effect size calculations.
· Sometimes the information you need will not be reported in a study. You should therefore have a separate code to indicate that the information for a particular question was unavailable. You can try contacting the authors for the information, but this often fails to gain you anything.
· Coding differences are often caused by ambiguities in the coding scheme. You should therefore concentrate on developing clear and detailed coding rules when piloting your scheme.
· Reliability is a measure of the consistency of your coding scheme. If your coding has low reliability, then the specific scheme you are using is adding a lot of variability to your measurements. It is actually a specific mathematical concept, namely variability of idealized "true" scores (numerator) / variability of measured scores (denominator)
· Since the variability of measured scores = (variability of true scores) + (measurement e
or), reliabilities will always be between 0 and 1. When reporting the reliability of your coding, you should use a statistic that conforms to this definition. Some examples are (for continuous variables) the intraclass co
elation, Chronbach's alpha, and (for categorical variables) Cohen's kappa.
2. What to Code
· The first set of characteristics you need to code are study identi¯ers, including
Study ID. You should assign a unique number to every study included in your analysis. You
should write this number on the photocopy of the study, as well as any coding or calculation
sheets.
Long and short references. You should record the full (APA style) reference, as well as a short citation to use when refe
ing to the study in your codes.
· You should code all moderating variables you wish to examine. The next section provides a detailed discussion of the different types of moderators you might wish to consider.
· You should code characteristics of study quality. You can then use these either as moderating variables or as bases for exclusion. One good way to code quality is to read through a list of validity threats (such as from Cook & Campbell, 1979) and consider whether each might have influenced studies in your analysis.
· You need to record information about the overall design of the study such as
Assignment of subjects. You should record whether subjects were assigned to conditions randomly or in some other fashion.
Experimental design. This should be an exact specification of the study design, specifying which factors are crossed and nested. You should specify all aspects of the design, not just those relevant to your analysis. Example: Time X S(Gender X Study method)
Manipulation codes. If important moderators are sometimes manipulated between subjects and sometimes within subjects you should code this as a moderating variable