What is the effect of increasing order of IIR filter? does it also effects stability? If we have an IIR filter of order 6, now we change its order to 7, will there be any change in its stability?
Stability depends on the pole locations, so one cannot generally say that increasing the filter order does or doesn't affect stability. It depends on how you increase the filter order. What are the additional coefficients of the denominator? (Because the denominator polynomial of the transfer function determines the pole locations).
Another question are the finite word length effects. If you quantize the filter coefficients, e.g., by using fixed point arithmetic (but of course also by using floating point), then higher filter orders tend to be more problematic than lower filter orders, because the chance that poles get very close to (or even outside) the unit circle through quantization becomes larger. But again, you cannot say anything in general unless you specify the filter's transfer function, the filter structure, and the type of quantization.
For direct implementation (without cascading), 6th order is already too high.
Let's look at the 6th order low-pass Butterworth filter with sample rate 24000 Hz and cut-off requency 110 Hz:
b0: 8.43345791e-12 a0: 1 b1: 5.06007475e-11 a1: -5.88873409 b2: 1.26501869e-10 a2: 14.4498436 b3: 1.68669158e-10 a3: -18.91181708 b4: 1.26501869e-10 a4: 13.92373555 b5: 5.06007475e-11 a5: -5.46772375 b6: 8.43345791e-12 a6: 0.89469577
The numerator (b) and denominator (a) coefficients have too much difference in order of magnitude. If this filter is implemented directly with single precision floating point, the rounding errors make it unstable:
If the same filter is implemented as 3 cascaded second order sections, b and a have much less difference in orders of magnitude:
Stage 0 b0: 0.00020171 a0: 1 b1: 0.00040341 a1: -1.94507277 b2: 0.00020171 a2: 0.9458796 Stage 1 b0: 0.00020318 a0: 1 b1: 0.00040636 a1: -1.95927903 b2: 0.00020318 a2: 0.96009175 Stage 2 b0: 0.00020578 a0: 1 b1: 0.00041156 a1: -1.98438228 b2: 0.00020578 a2: 0.98520541
In this case, the cascaded implementation is stable even with single precision floating point: