Predicting Computational Thinking in Elementary Science Lessons Using a Multilevel Model Approach

Table 2

HLM models for algorithms.

	Algorithms
Fixed effect	Model 1			Model 2			Model 3			Model 4
	Coeff.	S.E.	t-Ratio	Coeff.	S.E.	t-Ratio	Coeff.	S.E.	t-Ratio	Coeff.	S.E.	t-Ratio
Intercept (γ00)	75.76	7.59	9.98	75.68	7.49	10.10	69.11	7.75	8.91	65.09	8.04	8.10
RPP curriculum (γ01)							24.16	14.10	1.713	32.96	14.05	2.34
Poverty (γ02)							−1.07	0.65	−1.63
Teaching experience (γ10)				5.87	3.98	1.47	5.87	3.98	1.48	5.87	3.97	1.48
Grade level (γ20)				10.97	3.23	3.40	10.97	3.22	3.40	10.97	3.22	3.41
Confidence (γ30)				17.16	7.97	2.15	17.16	7.96	2.16	17.16	7.95	2.16
Random effect	Variance			Variance			Variance			Variance
Level 1 (r)	3,895.40			3,423.94			3,414.22			3,404.40
Level 2 (u0)	390.05			417.19			161.93			248.67
Variance	Partitioned			Explained			Explained			Explained
Level 1	90.90%			12.10%			12.35%			12.60%
Level 2	9.10%						61.19%			40.39%
Deviance (df)	1,635.21 (2)			1,598.09 (2)			1,582.61 (2)			1,587.87 (2)

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